WO2025192995A1 - Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method - Google Patents

Abstract

A decoding method and device for point cloud data are disclosed. The decoding method according to embodiments may comprise the steps of: receiving geometry data, attribute data and signaling information; decoding the geometry data on the basis of the signaling information; and decoding the attribute data on the basis of the signaling information and the decoded geometry data, wherein the step of decoding the geometry data can include the steps of: generating a geometry tree composed of a plurality of depths; and decoding the geometry data on the basis of the geometry tree.

Description

Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

Embodiments relate to a method and apparatus for processing point cloud content.

Point cloud content is content expressed as a point cloud, a collection of points belonging to a coordinate system that represents three-dimensional space (space or volume). Point cloud content can express three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), XR (Extended Reality), and autonomous driving services. However, expressing point cloud content requires tens to hundreds of thousands of point data. Therefore, a method for efficiently processing massive amounts of point data is required.

That is, transmitting and receiving point cloud data requires a significant amount of processing power. Therefore, encoding for compression and decoding for decompression are performed during the process of transmitting and receiving point cloud data. However, the large size of point cloud data makes the computations complex and time-consuming.

The technical problem according to the embodiments is to provide a device and method for efficiently transmitting/receiving a point cloud in order to solve the problems described above.

The technical problem according to the embodiments is to provide a device and method for resolving latency and encoding/decoding complexity.

The technical problem according to the embodiments is to provide a device and method for efficiently performing geometry encoding/decoding by using a region-adaptive planar mode.

However, the scope of the embodiments is not limited to the technical tasks described above, and the scope of the embodiments may be expanded to other technical tasks that can be inferred by a person skilled in the art based on the entire described content.

To achieve the above-described purpose and other advantages, a decoding method according to embodiments may include a step of receiving geometry data, attribute data, and signaling information, a step of decoding the geometry data, and a step of decoding the attribute data.

According to embodiments, the step of decoding the geometry data includes the step of generating a geometry tree composed of a plurality of depths, and the step of decoding the geometry data based on the geometry tree, wherein the decoding step determines whether planner eligibility exists for each depth of the geometry tree, and decodes geometry data of a depth determined to have planner eligibility based on a planner mode.

According to embodiments, the planner qualification of each depth may be determined based on the density of points at that depth.

According to embodiments, the density of the points may be determined based on the number of points coded in a data unit containing geometry data of the depth, the number of points coded in direct nodes, and the number of points existing at the depth.

According to embodiments, the density of the points may be determined based on information indicating whether the planner is qualified, included in the signaling information.

According to embodiments, the decoding step decodes the geometry data in units of subgroups, and whether each depth qualifies as a planar is also determined in units of subgroups, and each subgroup can be identified by a subgroup index.

According to embodiments, a decoding device includes a memory and at least one processor connected to the memory, wherein the at least one processor is configured to receive geometry data, attribute data, and signaling information, decode the geometry data, and decode the attribute data.

According to embodiments, the at least one processor includes a geometry decoder that decodes the geometry data, the geometry decoder generates a geometry tree composed of a plurality of depths, and can decode the geometry data based on the geometry tree.

According to embodiments, the geometry decoder can determine planner eligibility for each depth of the geometry tree, and decode geometry data of a depth determined to have planner eligibility based on a planner mode.

According to embodiments, the planner qualification of each depth may be determined based on the density of points at that depth.

According to embodiments, the density of the points may be determined based on the number of points coded in a data unit containing geometry data of the depth, the number of points coded in direct nodes, and the number of points existing at the depth.

According to embodiments, the density of the points may be determined based on information indicating whether the planner is qualified, included in the signaling information.

According to embodiments, the geometry decoder decodes the geometry data in units of subgroups, determines whether each depth is planar-qualified in units of subgroups, and each subgroup can be identified by a subgroup index.

According to embodiments, an encoding method may include a step of encoding geometry data, a step of encoding attribute data, and a step of transmitting the encoded geometry data, the encoded attribute data, and signaling information.

According to embodiments, the step of encoding the geometry data includes the step of generating a geometry tree composed of a plurality of depths and the step of encoding the geometry data based on the geometry tree, wherein the encoding step determines whether planner eligibility exists for each depth of the geometry tree, and encodes geometry data of a depth determined to have planner eligibility based on a planner mode.

According to embodiments, the planner qualification of each depth may be determined based on the density of points at that depth.

The device and method according to the embodiments can provide a quality point cloud service.

The devices and methods according to the embodiments can achieve various video codec schemes.

The device and method according to the embodiments can provide general-purpose point cloud content such as autonomous driving services.

The device and method according to the embodiments can provide improved parallel processing and scalability by performing spatially adaptive segmentation of point cloud data for independent encoding and decoding of point cloud data.

The device and method according to the embodiments can improve the encoding and decoding performance of a point cloud by dividing point cloud data into tiles and/or slices to perform encoding and decoding and signaling data required for this.

The device and method according to the embodiments can divide and transmit compressed data based on certain criteria for point cloud data. Furthermore, when layered coding is used, the compressed data can be divided and transmitted according to each layer. Therefore, the storage and transmission efficiency of the transmission device can be increased.

The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments.

Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

Figure 5 shows an example of a point configuration by LOD according to embodiments.

Figure 6 shows an example of a point configuration by LOD according to embodiments.

Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

Figure 8 is an example of a transmission device according to embodiments.

Fig. 9 is an example of a receiving device according to embodiments.

Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission/reception method/device according to embodiments.

Figures 11 and 12 are diagrams showing the encoding, transmission, and decoding processes of point cloud data according to embodiments.

FIG. 13 is a diagram illustrating an example of layer-based point cloud data configuration according to embodiments.

Fig. 14 (a) shows a bitstream structure of geometry data according to embodiments, and Fig. 14 (b) shows a bitstream structure of attribute data according to embodiments.

FIG. 15 is a diagram showing an example of a configuration of a bitstream for dividing a bitstream into layer units and transmitting it according to embodiments.

FIG. 16 illustrates an example of a bitstream alignment method when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

FIG. 17 illustrates another example of a bitstream alignment method when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

FIGS. 18(a) to 18(c) are drawings showing examples of symmetrical geometry-attribute selection according to embodiments.

FIGS. 19(a) to 19(c) are drawings showing examples of asymmetric geometry-attribute selection according to embodiments.

FIGS. 20(a) to 20(c) illustrate examples of a method for constructing slices including point cloud data according to embodiments.

Figures 21(a) and 21(b) illustrate geometry coding layer structures according to embodiments.

Figure 22 illustrates the layer group and subgroup structure according to embodiments.

Figures 23(a) to 23(c) illustrate representations of layer group-based point cloud data according to embodiments.

Fig. 24 illustrates a point cloud data transmission/reception device/method according to embodiments.

Fig. 25 is a flowchart showing an example of an encoding method of an encoder according to embodiments.

Fig. 26 is a flowchart showing an example of a decoding method of a decoder according to embodiments.

Fig. 27 is a drawing showing another example of a point cloud transmission device according to embodiments.

Fig. 28 is a drawing showing another example of a point cloud receiving device according to embodiments.

Figure 29 shows a bitstream configuration according to embodiments.

Figures 30a and 30b illustrate an example of a syntax structure of a sequence parameter set according to embodiments.

Figure 31 illustrates an example of a syntax structure of a dependent geometry data unit header according to embodiments.

Figure 32 illustrates an example of a syntax structure of a layer group structure inventory according to embodiments.

Figure 33 illustrates another example of the syntax structure of a dependent geometry data unit header according to embodiments.

Figures 34(a) and 34(b) are drawings showing an example of the relationship between layer groups and subgroups and FGS according to embodiments.

Figures 35a and 35b illustrate another example of the syntax structure of a sequence parameter set (SPS) according to embodiments.

FIG. 36 is a diagram illustrating an example of a syntax structure of a geometry data unit header according to embodiments.

FIG. 37 is a diagram illustrating another example of the syntax structure of a dependent geometry data unit header according to embodiments.

FIG. 38 is a drawing showing an example of compressing and serving the geometry and attributes of point cloud data according to embodiments.

FIG. 39 is a diagram showing another example of compressing and serving the geometry and attributes of point cloud data according to embodiments.

FIG. 40 is a diagram illustrating another example of compressing and serving the geometry and attributes of point cloud data according to embodiments.

Figure 41 shows a flowchart of a point cloud data transmission method according to embodiments.

Figure 42 shows a flowchart of a method for receiving point cloud data according to embodiments.

Preferred embodiments of the embodiments are described in detail, examples of which are illustrated in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to illustrate preferred embodiments of the embodiments, rather than merely show embodiments that can be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be practiced without these details.

While most of the terms used in the examples are commonly used in the field, some terms were arbitrarily selected by the applicant, and their meanings are described in detail in the following descriptions as needed. Therefore, the examples should be understood based on the intended meaning of the terms, not simply their names or meanings.

FIG. 1 illustrates an example of a point cloud content provision system according to embodiments.

The point cloud content provision system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) are capable of wired and wireless communication to transmit/receive point cloud data.

A transmission device (10000) according to embodiments can secure, process, and transmit a point cloud video (or point cloud content). According to embodiments, the transmission device (10000) can include a fixed station, a base transceiver system (BTS), a network, an Artificial Intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or a server, etc. In addition, according to embodiments, the transmission device (10000) can include a device that performs communication with a base station and/or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR/VR/XR device, a portable device, a home appliance, an IoT (Internet of Things) device, an AI device/server, etc.

A transmission device (10000) according to embodiments includes a Point Cloud Video Acquisition unit (10001), a Point Cloud Video Encoder (10002), and/or a Transmitter (or Communication module), 10003.

A point cloud video acquisition unit (10001) according to embodiments acquires a point cloud video through a processing process such as capture, synthesis, or generation. The point cloud video is point cloud content expressed as a point cloud, which is a collection of points located in a three-dimensional space, and may be referred to as point cloud video data, etc. The point cloud video according to embodiments may include one or more frames. One frame represents a still image/picture. Therefore, the point cloud video may include a point cloud image/frame/picture, and may be referred to as any one of a point cloud image, a frame, and a picture.

A point cloud video encoder (10002) according to embodiments encodes acquired point cloud video data. The point cloud video encoder (10002) may encode point cloud video data based on point cloud compression coding. The point cloud compression coding according to embodiments may include G-PCC (Geometry-based Point Cloud Compression) coding and/or V-PCC (Video-based Point Cloud Compression) coding or next-generation coding. In addition, the point cloud compression coding according to embodiments is not limited to the above-described embodiment. The point cloud video encoder (10002) may output a bitstream including encoded point cloud video data. The bitstream may include not only encoded point cloud video data but also signaling information related to encoding of the point cloud video data.

A transmitter (10003) according to embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to embodiments is encapsulated into a file or segment (e.g., streaming segment) and transmitted through various networks such as a broadcast network and/or a broadband network. Although not shown in the drawing, the transmission device (10000) may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation. In addition, the encapsulation unit may be included in the transmitter (10003) according to embodiments. According to embodiments, the file or segment may be transmitted to a receiving device (10004) through a network or may be stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to embodiments may communicate with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G via wired/wireless communication. Additionally, the transmitter (10003) can perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). Additionally, the transmission device (10000) can transmit encapsulated data in an on-demand manner.

A receiving device (10004) according to embodiments includes a receiver (10005), a point cloud video decoder (10006), and/or a renderer (10007). According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR/VR/XR device, mobile device, home appliance, IoT (Internet of Things) device, AI device/server, etc. that performs communication with a base station and/or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

A receiver (10005) according to embodiments receives a bitstream containing point cloud video data or a file/segment in which the bitstream is encapsulated, from a network or a storage medium. The receiver (10005) may perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). The receiver (10005) according to embodiments may decapsulate the received file/segment and output a bitstream. In addition, the receiver (10005) according to embodiments may include a decapsulation unit (or decapsulation module) for performing the decapsulation operation. In addition, the decapsulation unit may be implemented as a separate element (or component) from the receiver (10005).

A point cloud video decoder (10006) decodes a bitstream containing point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to how it is encoded (e.g., the reverse process of the operation of the point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. The point cloud decompression coding includes G-PCC coding.

The renderer (10007) renders the decoded point cloud video data. In one embodiment, the renderer (10007) may render the decoded point cloud video data according to a viewport, etc. The renderer (10007) may render not only the point cloud video data but also audio data to output point cloud content. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

The dotted arrows in the drawing indicate the transmission path of feedback information acquired from the receiving device (10004). The feedback information is information for reflecting the interaction with the user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service requiring interaction with the user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitter (e.g., the transmitting device (10000)) and/or the service provider. Depending on the embodiments, the feedback information may be used not only by the transmitting device (10000) but also by the receiving device (10004), or may not be provided.

Head orientation information according to embodiments may refer to information about the position, direction, angle, movement, etc. of the user's head. The receiving device (10004) according to embodiments may calculate viewport information based on the head orientation information. The viewport information is information about the area of the point cloud video that the user is looking at (i.e., the area that the user is currently viewing). In other words, the viewport information is information about the area that the user is currently viewing within the point cloud video. In other words, the viewport or the viewport area may refer to the area that the user is viewing within the point cloud video. In addition, the viewpoint is the point that the user is viewing within the point cloud video, and may refer to the exact center point of the viewport area. In other words, the viewport is an area centered on the viewpoint, and the size, shape, etc. that the area occupies may be determined by the FOV (Field Of View). Therefore, the receiving device (10004) may extract viewport information based on the vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device (10004) may perform gaze analysis, etc. based on head orientation information and/or viewport information to check the user's point cloud video consumption method, the point cloud video area the user gazes at, the gaze time, etc. According to embodiments, the receiving device (10004) may transmit feedback information including the gaze analysis result to the transmitting device (10000). According to embodiments, a device such as a VR/XR/AR/MR display may extract a viewport area based on the user's head position/direction, a vertical or horizontal FOV supported by the device, etc. According to embodiments, head orientation information and viewport information may be referred to as feedback information, signaling information, or metadata.

Feedback information according to embodiments may be acquired during the rendering and/or display process. The feedback information according to embodiments may be acquired by one or more sensors included in the receiving device (10004). Additionally, the feedback information according to embodiments may be acquired by the renderer (10007) or a separate external element (or device, component, etc.). The dotted line in Fig. 1 represents the transmission process of the feedback information acquired by the renderer (10007). The feedback information may not only be transmitted to the transmitting side, but may also be consumed by the receiving side. That is, the point cloud content providing system may process (encode/decode/render) point cloud data based on the feedback information. For example, the point cloud video decoder (10006) and the renderer (10007) may use the feedback information, i.e., head orientation information and/or viewport information, to preferentially decode and render only the point cloud video for the area currently being viewed by the user.

Additionally, the receiving device (10004) can transmit feedback information to the transmitting device (10000). The transmitting device (10000) (or point cloud video encoder (10002)) can perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information without processing (encoding/decoding) all point cloud data, and provide point cloud content to the user.

According to embodiments, the transmitting device (10000) may be referred to as an encoder, a transmitting device, a transmitter, a transmitting system, etc., and the receiving device (10004) may be referred to as a decoder, a receiving device, a receiver, a receiving system, etc.

Point cloud data processed (processed through a series of processes of acquisition/encoding/transmission/decoding/rendering) in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

The elements of the point cloud content provision system illustrated in FIG. 1 may be implemented by hardware, software, a processor, and/or a combination thereof.

FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

The block diagram of Fig. 2 illustrates the operation of the point cloud content provision system described in Fig. 1. As described above, the point cloud content provision system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

A point cloud content providing system according to embodiments (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can acquire a point cloud video (20000). The point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to embodiments can include a Ply (Polygon File format or the Stanford Triangle format) file. If the point cloud video has one or more frames, the acquired point cloud video can include one or more Ply files. The Ply file includes point cloud data such as the geometry and/or attributes of points. The geometry includes the positions of points. The position of each point can be expressed as parameters (e.g., values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (e.g., a coordinate system composed of XYZ axes). Attributes include attributes of points (e.g., texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). One point has one or more attributes (or properties). For example, one point may have one attribute of color, or two attributes of color and reflectance. According to embodiments, geometry may be referred to as positions, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc. In addition, a point cloud content providing system (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) may obtain point cloud data from information related to the acquisition process of a point cloud video (e.g., depth information, color information, etc.).

A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data can include geometry and attributes of points. Therefore, the point cloud content providing system can perform geometry encoding to encode geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding to encode attributes and output an attribute bitstream. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. A bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.

A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a transmitter (10003)) can transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data can be expressed as a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). In addition, the point cloud content providing system can encapsulate a bitstream that transmits the encoded point cloud data and transmit it in the form of a file or segment.

A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode encoded point cloud data (e.g., a geometry bitstream, an attribute bitstream) transmitted as a bitstream. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the geometry bitstream to restore positions (geometry) of points. The point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore attributes of points. A point cloud content provision system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can reconstruct a point cloud video based on positions and decoded attributes according to the reconstructed geometry.

A point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) according to embodiments can render decoded point cloud data (20004). The point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) can render the geometry and attributes decoded through the decoding process according to various rendering methods. Points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a certain minimum size centered on the vertex position, or circles centered on the vertex position. All or a portion of the rendered point cloud content is provided to a user through a display (e.g., a VR/AR display, a general display, etc.).

A point cloud content provision system according to embodiments (e.g., a receiving device (10004)) can obtain feedback information (20005). The point cloud content provision system can encode and/or decode point cloud data based on the feedback information. The feedback information and the operation of the point cloud content provision system according to embodiments are identical to the feedback information and operation described in FIG. 1, and therefore, a detailed description thereof will be omitted.

FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

FIG. 3 illustrates an example of a point cloud video encoder (10002) of FIG. 1. The point cloud encoder reconstructs point cloud data (e.g., positions and/or attributes of points) and performs an encoding operation to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) depending on network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps at 30 fps), the point cloud content provision system may not be able to stream the content in real time. Therefore, the point cloud content provision system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment, etc.

As described in FIGS. 1 and 2, the point cloud encoder can perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

The point cloud encoder according to the embodiments includes a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), a LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011) and/or an arithmetic encoder (30012). In the point cloud encoder of FIG. 3, the coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can be grouped and referred to as a geometry encoder. In addition, the color conversion unit (30006), attribute conversion unit (30007), RAHT conversion unit (30008), LOD generation unit (30009), lifting conversion unit (30010), coefficient quantization unit (30011) and/or arithmetic encoder (30012) can be grouped and referred to as an attribute encoder.

The coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can perform geometry encoding. The geometry encoding according to the embodiments can include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. In addition, the geometry encoding is not limited to the above examples.

As illustrated in the drawing, a coordinate system conversion unit (30000) according to embodiments receives positions and converts them into coordinates. For example, the positions may be converted into location information of a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system, etc.). The location information of the three-dimensional space according to embodiments may be referred to as geometry information.

A quantization unit (30001) according to embodiments quantizes geometry. For example, the quantization unit (30001) may quantize points based on the minimum position value of all points (e.g., the minimum value on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation of multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value, and then rounding down or up to find the closest integer value. Accordingly, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. The minimum unit containing two-dimensional image/video information is a pixel, and points of point cloud content (or three-dimensional point cloud video) according to embodiments may be included in one or more voxels. A voxel is a combination of a volume and a pixel, and refers to a three-dimensional cubic space generated when a three-dimensional space is divided into units (unit=1.0) based on axes representing the three-dimensional space (e.g., X-axis, Y-axis, Z-axis). The quantization unit (40001) may match groups of points in the three-dimensional space to voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. In addition, in order to express one voxel as one point, the position of the center of the voxel may be set based on the positions of one or more points included in one voxel. In this case, the attributes of all positions contained in one voxel can be combined and assigned to the voxel.

The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on an octree structure.

The surface approximation analysis unit (30003) according to the embodiments can analyze and approximate an octree. The octree analysis and approximation according to the embodiments is a process of analyzing and voxelizing an area including a large number of points to efficiently provide an octree and voxelization.

An arithmetic encoder (30004) according to embodiments entropy encodes an octree and/or an approximated octree. For example, the encoding method includes an arithmetic encoding method. The encoding results in a geometry bitstream.

The color conversion unit (30006), the attribute conversion unit (30007), the RAHT conversion unit (30008), the LOD generation unit (30009), the lifting conversion unit (30010), the coefficient quantization unit (30011) and/or the arithmetic encoder (30012) perform attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to the attributes of one point. However, when one attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments may include color transform coding, attribute transform coding, RAHT (Region Adaptive Hierarchial Transform) coding, Interpolarization-based hierarchical nearest-neighbor prediction-Prediction Transform) coding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. In addition, attribute encoding according to embodiments is not limited to the above-described examples.

The color conversion unit (30006) according to the embodiments performs color conversion coding to convert color values (or textures) included in attributes. For example, the color conversion unit (30006) may convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion unit (30006) according to the embodiments may be optionally applied depending on the color values included in the attributes.

The geometry reconstruction unit (30005) according to the embodiments reconstructs (decompresses) an octree and/or an approximated octree. The geometry reconstruction unit (30005) reconstructs an octree/voxel based on the results of analyzing the distribution of points. The reconstructed octree/voxel may be referred to as a reconstructed geometry (or restored geometry).

The attribute conversion unit (30007) according to the embodiments performs attribute conversion that converts attributes based on positions for which geometry encoding has not been performed and/or reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit (30007) can convert the attributes based on the reconstructed geometry information. For example, the attribute conversion unit (30007) can convert the attribute of a point at a position based on the position value of the point included in the voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in the voxel, the attribute conversion unit (30007) converts the attributes of one or more points. When try-soup geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the try-soup geometry encoding.

The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of the attributes or attribute values (e.g., the color or reflectance of each point) of neighboring points within a specific position/radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight according to the distance from the center point to each point when calculating the average value. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

The attribute transformation unit (30007) can search for neighboring points within a specific position/radius from the position of the center point of each voxel based on the K-D tree or the Moulton code. The K-D tree is a binary search tree that supports a data structure that can manage points based on their positions to enable fast nearest neighbor search (NNS). The Moulton code represents the coordinate values (e.g. (x, y, z)) indicating the 3D positions of all points as bit values and is generated by mixing the bits. For example, if the coordinate values indicating the position of a point are (5, 9, 1), the bit values of the coordinate values are (0101, 1001, 0001). If the bit values are mixed in the order of z, y, and x according to the bit index, it is 010001000111. If this value is expressed in decimal, it is 1095. That is, the Moulton code value of the point with coordinate values (5, 9, 1) is 1095. The attribute transformation unit (30007) can sort points based on the Moulton code value and perform nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if nearest neighbor search (NNS) is also required in other transformation processes for attribute coding, the K-D tree or Moulton code is utilized.

As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and/or the LOD generation unit (30009).

The RAHT transform unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transform unit (30008) can predict attribute information of a node at an upper level of an octree based on attribute information associated with a node at a lower level of the octree.

The LOD generation unit (30009) according to the embodiments generates a LOD (Level of Detail) to perform predictive transformation coding. The LOD according to the embodiments represents the level of detail of point cloud content. A smaller LOD value indicates lower detail of point cloud content, and a larger LOD value indicates higher detail of point cloud content. Points can be classified according to LOD.

The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms attributes of a point cloud based on weights. As described above, lifting transformation coding may be applied selectively.

The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

The elements of the point cloud encoder of FIG. 3 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 3 described above. Furthermore, the one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud encoder of FIG. 3. The one or more memories according to embodiments may include high-speed random access memory, or may include non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

As described in FIGS. 1 to 3, the point cloud content provision system (point cloud video encoder (10002)) or point cloud encoder (e.g., octree analysis unit (30002)) performs octree geometry coding (or octree coding) based on an octree structure to efficiently manage the area and/or position of a voxel.

The upper part of Fig. 4 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed by the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). The octree structure is generated by recursively subdividing a cubical axis-aligned bounding box defined by two extreme points (0,0,0) and (2 ^d , 2 ^d , 2 ^d ). 2 d can be set to a value that constitutes the smallest bounding box that encloses all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined according to the following equation. In the equation below, (x ^int _n , y ^int _n , z ^int _n ) represent positions (or position values) of quantized points.

As shown in the middle of the upper part of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is expressed as a cube with six faces. As shown in the upper right of Fig. 4, each of the eight spaces is again divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Therefore, each space is again divided into eight smaller spaces. The divided smaller spaces are also expressed as cubes with six faces. This division method is applied until the leaf nodes of the octree become voxels.

The bottom of Fig. 4 shows the occupancy code of the octree. The occupancy code of the octree is generated to indicate whether each of the eight partitioned spaces generated by partitioning one space contains at least one point. Therefore, one occupancy code is expressed by eight child nodes. Each child node represents the occupancy of the partitioned space, and each child node has a value of 1 bit. Therefore, the occupancy code is expressed as an 8-bit code. That is, if the space corresponding to the child node contains at least one point, the node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupancy code illustrated in Fig. 4 is 00100001, it indicates that the spaces corresponding to the third and eighth child nodes among the eight child nodes each contain at least one point. As shown in the drawing, the third child node and the eighth child node each have eight child nodes, and each child node is expressed by an 8-bit occupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30004)) can entropy encode the occupancy code. In addition, the point cloud encoder can intra/inter code the occupancy code to increase compression efficiency. A receiving device according to embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs an octree based on the occupancy code.

A point cloud encoder according to embodiments (e.g., the point cloud encoder of FIG. 3, or the octree analysis unit (30002)) can perform voxelization and octree coding to store the positions of points. However, points within a 3D space are not always evenly distributed, and thus, there may be specific areas where there are not many points. Therefore, performing voxelization on the entire 3D space is inefficient. For example, if there are few points in a specific area, there is no need to perform voxelization up to that area.

Therefore, the point cloud encoder according to the embodiments can perform direct coding that directly codes the positions of points included in the specific region (or nodes excluding leaf nodes of the octree) without performing voxelization for the specific region described above. The coordinates of the direct coded points according to the embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to the embodiments can perform trisoup geometry encoding that reconstructs the positions of points within the specific region (or node) on a voxel basis based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments can be selectively performed. Additionally, direct coding and tri-subtractive geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

In order to perform direct coding, the option to use direct mode for applying direct coding must be activated, the node to which direct coding is to be applied must not be a leaf node, and there must be points below a threshold within a specific node. In addition, the total number of points subject to direct coding must not exceed a preset threshold. If the above conditions are satisfied, the point cloud encoder (or arithmetic encoder (30004)) according to the embodiments can entropy code the positions (or position values) of the points.

A point cloud encoder according to embodiments (e.g., surface approximation analysis unit (30003)) can determine a specific level of an octree (when the level is smaller than the depth d of the octree) and, starting from that level, perform tri-subject geometry encoding to reconstruct the positions of points within a node region on a voxel basis using a surface model (tri-subject mode). A point cloud encoder according to embodiments can specify a level to which tri-subject geometry encoding is to be applied. For example, when the specified level is equal to the depth of the octree, the point cloud encoder does not operate in tri-subject mode. That is, a point cloud encoder according to embodiments can operate in tri-subject mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic area of nodes at a specified level according to embodiments is called a block. One block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented by a surface. According to embodiments, a surface may intersect each edge of the block at most once.

Since one block has 12 edges, there are at least 12 intersections within one block. Each intersection is called a vertex. A vertex existing along an edge is detected if there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An occupied voxel according to embodiments means a voxel containing a point. The position of a vertex detected along an edge is the average position along the edge of all voxels adjacent to the edge among all blocks sharing the edge.

When a vertex is detected, the point cloud encoder according to the embodiments calculates the starting point of the edge (x, y, z), the direction vector of the edge ( x, y, z), vertex position values (relative position values within an edge) can be entropy-coded. When tri-subspace geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., geometry reconstruction unit (30005)) can perform triangle reconstruction, up-sampling, and voxelization processes to generate restored geometry (reconstructed geometry).

The vertices located at the edge of a block determine the surface passing through the block. According to the embodiments, the surface is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② Subtract the centroid value from each vertex value, and ③ Square the values, and then add up all the values to obtain the value.

Then, the minimum of the added values is found, and the projection process is performed according to the axis with the minimum value. For example, if the x element is minimum, each vertex is projected to the x-axis based on the center of the block, and projected onto the (y, z) plane. If the value produced when projected onto the (y, z) plane is (ai, bi), the θ value is found through atan2(bi, ai), and the vertices are sorted based on the θ value. The table below shows the combination of vertices to create a triangle depending on the number of vertices. The vertices are sorted in order from 1 to n. Table 1 below shows that two triangles can be formed depending on the combination of vertices for four vertices. The first triangle may be composed of the 1st, 2nd, and 3rd vertices among the sorted vertices, and the second triangle may be composed of the 3rd, 4th, and 1st vertices among the sorted vertices.

[Table 1] Triangles formed from vertices ordered 1,… , n n Triangles 3 (1,2,3) 4 (1,2,3), (3,4,1) 5 (1,2,3), (3,4,5), (5,1,3) 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5) 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7) 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1) 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3) 10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5) 11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7) 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

The upsampling process is performed to voxelize the triangle by adding points in the middle along the edges. Additional points are generated based on the upsampling factor and the width of the block. The additional points are called refined vertices. A point cloud encoder according to embodiments can voxelize the refined vertices. The point cloud encoder can also perform attribute encoding based on the voxelized positions (or position values).

Figure 5 shows an example of a point configuration by LOD according to embodiments.

As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trysoup geometry encoding is applied, the geometry reconstruction process includes triangle reconstruction, upsampling, and voxelization. Since attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

A point cloud encoder (e.g., LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to LOD. The left side of the drawing shows the original point cloud content. The second figure from the left in the drawing shows the distribution of points of the lowest LOD, and the rightmost figure in the drawing shows the distribution of points of the highest LOD. That is, points of the lowest LOD are sparsely distributed, and points of the highest LOD are densely distributed. That is, as LOD increases in the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between points becomes shorter.

Figure 6 shows an example of a point configuration by LOD according to embodiments.

As described in FIGS. 1 to 5, a point cloud content providing system, or a point cloud encoder (e.g., a point cloud video encoder (10002), the point cloud encoder of FIG. 3, or a LOD generation unit (30009)) can generate a LOD. The LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distances). The LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.

The upper part of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in 3D space. The original order in Fig. 6 represents the order of points P0 to P9 before LOD generation. The LOD-based order in Fig. 6 represents the order of points according to LOD generation. The points are rearranged by LOD. Additionally, a higher LOD includes points belonging to a lower LOD. As shown in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes points of LOD0 and P1, P6, and P3. LOD2 includes points of LOD0, points of LOD1, and P9, P8, and P7.

As described in FIG. 3, the point cloud encoder according to the embodiments can selectively or in combination perform predictive transform coding, lifting transform coding, and RAHT transform coding.

A point cloud encoder according to embodiments can perform predictive transformation coding to generate a predictor for points and set a predicted attribute (or predicted attribute value) for each point. That is, N predictors can be generated for N points. The predictor according to embodiments can calculate a weight (= 1/distance) value based on the LOD value of each point, indexing information for neighboring points existing within a distance set for each LOD, and distance values to the neighboring points.

According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the product of the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point and the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (for example, the coefficient quantization unit (30011)) can quantize and inverse quantize the residual values (which may be referred to as residual attribute, residual attribute value, attribute prediction residual value, etc.) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is as shown in Tables 2 and 3 below.

int PCCQuantization(int value, int quantStep) { if( value >=0) { return floor(value / quantStep + 1.0 / 3.0); } else { return -floor(-value / quantStep + 1.0 / 3.0); } }

int PCCInverseQuantization(int value, int quantStep) { if( quantStep ==0) { return value; } else { return value * quantStep; } }

A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the quantized and dequantized residuals as described above when there are neighboring points to the predictor of each point. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the attributes of the point without performing the above-described process when there are no neighboring points to the predictor of each point.

A point cloud encoder according to embodiments (e.g., lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights according to the distance to the neighboring points. Lifting transformation coding according to embodiments is similar to the above-described predictive transformation coding, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values according to embodiments is as follows.

1) Create an array QW (QuantizationWieght) that stores the weight values of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the weight of the current point's predictor by the QW value of the predictor index of the neighboring node registered in the predictor.

2) Lift prediction process: To calculate the predicted attribute value, the weighted value of the point's attribute value is multiplied and subtracted from the existing attribute value.

3) Create temporary arrays called updateweight and update and initialize them to 0.

4) For each predictor, the calculated weights are multiplied by the weights stored in the QW corresponding to the predictor index, and the resulting weights are cumulatively added to the update weight array as the index of the neighboring node. The update array accumulates the values obtained by multiplying the calculated weights by the attribute values of the indexes of the neighboring nodes.

5) Lift update process: For each predictor, the attribute values in the update array are divided by the weight values in the update weight array of the predictor index, and the existing attribute values are added to the divided value.

6) For all predictors, the predicted attribute values are calculated by additionally multiplying the updated attribute values through the lift update process by the weights (stored in QW) updated through the lift prediction process. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., arithmetic encoder (30012)) entropy-codes the quantized attribute values.

A point cloud encoder according to embodiments (e.g., RAHT transform unit (30008)) can perform RAHT transform coding that predicts attributes of upper-level nodes using attributes associated with nodes at lower levels of an octree. RAHT transform coding is an example of attribute intra coding through octree backward scan. A point cloud encoder according to embodiments scans from a voxel to the entire area, and repeats the merging process up to a root node while merging voxels into larger blocks at each step. The merging process according to embodiments is performed only for occupied nodes. The merging process is not performed for empty nodes, and the merging process is performed for the node immediately above the empty node.

The following equation represents the RAHT transformation matrix. g _lx,y,z represents the average attribute value of voxels at level l. g _lx,y,z can be calculated from g _{l+1 2x,y,z} and g _{l+1 2x+1,y,z} . The weights of g _{l 2x,y,z} and g _{l 2x+1,y,z} are w1=w _{l 2x,y,z} and w2=w _{l 2x+1,y,z} .

g _{l-1 x,y,z} are low-pass values, which are used in the merging process at the next higher level. h _{l-1 x,y,z} are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy coded (e.g., encoding of an arithmetic encoder (30012)). The weights are computed as w _{l-1 x,y,z} = w _{l 2x,y,z} + w _{l 2x+1,y,z} . The root node is generated as follows through the last g _{1 0,0,0} and g _{1 0,0,1} .

The gDC values are also quantized and entropy coded, like the high-pass coefficients.

Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 7 is an example of a point cloud decoder and can perform a decoding operation, which is the reverse process of the encoding operation of the point cloud encoder described in FIGS. 1 to 6.

As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

A point cloud decoder according to embodiments includes an arithmetic decoder (7000), an octree synthesizer (7001), a surface approximation synthesizer (7002), a geometry reconstructor (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), a LOD generator (7008), an inverse lifting (7009), and/or an inverse transform colors (7010).

The arithmetic decoder (7000), the octree synthesis unit (7001), the surface oproximation synthesis unit (7002), the geometry reconstruction unit (7003), and the coordinate system inversion unit (7004) can perform geometry decoding. Geometry decoding according to embodiments can include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied selectively. In addition, geometry decoding is not limited to the above examples, and is performed by the reverse process of the geometry encoding described in FIGS. 1 to 6.

An arithmetic decoder (7000) according to embodiments decodes a received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder (7000) corresponds to the reverse process of the arithmetic encoder (30004).

The octree synthesis unit (7001) according to the embodiments can generate an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the geometry obtained as a result of decoding). A specific description of the occupancy code is as described in FIGS. 1 to 6.

The surface off-axis synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and/or the generated octree when the tri-sub geometry encoding is applied.

The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on the surface and/or decoded geometry. As described in FIGS. 1 to 6, direct coding and try-soup geometry encoding are selectively applied. Therefore, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding is applied. In addition, when try-soup geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing a reconstruction operation of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. The specific details are the same as described in FIG. 4 and are therefore omitted. The restored geometry may include a point cloud picture or frame that does not include attributes.

The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of points by transforming the coordinate system based on the restored geometry.

The arithmetic decoder (7005), the inverse quantization unit (7006), the RAHT transform unit (7007), the LOD generation unit (7008), the inverse lifting unit (7009), and/or the color inverse transform unit (7010) can perform attribute decoding. The attribute decoding according to the embodiments can include RAHT (Region Adaptive Hierarchical Transform) decoding, prediction transform (Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding. The three decodings described above can be used selectively, or a combination of one or more decodings can be used. In addition, the attribute decoding according to the embodiments is not limited to the above-described examples.

An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attributes obtained as a result of the decoding and outputs the inverse quantized attributes (or attribute values). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

According to embodiments, the RAHT transform unit (7007), the LOD generator (7008), and/or the inverse lifting unit (7009) may process the reconstructed geometry and the inverse quantized attributes. As described above, the RAHT transform unit (7007), the LOD generator (7008), and/or the inverse lifting unit (7009) may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.

The color inverse transform unit (7010) according to the embodiments performs inverse transform coding to inversely transform the color values (or textures) included in the decoded attributes. The operation of the color inverse transform unit (7010) may be selectively performed based on the operation of the color transform unit (30006) of the point cloud encoder.

The elements of the point cloud decoder of FIG. 7 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in a point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 7 described above. Furthermore, the one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud decoder of FIG. 7.

Figure 8 is an example of a transmission device according to embodiments.

The transmission device illustrated in FIG. 8 is an example of the transmission device (10000) of FIG. 1 (or the point cloud encoder of FIG. 3). The transmission device illustrated in FIG. 8 can perform at least one or more of the same or similar operations and encoding methods as the operations and encoding methods of the point cloud encoder described in FIGS. 1 to 6. A transmission device according to embodiments may include a data input unit (8000), a quantization processing unit (8001), a voxelization processing unit (8002), an octree occupancy code generation unit (8003), a surface model processing unit (8004), an intra/inter coding processing unit (8005), an arithmetic coder (8006), a metadata processing unit (8007), a color conversion processing unit (8008), an attribute conversion processing unit (or a property conversion processing unit) (8009), a prediction/lifting/RAHT conversion processing unit (8010), an arithmetic coder (8011), and/or a transmission processing unit (8012).

The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform operations and/or acquisition methods identical or similar to those of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

The data input unit (8000), quantization processing unit (8001), voxelization processing unit (8002), octree occupancy code generation unit (8003), surface model processing unit (8004), intra/inter coding processing unit (8005), and arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is the same or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., position values of points or position values). The operation and/or quantization of the quantization processing unit (8001) is identical to or similar to the operation and/or quantization of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIGS. 1 to 6.

The voxelization processing unit (8002) according to the embodiments voxels the position values of quantized points. The voxelization processing unit (80002) may perform operations and/or processes identical or similar to the operations and/or voxelization processes of the quantization unit (30001) described in FIG. 3. Specific descriptions are identical to those described in FIGS. 1 to 6.

The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on the octree structure. The octree occupancy code generation unit (8003) can generate an occupancy code. The octree occupancy code generation unit (8003) can perform operations and/or methods identical or similar to those of the point cloud encoder (or octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is the same as that described in FIGS. 1 to 6.

The surface model processing unit (8004) according to the embodiments can perform tri-subject geometry encoding to reconstruct the positions of points within a specific area (or node) on a voxel basis based on the surface model. The surface model processing unit (8004) can perform operations and/or methods identical or similar to those of the point cloud encoder (e.g., surface approximation analysis unit (30003)) described in FIG. 3. The specific description is the same as that described with reference to FIGS. 1 to 6.

The intra/inter coding processing unit (8005) according to embodiments may perform intra/inter coding on point cloud data. The intra/inter coding processing unit (8005) may perform coding identical to or similar to intra/inter coding. According to embodiments, the intra/inter coding processing unit (8005) may be included in an arithmetic coder (8006).

An arithmetic coder (8006) according to embodiments entropy encodes an octree and/or an approximated octree of point cloud data. For example, the encoding method includes an arithmetic encoding method. The arithmetic coder (8006) performs operations and/or methods identical or similar to those of the arithmetic encoder (30004).

The metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides the metadata to a necessary processing step, such as geometry encoding and/or attribute encoding. In addition, the metadata processing unit (8007) according to the embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. The signaling information according to the embodiments may be encoded and processed separately from geometry encoding and/or attribute encoding. In addition, the signaling information according to the embodiments may be interleaved.

The color conversion processing unit (8008), the attribute conversion processing unit (8009), the prediction/lifting/RAHT conversion processing unit (8010), and the arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

The color conversion processing unit (8008) according to the embodiments performs color conversion coding to convert the color values included in the attributes. The color conversion processing unit (8008) can perform color conversion coding based on the reconstructed geometry. The description of the reconstructed geometry is the same as that described with reference to FIGS. 1 to 6. In addition, the color conversion processing unit (8008) performs the same or similar operation and/or method as that of the color conversion unit (30006) described with reference to FIG. 3. A detailed description thereof will be omitted.

The attribute transformation processing unit (8009) according to embodiments performs attribute transformation to transform attributes based on positions for which geometry encoding has not been performed and/or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and/or methods that are the same as or similar to those of the attribute transformation unit (30007) described in FIG. 3. A detailed description thereof will be omitted. The prediction/lifting/RAHT transformation processing unit (8010) according to embodiments can code transformed attributes by using any one or a combination of RAHT coding, prediction transformation coding, and lifting transformation coding. The prediction/lifting/RAHT transformation processing unit (8010) performs at least one or more of operations that are the same as or similar to those of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the description of the prediction transformation coding, lifting transformation coding, and RAHT transformation coding is the same as that described in FIGS. 1 to 6, so a detailed description is omitted.

An arithmetic coder (8011) according to embodiments can encode coded attributes based on arithmetic coding. The arithmetic coder (8011) performs operations and/or methods identical or similar to those of the arithmetic encoder (300012).

The transmission processing unit (8012) according to embodiments may transmit each bitstream including encoded geometry and/or encoded attribute, metadata information, or may transmit the encoded geometry and/or encoded attribute, and metadata information as one bitstream. When the encoded geometry and/or encoded attribute, and metadata information according to embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information including a Sequence Parameter Set (SPS) for sequence-level signaling, a Geometry Parameter Set (GPS) for signaling geometry information coding, an Attribute Parameter Set (APS) for signaling attribute information coding, and a Tile Parameter Set (TPS) for tile-level signaling, and slice data. The slice data may include information about one or more slices. A slice according to embodiments may include one geometry bitstream (Geom0 ⁰ ) and one or more attribute bitstreams (Attr0 ⁰ , Attr1 ⁰ ).

A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

A TPS according to embodiments may include information about each tile (e.g., coordinate value information of a bounding box and height/size information, etc.) for one or more tiles. A geometry bitstream may include a header and a payload. The header of a geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload. As described above, a metadata processing unit (8007) according to embodiments may generate and/or process signaling information and transmit it to a transmission processing unit (8012). According to embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data/information with each other as indicated by a dotted line. The transmission processing unit (8012) according to the embodiments may perform operations and/or transmission methods identical or similar to those of the transmitter (10003). A detailed description thereof is omitted as it is the same as that described in FIGS. 1 and 2.

Fig. 9 is an example of a receiving device according to embodiments.

The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1. The receiving device illustrated in FIG. 9 can perform at least one of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 8.

A receiving device according to embodiments may include a receiving unit (9000), a receiving processing unit (9001), an arithmetic decoder (9002), an occupancy code-based octree reconstruction processing unit (9003), a surface model processing unit (triangle reconstruction, up-sampling, voxelization) (9004), an inverse quantization processing unit (9005), a metadata parser (9006), an arithmetic decoder (9007), an inverse quantization processing unit (9008), a prediction/lifting/RAHT inverse transform processing unit (9009), a color inverse transform processing unit (9010), and/or a renderer (9011). Each component of the decoding according to embodiments may perform the reverse process of the component of the encoding according to embodiments.

The receiving unit (9000) according to the embodiments receives point cloud data. The receiving unit (9000) may perform operations and/or receiving methods identical or similar to those of the receiver (10005) of FIG. 1. A detailed description thereof will be omitted.

The receiving processing unit (9001) according to the embodiments can obtain a geometry bitstream and/or an attribute bitstream from the received data. The receiving processing unit (9001) can be included in the receiving unit (9000).

The arithmetic decoder (9002), the occupancy code-based octree reconstruction processing unit (9003), the surface model processing unit (9004), and the inverse quantization processing unit (9005) can perform geometry decoding. Since the geometry decoding according to the embodiments is identical or similar to the geometry decoding described in at least one of FIGS. 1 to 8, a detailed description thereof will be omitted.

An arithmetic decoder (9002) according to embodiments can decode a geometry bitstream based on arithmetic coding. The arithmetic decoder (9002) performs operations and/or coding identical to or similar to those of the arithmetic decoder (7000).

The occupancy code-based octree reconstruction processing unit (9003) according to embodiments can reconstruct an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the geometry obtained as a result of decoding). The occupancy code-based octree reconstruction processing unit (9003) performs the same or similar operations and/or methods as those of the octree synthesis unit (7001) and/or the octree generation method. The surface model processing unit (9004) according to embodiments can perform tri-sub geometry decoding and related geometry reconstructing (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when tri-sub geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as those of the surface off-ratio synthesis unit (7002) and/or the geometry reconstructing unit (7003).

The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

The metadata parser (9006) according to the embodiments can parse metadata, such as setting values, contained in the received point cloud data. The metadata parser (9006) can pass the metadata to geometry decoding and/or attribute decoding. A detailed description of the metadata is omitted as it is the same as described in FIG. 8.

The arithmetic decoder (9007), the inverse quantization processing unit (9008), the prediction/lifting/RAHT inverse transform processing unit (9009), and the color inverse transform processing unit (9010) perform attribute decoding. Since attribute decoding is identical or similar to the attribute decoding described in at least one of FIGS. 1 to 8, a detailed description thereof will be omitted.

An arithmetic decoder (9007) according to embodiments can decode an attribute bitstream using arithmetic coding. The arithmetic decoder (9007) can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder (9007) performs operations and/or coding identical or similar to those of the arithmetic decoder (7005).

The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs operations and/or methods identical or similar to the operations and/or inverse quantization methods of the inverse quantization unit (7006).

The prediction/lifting/RAHT inverse transform processing unit (9009) according to embodiments can process reconstructed geometry and inverse quantized attributes. The prediction/lifting/RAHT inverse transform processing unit (9009) performs at least one or more of the same or similar operations and/or decodings as the operations and/or decodings of the RAHT transformation unit (7007), the LOD generation unit (7008), and/or the inverse lifting unit (7009) of FIG. 7. The color inverse transform processing unit (9010) according to embodiments performs inverse transform coding for inverse transforming the color value (or texture) included in the decoded attributes. The color inverse transform processing unit (9010) performs the same or similar operations and/or inverse transform coding as the operations and/or inverse transform coding of the color inverse transform unit (7010) of FIG. 7. A renderer (9011) according to embodiments can render point cloud data.

Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission/reception method/device according to embodiments.

The structure of FIG. 10 represents a configuration in which at least one of a server (1060), a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050), and/or an HMD (1070) is connected to a cloud network (1010). The robot (1010), the autonomous vehicle (1020), the XR device (1030), the smartphone (1040), or the home appliance (1050) are referred to as devices. In addition, the XR device (1030) may correspond to or be linked with a point cloud data (PCC) device according to embodiments.

A cloud network (1000) may refer to a network that constitutes part of a cloud computing infrastructure or exists within the cloud computing infrastructure. Here, the cloud network (1000) may be configured using a 3G network, a 4G or LTE (Long Term Evolution) network, or a 5G network.

The server (1060) is connected to at least one of a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050), and/or an HMD (1070) through a cloud network (1000), and can assist in at least part of the processing of the connected devices (1010 to 1070).

The HMD (Head-Mount Display) (1070) represents one of the types in which the XR device and/or the PCC device according to the embodiments can be implemented. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I/O unit, a sensor unit, and a power supply unit.

Below, various embodiments of devices (1010 to 1050) to which the above-described technology is applied are described. Here, the devices (1010 to 1050) illustrated in FIG. 10 can be linked/combined with the point cloud data transmission/reception devices according to the above-described embodiments.

<PCC+XR>

The XR/PCC device (1030) may be implemented as a HMD (Head-Mount Display), a HUD (Head-Up Display) equipped in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a fixed robot, a mobile robot, etc., by applying PCC and/or XR (AR+VR) technology.

The XR/PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data acquired through various sensors or from external devices to generate location data and attribute data for 3D points, and can render and output an XR object to be output. For example, the XR/PCC device (1030) can output an XR object including additional information about a recognized object in correspondence with the recognized object.

<PCC+XR+Mobile Phone>

The XR/PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

The mobile phone (1040) can decode and display point cloud content based on PCC technology.

<PCC+Autonomous Driving+XR>

Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

An autonomous vehicle (1020) to which XR/PCC technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images, or an autonomous vehicle that is the subject of control/interaction within an XR image. In particular, an autonomous vehicle (1020) that is the subject of control/interaction within an XR image is distinct from an XR device (1030) and can be linked with each other.

An autonomous vehicle (1020) equipped with a means for providing XR/PCC images can obtain sensor information from sensors including cameras and output XR/PCC images generated based on the obtained sensor information. For example, the autonomous vehicle (1020) can be equipped with a HUD to output XR/PCC images, thereby providing passengers with XR/PCC objects corresponding to real objects or objects on a screen.

At this time, when the XR/PCC object is output to the HUD, at least a part of the XR/PCC object may be output so as to overlap with an actual object toward which the passenger's gaze is directed. On the other hand, when the XR/PCC object is output to a display provided inside the autonomous vehicle, at least a part of the XR/PCC object may be output so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may output XR/PCC objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, etc.

VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology and/or PCC (Point Cloud Compression) technology according to the embodiments can be applied to various devices.

In other words, VR technology is a display technology that provides only CG images of real-world objects or backgrounds. On the other hand, AR technology refers to a technology that shows a virtually created CG image on top of an image of an actual object. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects in the real world. However, in AR technology, the distinction between real objects and virtual objects created with CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are considered to have the same characteristics as real objects. A more specific example is the hologram service, which is an application of the aforementioned MR technology.

However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are often referred to as XR (extended reality) technologies. Therefore, the embodiments of the present disclosure are applicable to all VR, AR, MR, and XR technologies. These technologies can be applied to encoding/decoding based on PCC, V-PCC, and G-PCC technologies.

The PCC method/device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

Vehicles providing autonomous driving services are connected to PCC devices to enable wired/wireless communication.

A point cloud data (PCC) transmission/reception device according to embodiments, when connected to a vehicle to enable wired/wireless communication, can receive/process content data related to AR/VR/PCC services that can be provided together with autonomous driving services and transmit the same to the vehicle. In addition, when the point cloud data transmission/reception device is mounted on a vehicle, the point cloud transmission/reception device can receive/process content data related to AR/VR/PCC services and provide the same to a user according to a user input signal input through a user interface device. A vehicle or a user interface device according to embodiments can receive a user input signal. The user input signal according to embodiments can include a signal instructing an autonomous driving service.

The point cloud data transmission method/device according to the embodiments is interpreted as a term referring to the transmission device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the point cloud video encoder of FIG. 3, the transmission device of FIG. 8, the device of FIG. 10, the encoding method of FIG. 25, the transmission device of FIG. 27, the transmission method of FIG. 41, etc.

The point cloud data receiving method/device according to the embodiments is interpreted as a term referring to the receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, transmission-decoding-rendering (20002-20003-20004) of FIG. 2, point cloud video decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoding method of FIG. 26, receiving device of FIG. 28, receiving method of FIG. 41, etc.

Additionally, the point cloud data transmission/reception method/device according to the embodiments may be abbreviated as the method/device according to the embodiments.

According to embodiments, geometry data, geometry information, location information, etc., which constitute point cloud data, are interpreted as having the same meaning. Attribute data, attribute information, property information, etc., which constitute point cloud data, are interpreted as having the same meaning.

The method/device according to the embodiments can process point cloud data taking into account scalable transmission.

The method/device according to the embodiments describes a method for efficiently supporting selective decoding of a portion of data when transmitting/receiving point cloud data due to receiver performance or transmission speed. In particular, this document proposes a technique for increasing the efficiency of scalable coding, wherein an encoder on the transmitting side can selectively transmit information required by a decoder on the receiving side for already compressed data, and the decoder can decode the information. In this case, a coding unit can be configured as an independent slice according to the meaning of a tree level, LOD, layer group unit, etc.

In particular, this paper proposes a method for improving the efficiency of scalable coding among point cloud data compression methods. Here, scalable coding is a technology that can gradually change the resolution of data depending on the circumstances such as the request/processing speed/performance/transmission bandwidth of the receiving end, so that the transmitting end can efficiently transmit compressed data and the receiving end can decode the compressed data. To this end, in addition to the technology of the present disclosure, packing can be applied to effectively transmit point cloud data organized based on layers, and in particular, when a direct compression mode is used for position compression, a method for organizing and transmitting/receiving slice segments to be more suitable for scalable PCC services is proposed. In addition, a compression method can be used for efficient storage and transmission of large-capacity point cloud data with a wide distribution and high point density.

Referring to the point cloud data transmission/reception device (or may be abbreviated as encoder/decoder) according to the embodiments illustrated in FIGS. 3 and 7, the point cloud data is composed of a set of points, and each point is composed of geometry information (or referred to as geometry or geometry data) and attribute information (or referred to as attribute or attribute data). The geometry information is three-dimensional position information (xyz) of each point. That is, the position of each point is expressed as parameters on a coordinate system representing a three-dimensional space (for example, parameters (x, y, z) of the three axes representing the space, namely the X-axis, Y-axis, and Z-axis). In addition, the attribute information means the color (RGB, YUV, etc.), reflectance, normal vectors, transparency, etc. of the point. Point Cloud Compression (PCC) uses octree-based compression to efficiently compress distribution characteristics that are unevenly distributed in three-dimensional space, and compresses attribute information based on this. The point cloud video encoder and point cloud video decoder illustrated in FIGS. 3 and 7 can process operation(s) according to embodiments through each component.

According to embodiments, a transmitting device compresses geometric information (e.g., location) and attribute information (e.g., color/brightness/reflectivity, etc.) of point cloud data and transmits them to a receiving device. At this time, point cloud data can be organized according to an octree structure with layers or LoD (Level of Detail) depending on the level of detail, and scalable point cloud data coding and representation are possible based on this. At this time, it is possible to decode or represent only a part of the point cloud data depending on the performance or transmission speed of the receiving device, but there is currently no method for removing unnecessary data in advance.

That is, in cases where only a portion of a scalable point cloud compression bitstream needs to be transmitted (e.g., when only some layers are decoded during scalable decoding), it is not possible to selectively transmit only the required portion. Therefore, the required portion must be re-encoded after decoding at the transmitting device, as shown in Fig. 11, or the entire bitstream must be transmitted to the receiving device, as shown in Fig. 12, and then the required data must be selectively applied after decoding at the receiving device.

However, in the case of Fig. 11, there may be a delay due to the time for decoding and re-encoding, and in the case of Fig. 12, there is a disadvantage in that bandwidth efficiency is reduced because unnecessary data is transmitted to the receiving device, and when a fixed bandwidth is used, data quality must be lowered for transmission.

Accordingly, the method/device according to the embodiments provides slices so that the point cloud can be divided and processed by region.

In particular, for octree-based positional compression, entropy-based compression methods and direct coding can be used together, in which case a slice configuration is proposed to efficiently utilize scalability.

Additionally, the method/device according to the embodiments can define a slice segmentation structure of point cloud data and signal a scalable layer and slice structure for scalable transmission.

The method/device according to the embodiments can process a bitstream by dividing it into specific units for efficient bitstream transmission and decoding.

The method/device according to the embodiments enables selective transmission and decoding of point cloud data composed of layers in bitstream units.

A unit according to embodiments may be referred to as LOD, layer, slice, etc. LOD is a term similar to LOD of attribute data coding, but in another sense, it may mean a data unit for a layer structure of a bitstream. LOD according to embodiments may be a concept corresponding to one depth or binding two or more depths based on the depth (level) of the layer structure of point cloud data, for example, an octree or multiple trees. Similarly, a layer is a concept corresponding to one depth or binding two or more depths for generating a unit of a sub-bitstream, and may correspond to one LOD or corresponding to two or more LODs. In addition, a slice is a unit for configuring a unit of a sub-bitstream, and may correspond to one depth, a portion of one depth, or corresponding to two or more depths. In addition, a slice may correspond to one LOD, a portion of one LOD, or corresponding to two or more LODs. In embodiments, LOD, layer, and slice may correspond to or be in an inclusive relationship with each other. Additionally, units according to embodiments may include LODs, layers, slices, layer groups, subgroups, etc., and may be referred to as complementary to each other. According to embodiments, in an octree structure, layer, depth, level, and depth level may be used with the same meaning.

Fig. 13 is a diagram illustrating an example of a layer-based point cloud data configuration according to embodiments. Fig. 13 is an example of an octree structure in which the depth level of the root node is set to 0 and the depth level of the leaf node is set to 7.

The method/device according to the embodiments can encode and decode point cloud data by configuring layer-based point cloud data as shown in FIG. 13.

Layering of point cloud data according to embodiments may have a layer structure in various perspectives such as SNR, spatial resolution, color, temporal frequency, bit depth, etc. depending on the application field, and may form layers in the direction of increasing data density based on an octree structure or LOD structure.

That is, when generating LOD based on an octree structure, the LOD can be defined to increase in the direction of increasing detail, i.e., in the direction of increasing octree depth levels. In this document, the term "layer" can be used interchangeably with "level," "depth," and "depth level."

As an example, in Fig. 13, in an octree structure having 7 depth levels excluding the root node level (or referred to as the root level), LOD 0 is configured including from the root node level to octree depth level 4, LOD 1 is configured including from the root node level to octree depth level 5, and LOD 2 is configured including from the root node level to octree depth level 7.

Fig. 14 (a) shows a bitstream structure of geometry data according to embodiments, and Fig. 14 (b) shows a bitstream structure of attribute data according to embodiments.

The method/device according to the embodiments can generate LODs based on layering of an octree structure as in FIG. 13, and configure a geometry bitstream and an attribute bitstream as in (a) and (b) of FIG. 14.

A transmission device according to embodiments can divide a bitstream obtained through point cloud compression into a geometry bitstream and an attribute bitstream according to the type of data and transmit the bitstream.

At this time, each bitstream can be transmitted as a slice. According to embodiments, a geometry bitstream (e.g., (a) of FIG. 14) and an attribute bitstream (e.g., (b) of FIG. 14) can each be transmitted as a slice, regardless of layer information or LoD information. In this case, in order to use only a part of a layer or LoD, a process of decoding the bitstream, a process of selecting only the part to be used and removing unnecessary parts, and a process of re-encoding based only on the necessary information must be performed.

This paper proposes a method to divide the bitstream into layers (or LoDs) and deliver them to avoid these unnecessary intermediate processes.

FIG. 15 is a diagram showing an example of a configuration of a bitstream for dividing the bitstream into layer (or LoD) units and transmitting it according to embodiments.

For example, considering the case of LoD-based PCC technology, it has a structure in which a lower LoD is included in a higher LoD. That is, the upper LoD includes all points of the lower LoD. In addition, if the information of points newly added for each LoD, that is, points included in the current LoD but not in the previous LoD, is defined as R (rest or retained), the transmitting device can divide the initial LoD information and the information R newly included in each LoD into independent units (e.g., slices) and transmit them, as shown in FIG. 15.

In other words, the set of points newly added compared to the previous LoD to constitute each LoD can be defined as information R. Figure 15 is an example in which LoD1 includes LoD0 + information R1, and LoD2 includes LoD1 + information R2.

According to one embodiment, points sampled for one or more octree depth levels can be determined as data belonging to information R. That is, a set of points sampled (i.e., matching occupied nodes) for one or more octree depth levels can be defined as information R. According to another embodiment, points sampled for one octree depth level can be defined by dividing them into multiple pieces of information R according to a certain criterion. At this time, various criteria for dividing one octree depth level into multiple pieces of information R can be considered as follows. For example, when dividing one octree depth level into M pieces of information R, data in the information R can be made into M pieces of information R having consecutive Moulton codes, or M pieces of information R can be made by grouping together pieces with the same remainder when Moulton code order index is divided by M, or M pieces of information R can be made by grouping together pieces at the same position when grouped as sibling nodes. According to another embodiment, if necessary, some of the sampled points of multiple octree depth levels can be determined as information R.

Figure 15 shows an example in which a geometry bitstream and an attribute bitstream are each divided into three slices according to embodiments. Each slice includes a header and a payload (or data unit) containing actual data (e.g., geometry data, attribute data). The header may include information about the corresponding slice. In addition, the header may further include reference information related to a previous slice, a previous LoD, or a previous layer for LoD configuration.

Taking Fig. 15 as an example, the geometry bitstream is divided into a slice that carries geometry data belonging to LoD0, a slice that carries geometry data belonging to information R1, and a slice that carries geometry data belonging to information R2. And the attribute bitstream is divided into a slice that carries attribute data belonging to LoD0, a slice that carries attribute data belonging to information R1, and a slice that carries attribute data belonging to information R2.

The receiving method/device according to the embodiments can receive a bitstream divided into LODs or layers and efficiently decode only the data to be used without a complex intermediate process.

At this time, various embodiments can be applied to the method of transmitting the bitstream.

For example, the geometry bitstream and the attribute bitstream may be transmitted separately, or the geometry bitstream and the attribute bitstream may be multiplexed into a single bitstream and transmitted.

Additionally, when each bitstream includes LoD0 and one or more pieces of information R, the transmission order of LoD0 and one or more pieces of information R may be different.

Figure 15 is an example in which a geometry bitstream and an attribute bitstream are transmitted respectively, and at this time, LoD0 including a geometry bitstream and two pieces of information R(R1, R2) are transmitted sequentially, and LoD0 including an attribute bitstream and two pieces of information R(R1, R2) are transmitted sequentially.

FIG. 16 illustrates an example of a bitstream alignment method when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

The transmission method/device according to the embodiments can serially transmit geometry data and attribute data as illustrated in FIG. 16 when transmitting a bitstream. At this time, depending on the type of data, the entire geometry data (or geometry information) can be transmitted first, and then attribute data (or attribute information) can be transmitted. In this case, there is an advantage in that the geometry data can be quickly restored based on the information in the transmitted bitstream.

FIG. 16 illustrates an example in which layers (LODs) including geometry data may be positioned first in the bitstream, and layers (LODs) including attribute data may be positioned after the geometry layer. Since attribute data is dependent on geometry data, layers (LODs) including geometry data may be positioned before layers (LODs) including attribute data. FIG. 16 illustrates an example in which LoD0 including geometry data and two pieces of information R (R1, R2) are sequentially transmitted, and then LoD0 including attribute data and two pieces of information R (R1, R2) are sequentially transmitted. At this time, the positions may be variously changed according to embodiments. In addition, references between geometry headers are possible, and references between attribute headers and geometry headers are also possible.

FIG. 17 illustrates another example of a bitstream alignment method when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

The transmission method/device according to the embodiments can serially transmit geometry data and attribute data as shown in FIG. 17 when transmitting a bitstream. At this time, bitstreams constituting the same layer including geometry data and attribute data can also be collected and transmitted. In this case, if a compression technique capable of parallel decoding of geometry and attributes is used, the decoding execution time can be shortened. At this time, information that must be processed first (small LoD, geometry must precede attributes) can be placed first.

Figure 17 is an example in which LoD0 including geometry data, LoD0 including attribute data, information R1 including geometry data, information R1 including attribute data, information R2 including geometry data, and information R2 including attribute data are transmitted in that order. At this time, the position can be changed in various ways depending on the embodiments. In addition, references between geometry headers are possible, and references between attribute headers and geometry headers are also possible.

The transmission/reception method/device according to the embodiments can efficiently select a desired layer (or LoD) in an application field at the bitstream level when transmitting and receiving a bitstream. In the bitstream alignment method according to the embodiments, when geometry information is collected and transmitted as in FIG. 16, an empty part may appear in the middle after selecting a specific bitstream level, and in this case, the bitstream may need to be rearranged.

Meanwhile, when geometry data and attribute data are bundled and transmitted according to layers as in Fig. 17, necessary information can be selectively transmitted and/or unnecessary information can be selectively removed as in Figs. 18(a) to 18(c) or Figs. 19(a) to 19(c) depending on the application field.

FIGS. 18(a) to 18(c) are drawings showing examples of symmetrical geometry-attribute selection according to embodiments.

In cases where a part of a bitstream needs to be selected according to embodiments, taking FIGS. 18(a) to 18(c) as an example, the transmitting device selects and transmits only up to LoD1 (i.e., LoD0+R1), and removes and does not transmit information R2 (i.e., a new part among LoD2) corresponding to the upper layer from the bitstream. In the case of symmetric geometry-attribute selection, geometry data and attribute data of the same layer are selected and transmitted simultaneously, or selected and removed simultaneously.

Figures 19(a) to 19(c) are diagrams illustrating examples of asymmetric geometry-attribute selection according to embodiments. In the case of asymmetric geometry-attribute selection, only one of the geometry data and attribute data of the same layer is selected and transmitted or removed.

In embodiments where a part of a bitstream needs to be selected, taking FIGS. 19(a) to 19(c) as examples, the transmitting device selects and transmits LoD1 (LoD0 +R1) including geometry data, LoD1 (LoD0 +R1) including attribute data, and R2 including geometry data, and removes R2 including attribute data from the bitstream and does not transmit it. That is, attribute data is an example of selecting and transmitting the remaining data except for data of an upper layer (R2), and geometry data is an example of transmitting data of all layers (from level 0 (root level) to level 7 (leaf level) of an octree structure).

In cases where a portion of a bitstream needs to be selected according to embodiments, a portion of the bitstream can be selected by using the symmetrical geometry-attribute selection method of FIGS. 18(a) to 18(c) or the asymmetrical geometry-attribute selection method of FIGS. 19(a) to 19(c) or a combination of the symmetrical geometry-attribute selection method and the asymmetrical geometry-attribute selection method.

The segmentation of bitstreams and selection of some bitstreams described above are intended to support the scalability of point cloud data.

This document represents point cloud data in an octree structure and supports scalable encoding/decoding (scalability) when separated by LOD (or layer).

Scalability features according to embodiments may include slice level scalability and/or octree level scalability.

According to embodiments, a LoD may be used as a unit to represent a set of one or more octree layers. Additionally, a LoD may also have the meaning of a bundle of octree layers to be configured as a slice unit.

LOD according to the embodiments can be used in a broad sense, such as a unit for dividing data in detail, by extending the meaning of LOD when encoding/decoding attributes.

That is, spatial scalability by the actual octree layer (or scalable attribute layer) can be provided for each octree layer, but it can be selected at the LoD level when configuring scalability at the slice level before bitstream parsing.

Taking Fig. 13 as an example, in the octree structure, the level from the root level to the 4th level corresponds to LoD0, the level from the root level to the 5th level corresponds to LoD1, and the level from the root level to the 7th level (i.e., the leaf level) corresponds to LoD2.

That is, taking Fig. 13 as an example, when utilizing scalability in slice units, the scalable stages provided are three stages of LoD0, LoD1, and LoD2, and the scalable stages that can be provided in the decoding stage by the octree structure are eight stages from the root level to the leaf level.

According to embodiments, when LoD0 to LoD2 are each composed of slices, a transcoder (see FIG. 11) of a receiver or transmitter may select only LoD0, only LoD1, or LoD2 for scalable processing. In FIG. 13, LoD1 includes LoD0, and LOD2 includes LoD1 and LoD2.

For example, if only LoD0 is selected, the maximum octree level is 4, and one scalable layer among octree layers 0 to 4 can be selected during the decoding process. At this time, the receiving device can consider the node size that can be obtained through the maximum octree level (or depth) as a leaf node, and the node size at this time can be transmitted as signaling information.

For example, if LoD1 is selected, layer 5 is added, so that the maximum octree level becomes 5, and one scalable layer among octree layers 0 to 5 can be selected during the decoding process. At this time, the receiving device can consider the node size that can be obtained through the maximum octree level (or depth) as a leaf node, and can transmit the node size at this time as signaling information. According to embodiments, octree depth, octree layer, octree level, etc. mean units for dividing data in detail.

For example, if LoD2 is selected, layers 6 and 7 are added, so that the maximum octree level becomes 7, and one scalable layer among the octree layers 0 to 7 can be selected during the decoding process. At this time, the receiving device can consider the node size that can be obtained through the maximum octree level (or depth) as a leaf node, and the node size at this time can be transmitted as signaling information.

FIGS. 20(a) to 20(c) illustrate examples of a method for constructing slices including point cloud data according to embodiments.

The transmission method/device/encoder according to the embodiments may be configured by dividing a G-PCC bit stream into a slice structure. A slice may be a data unit for detailed data representation.

For example, one slice may have one or more octree layers (or depths).

A transmission method/device according to embodiments, for example, an encoder, may scan nodes (points) included in an octree in the direction of a scan order (41000) to construct a bitstream based on a slice (41001). One slice may include nodes of one or more levels in the octree structure, may include only nodes of a specific level, may include only some nodes of a specific level, or may include only some nodes of one or more levels.

Fig. 20(a) shows an example in which an octree structure is composed of seven slices. Slice (41002) may be composed of nodes from level 0 to level 4. Slice (41003) may be composed of some nodes of level 5, slice (41004) may be composed of other some nodes of level 5, and slice (41005) may be composed of still other some nodes of level 5. That is, level 5 in Fig. 20(a) is divided into three slices. Similarly, level 6 (i.e., leaf level) in Fig. 20(a) is also divided into three slices. In other words, one slice may be composed of some nodes of a specific level.

Figure 20(b) shows an example in which an octree structure is composed of four slices, with one slice composed of nodes from level 0 to level 3 and some nodes of level 4, one slice composed of the remaining nodes of level 4 and some nodes of level 5, and one slice composed of the remaining nodes of level 5 and some nodes of level 6. In addition, one slice composed of the remaining nodes of level 5 and some nodes of level 6, and one slice composed of the remaining nodes of level 6.

Fig. 20(c) shows an example in which an octree structure is composed of five slices, with one slice composed of nodes from level 0 to level 3, and four slices composed of nodes from level 4 to level 6. That is, one slice is composed of some nodes of level 4, some nodes of level 5, and some nodes of level 6. That is, one slice at level 4 or level 6 can include some data of level 4 and data of level 5 or level 6 corresponding to child nodes of that data.

In other words, as shown in FIGS. 20(b) and 20(c), when multiple octree layers are matched to a single slice, only some nodes of each layer may be included. In this way, when multiple slices constitute a single geometry/attribute frame, the information required to configure the layers at the receiving device can be transmitted to the receiving device through signaling information. For example, the signaling information may include layer information included in each slice, node information included in each layer, etc.

An encoder and a device corresponding to the encoder according to the embodiments can encode point cloud data and generate and transmit a bitstream further including signaling information (or parameter information) regarding the encoded data and the point cloud data.

Furthermore, when generating a bitstream, the bitstream can be generated based on a bitstream structure according to embodiments (e.g., see FIGS. 14-20, etc.). Accordingly, a receiving device, a decoder, a corresponding device, etc. according to embodiments can receive and parse a bitstream configured to suit a selective decoding structure of some data, thereby decoding only a portion of the point cloud data and efficiently providing it.

The following describes scalable transmission of point cloud data.

The point cloud data transmission method/device according to the embodiments can scalably transmit a bitstream including point cloud data, and the point cloud data reception method/device according to the embodiments can scalably receive and decode the bitstream.

When a bitstream having the structure described in FIGS. 11 to 20 is used for scalable transmission, signaling information for selecting a slice required by the receiving device can be transmitted to the receiving device. Scalable transmission may mean transmitting or decoding only a portion of the bitstream rather than transmitting or decoding the entire bitstream, and the result may be low resolution point cloud data.

When applying scalable transmission to an octree-based geometry bitstream according to embodiments, it should be possible to construct point cloud data with only information up to a specific octree layer for the bitstream of each octree layer (Fig. 13) from the root node to the leaf node.

To achieve this, the target octree layer must not depend on information from lower octree layers. This can be a common constraint for geometry/attribute coding.

In addition, during scalable transmission, it is necessary to transmit a scalable structure for selecting a scalable layer from the transmitting/receiving device to the receiving device. Considering the octree structure according to the embodiments, all octree layers may support scalable transmission, but scalable transmission may be enabled only for a specific octree layer or lower. For example, if some of the octree layers are included, the receiving device can determine whether the corresponding slice is necessary or unnecessary at the bitstream stage by notifying the receiving device of which scalable layer the corresponding slice is included in through signaling information. In the example of Fig. 20(a), levels 0 (i.e., the root level) to 4 (41002) do not support scalable transmission and constitute a single scalable layer, and the octree layers below can be configured to have a one-to-one matching with the scalable layer. In general, scalability can be supported for the part corresponding to the leaf node, and in the case where multiple octree layers are included in one slice, as in Fig. 20(c), the layers can be defined to form one scalable layer.

At this time, scalable transmission and scalable decoding can be used separately depending on the purpose. According to embodiments, scalable transmission can be used for the purpose of selecting information up to a specific layer without going through a decoder in a transmitting/receiving device. According to embodiments, scalable decoding can be used for the purpose of selecting a specific layer during coding. That is, scalable transmission can support the selection of required information without going through a decoder in a compressed state (i.e., at the bitstream stage), thereby enabling the identification of a specific layer in a transmitting or receiving device. On the other hand, scalable decoding can be used in cases such as scalable representation by supporting encoding/decoding only up to the required part in the encoding/decoding process.

In this case, the layer configuration for scalable transmission and the layer configuration for scalable decoding may differ. For example, the three lower octree layers including leaf nodes may constitute one layer from the perspective of scalable transmission, but from the perspective of scalable decoding, scalable decoding may be possible for each of the leaf node layer, leaf node layer-1, and leaf node layer-2 if all layer information is included.

Figures 21(a) and 21(b) illustrate geometry coding layer structures according to embodiments. In particular, Figure 21(a) is a diagram showing an example of three slices generated by a layer group structure in an encoder on the transmitting side, and Figure 21(b) is a diagram showing an example of a partially decoded output using two slices in a decoder on the receiving side.

According to embodiments, when fine granularity slicing (FGS) is enabled, a G-PCC bitstream may be segmented into multiple sub-bitstreams. Here, fine granularity slicing may be referred to as layer group-based slicing. To effectively utilize the layering structure of G-PCC, each slice may include coded data of a partial coding layer or a partial region. Segmentation or partitioning of slices paired with the coding layer structure may efficiently support scalable transmission or spatial random access use cases.

Layer group based slice segmentation

In fine-grained slicing, each slice segment may contain data coded from a group of layers defined as follows.

A layer group can be defined as a group of consecutive tree layers, and the depth of the group of tree layers can be any number within the tree depth, and the start depth can be less than the end depth. The order of the coded data in a slice segment can be the same as the order of the coded data in a single slice. In the present disclosure, a tree layer can be referred to as a tree level.

For example, considering a geometry coding layer structure with eight coding layers as shown in Fig. 21(a), there are three layer groups, and each layer group matches a different slice. More specifically, layer group 1 for coding layers 0 to 4 matches slice 1, layer group 2 for coding layer 5 matches slice 2, and layer group 3 for coding layers 6-7 matches slice 3. When the first two slices (i.e., slices 1 and 2) are transmitted or selected, the decoded output becomes partial layers 0 to 5 as shown in Fig. 21(b). Using slices in the layer group structure allows for partial decoding of coding layers without accessing the entire bitstream.

Bitstream and point cloud data according to embodiments can be generated based on coding layer-based slice segmentation. By slicing the bitstream at the end of the coding layer of the encoding process, the method/device according to embodiments can select relevant slices, thereby supporting scalable transmission or partial decoding.

Figure 21(a) illustrates a geometry coding layer structure in which each slice has eight layers (or levels) corresponding to a layer group. Layer group 1 includes coding layers 0 to 4. Layer group 2 includes coding layer 5. Layer group 3 is a group for coding layers 6 and 7. When a geometry (or attribute) has a tree structure with eight levels (depths), data corresponding to one or more levels (depths) can be grouped to hierarchically structure a bitstream. Each group can be included in one slice.

Figure 21(b) shows the decoded output when two of three slices are selected. When the decoder selects Group 1 and Group 2, partial layers from levels 0 to 5 of the tree are selected. In other words, using slices in a layer group structure, partial decoding of a coding layer can be supported without accessing the entire bitstream.

For the partial decoding process according to the embodiments, the encoder may generate three slices based on the layer group structure. The decoder according to the embodiments may select two slices from the three slices to perform partial decoding.

A bitstream according to embodiments may include slices based on layer groups. Each slice may include a header including signaling information regarding point cloud data (i.e., geometry data and/or attribute data) included in the slice. A receiving method/device according to embodiments may select slices and decode point cloud data included in the payload of the slice based on the header included in the selected slice.

In addition to the layer group structure, the method/device according to the embodiments can further divide the layer group into multiple subgroups, taking into account spatial random access use cases. The subgroups according to the embodiments are mutually exclusive, and the set of subgroups can be identical to the layer group. Since the points of each subgroup form a boundary in a spatial region, the subgroups can be represented by subgroup bounding box information. Using the spatial information, the layer group and subgroup structures can support access to a region of interest (ROI) by selecting slices that cover the ROI. Spatial random access within a frame or tile can be supported by efficiently comparing the region of interest (ROI) with the bounding box information of each slice.

The method/device according to the embodiments can configure a slice for transmitting point cloud data as shown in FIG. 21(a).

According to embodiments, the entire coded bitstream may be included in a single slice. Furthermore, for multiple slices, each slice may include a sub-bitstream. The order of the slices may be identical to the order of the sub-bitstreams. In addition, each slice may correspond to a layer group in a tree structure.

Additionally, slices may not affect previous slices, just as higher layers in a geometry tree do not affect lower layers.

Segmented slices according to the embodiments are efficient in terms of error robustness, effective transmission, supporting region of interest, etc.

1) Error resilience

Compared to a single-slice structure, split slices can be more error-resistant. That is, if a single slice contains the entire bitstream of a frame, data loss can affect the entire frame data. Conversely, if a bitstream is split into multiple slices, even if at least one slice is lost, at least one slice unaffected by the loss can still be decoded.

2) Scalable transmission

This document may consider cases where multiple decoders with different capabilities can be supported.

If the coded point cloud data (i.e., the Point Cloud Compression (PCC) bitstream) is contained within a single slice, the LOD of the coded point cloud data can be determined prior to encoding. Therefore, multiple pre-encoded bitstreams with different resolutions of the point cloud data can be transmitted independently. This can be inefficient in terms of bandwidth or storage space.

If the coded point cloud data (i.e., the Point Cloud Compression (PCC) bitstream) is contained in segmented slices, a single bitstream can support different levels of decoders. From the decoder side, the receiver can select target layers and transmit a partially selected bitstream to the decoder. Similarly, by using a single bitstream without segmenting the entire bitstream, partial bitstreams can be efficiently generated at the transmitter.

3) Region-based spatial scalability

In terms of G-PCC requirements according to embodiments, region-based spatial scalability can be defined as follows: a compressed bitstream consists of one or more layers, so that a specific region of interest can have higher density with additional layers, and layers can be predicted from lower layers.

To support this requirement, it is necessary to support region-wise different detailed representations. For example, in VR/AR applications, it is desirable to represent far objects with lower precision, while representing closer objects with higher precision. Furthermore, the decoder can increase the resolution of the region of interest upon request. This can be achieved by utilizing scalable structures of G-PCC, such as geometry octrees and scalable attribute coding schemes.

According to embodiments, decoders must access the entire bitstream based on the current slice structure, which includes the entire geometry or attributes. This may result in bandwidth, memory, and decoder inefficiencies. On the other hand, if the bitstream is segmented into multiple slices, and each slice includes sub-bitstreams according to scalable layers, the decoder according to embodiments can select a slice as needed before efficiently parsing the bitstream.

The method/device according to the embodiments can create a layer group using a tree structure (or layer structure) of point cloud data.

As an example, in Fig. 21(a), there are eight layers (or levels) within a geometry coding layer structure (e.g., an octree structure), and three slices can be used to contain one or more layers. A group represents a group of layers. When scalable attribute coding is used, the tree structure is identical to the geometry tree structure. The same octree-slice mapping can be used to create attribute slice segments.

A layer group according to embodiments represents a bundle of layer structure units generated in G-PCC coding, such as an octree layer, LoD layer, etc.

A subgroup can be represented as a set of adjacent nodes within a single layer group. For example, it can be composed of nodes grouped by Morton code order, nodes grouped by distance, or nodes grouped by coding order. Nodes with parent-child relationships can also exist within a single subgroup.

When a subgroup is defined, a boundary occurs in the middle of the layer, and parameters such as entropy_continuation_enabled_flag can be signaled to indicate whether there is entropy continuity at the boundary. Continuity can also be maintained by referencing the previous slice via ref_slice_id.

The tree structure according to the embodiments may be an octree structure, and the attribute layer structure or attribute coding tree according to the embodiments may include a structure of a level of detail (LOD). That is, the tree structure for point cloud data includes layers corresponding to depth or level, and the layers may be grouped.

The method/device according to the embodiments (e.g., the octree analysis unit (30002) or LOD generation unit (30009) of FIG. 3, the octree synthesis unit (7002) or LOD generation unit (7008) of FIG. 7) can generate an octree structure of geometry or an LOD tree structure of attributes. In addition, point cloud data can be grouped based on layers of the tree structure.

Referring to Figure 21(a), multiple layers (or levels) are grouped to form first to third groups (or layer groups). A single group can be further divided to form subgroups.

According to embodiments, a slice may include data coded from a layer group, wherein the layer group is defined as a group of contiguous tree layers, and the start and end depths of the tree layers may be a specific number within the tree depth, with the start being less than the end.

Figure 21(a) shows the geometry coding layer structure as an example of a tree structure, but a coding layer structure for attributes can also be created similarly.

Figure 22 illustrates the layer group and subgroup structure according to embodiments.

Referring to Figure 22, point cloud data and bitstream can be expressed by being separated into bounding boxes.

Referring to Fig. 22, the subgroup structure and the bounding boxes corresponding to the subgroups are illustrated. Layer group 2 is divided into two subgroups (group2-1, group2-2) and included in different slices, and layer group 3 is divided into four subgroups (group3-1, group3-2, group3-3, group3-4) and included in different slices. Given slices of layer groups and subgroups along with bounding box information, spatial access can be performed by 1) comparing the bounding box of each slice with the ROI, 2) selecting a slice in which the subgroup bounding box overlaps the ROI, and 3) decoding the selected slice.

When considering the ROI in region 3-3, slices 1, 3, and 6 are selected as the subgroup bounding boxes of layer group 1, subgroups 2-2, and 3-3 covering the ROI area. For efficient spatial access, it is assumed that there are no dependencies between subgroups within the same layer group. For live streaming or low-latency use cases, selection and decoding can be performed upon receiving each slice segment to improve time efficiency.

The method/device according to the embodiments may represent data as a tree (45000) composed of layers (which may be referred to as depths, levels, etc.) when encoding geometry and/or attributes. Point cloud data corresponding to each layer (depth/level) may be grouped into a layer group (or group, 45001). Layer group 2 may be further divided (segmented) into two subgroups (45002), and layer group 3 may be further divided (segmented) into four subgroups (45003). Each subgroup may be composed of each slice, and a bitstream may be generated.

A receiving device according to embodiments may receive a bitstream, select a specific slice from the bitstream, and decode a bounding box corresponding to a subgroup included in the selected slice. For example, when slice 1 is selected, a bounding box (45004) corresponding to layer group 1 may be decoded. Layer group 1 may be data corresponding to the largest area. When additionally displaying a detailed area for layer group 1, a method/device according to embodiments may partially hierarchically access bounding boxes (point cloud data) of subgroup 2-2 and/or subgroup 3-3 for a detailed area included in an area of layer group 1 by selecting slice 3 and/or slice 6.

Encoding and decoding of point cloud data using the layer group and subgroup of FIG. 22 can be performed by the transmitting/receiving device of FIG. 1, the encoding and decoding of FIG. 2, the transmitting device/method of FIG. 3, the receiving device/method of FIG. 7, the transmitting/receiving device/method of FIG. 8 and FIG. 9, the devices of FIG. 10, the transmitting/receiving methods of FIG. 24, FIG. 25, and FIG. 27, the transmitting/receiving devices of FIG. 26 and FIG. 28, and the transmitting/receiving methods of FIG. 40 and FIG. 41.

Figures 23(a) to 23(c) illustrate representations of layer group-based point cloud data according to embodiments.

The device/method according to the embodiments can provide efficient access to large-scale point cloud data or high-density point cloud data through layer group slicing based on scalability and spatial access capabilities. Because point cloud data has a large number of points and large data sizes, rendering or displaying the content can take a significant amount of time. Therefore, as an alternative approach, the level of detail can be adjusted based on the viewer's interest. For example, when the viewer is far away from a scene or object, structural or global region information is more important than local details, whereas when the viewer is close to a specific area or object, detailed information about the region of interest is needed. Using an adaptive approach, the renderer according to the embodiments can efficiently provide data of sufficient quality to the viewer. Figures 23(a) to 23(c) illustrate examples of increasing detail for three different viewing distances based on the ROI.

The high-level view of Fig. 23(a) shows coarse detail, the mid-level view of Fig. 23(b) shows medium-level detail, and the low-level view of Fig. 23(c) shows fine-grained detail.

Fig. 24 illustrates a point cloud data transmission/reception device/method according to embodiments.

Multi-resolution ROIs according to embodiments may be supported when layer group slicing is used to generate a G-PCC bitstream.

Referring to Fig. 24, multi-resolution ROIs can be supported by the scalability and spatial accessibility of hierarchical slicing. In Fig. 24, the encoder (47001) on the transmitting side can generate bitstream slices of spatial subgroups of each layer group or octree layer-groups. Upon request, a slice matching each resolution ROI is selected and transmitted to the receiving end. The overall bitstream size is reduced compared to the tile-based approach because it does not include details other than the requested ROI. At the receiver, the decoder (47004) can combine the slices to produce three outputs, for example: 1) a high-level view output from the layer group; 2) a mid-level view output from selected subgroups of layer group 1 and layer group 2; and 3) a low-level view output with good detail from selected subgroups of layer groups 2 and 3 and layer group 1. Since the outputs can be generated progressively, the receiver can provide a viewing experience such as zooming in which the resolution progressively increases from the high-level view to the low-level view.

The above encoder (47001) is a point cloud encoder according to embodiments, and may correspond to a geometry encoder and/or an attribute encoder. The encoder (47001) may slice point cloud data based on a layer group (or groups). A layer may be referred to as a tree depth, a LOD level, etc. As in 47002, the depth of an octree of a geometry and/or the level of an attribute layer may be divided into layer groups (or subgroups).

The slice selector (47003) can be linked with the encoder (47001) to select a divided slice (or sub-slice) and selectively transmit it partially, such as layer group 1 to layer group 3.

The decoder (47004) can selectively and partially decode transmitted point cloud data. For example, for a high-level view, it can decode layer group 1 (high depth/layer/level or index 0, closer to the root). Furthermore, for a mid-level view, it can decode based on layer group 1 and layer group 2, increasing the depth/level index slightly more than layer group 1 alone. Furthermore, for a low-level view, it can decode based on layer group 1 to layer group 3.

Referring to FIG. 24, an encoder (47001) according to embodiments can receive point cloud data as input and slice it into layer groups. That is, the point cloud data can be hierarchically structured and divided into layer groups. At this time, the hierarchical structure can mean an octree structure or LoD (Level of Detail). 47002 represents how point cloud data is divided into layer groups. A slice selector (47003) can select a layer group (or a slice corresponding thereto), and the selected slices are transmitted to a decoder (47004) of a receiving end. The decoder (47004) can combine the received slices according to a user's request to restore only layer group 1, layer groups 1 and 2, or all received layer groups. The layer groups are hierarchical and have different levels of detail. When only layer group 1 is restored, the restored range is wide but details are not expressed. When all layer groups 1 to 3 are restored, the restored range is narrow but details can be expressed in detail.

As mentioned above, the inputs of the encoder for layer group slicing are point cloud data and parameter information (e.g., Sequence Parameter Set (SPS), Geometry Parameter Set (GPS), Layer-Group Slicing Inventory or Layer-Group Structure Inventory (LGSI)) describing the structural information of layer group slicing. At the beginning of each tree depth, the layer group of the target tree depth is determined using the layer group structure parameters. Using the layer group index, the subgroup index of each node is determined using the subgroup bounding box. When the subgroup for a node is changed, the context state and buffer used by the previous subgroup encoder are saved and the context state and buffer of the current subgroup encoder are loaded. Using a separate encoder for each subgroup allows the context state to be persisted within the subgroup. Additionally, to restrict neighboring nodes to belong to the same subgroup as the current node, the geometry occupancy atlas is updated by considering subgroup boundaries as well as the atlas boundary (on top of the atlas boundary). Both methods allow each coded bitstream to be independently decoded at the decoder without information about the nodes of neighboring subgroups. This process is performed recursively for all nodes of all tree depths. Upon reaching the end of the nodes of the target tree depth, fine-grained slices that match the subgroups of each layer group one-to-one are generated. In the present disclosure, fine-grained slices may be referred to as layer group-based slices.

The encoding operation of the above encoder can be performed by a combination of at least one or more of the transmitting device of FIG. 1, the encoding of FIG. 2, the transmitting device/method of FIG. 3, the transmitting device/method of FIG. 8 and FIG. 9, the devices of FIG. 10, the encoding method of FIG. 25, the transmitting device of FIG. 27, and the transmitting method of FIG. 40.

The decoding operation of the above decoder can be performed by a combination of at least one or more of the receiving device of FIG. 1, the decoding of FIG. 2, the receiving device/method of FIG. 7, the receiving device/method of FIG. 8 and FIG. 9, the devices of FIG. 10, the decoding method of FIG. 26, the receiving device of FIG. 28, and the receiving method of FIG. 41.

Fig. 25 is a flowchart showing an example of an encoding method of an encoder according to embodiments. That is, Fig. 25 is an encoding process for layer group slicing, which allows an encoded bitstream to be independently decoded in a decoder without node information of neighboring subgroups. The encoding method of Fig. 25 can be performed by the transmitting device of Fig. 1, the encoding of Fig. 2, the transmitting device/method of Fig. 3, the transmitting device/method of Fig. 8, the devices of Fig. 10, the transmitting device of Fig. 27, or a combination thereof.

An encoder according to embodiments generates parameter information such as SPS, GPS, LGSI, etc., and determines layer groups and subgroups (S2501, S2502). Then, it checks whether the subgroup changes (S2503), and whenever the subgroup changes, information used to encode point cloud data within the subgroup is stored and necessary information is loaded to efficiently encode (S2504). For example, context information can be stored and loaded for use during the next encoding. That is, when the subgroup for a node changes, the context state and buffer used in the previous subgroup encoder are stored, and the context state and buffer of the current subgroup encoder are loaded. In addition, the geometry occupancy atlas is updated according to the encoding (S2505). That is, in order to restrict neighboring nodes to belong to the same subgroup as the current node, the geometry occupancy atlas is updated by considering not only the atlas boundary but also the subgroup boundary. Then, the nodes of the subgroup are encoded (S2506). When this process is repeated until all nodes of all depths in the occupancy tree are encoded (S2507, S2508), i.e., when the end of the nodes of the target tree depth is reached, geometry data unit headers are generated, and a geometry bitstream including parameter information, geometry data unit headers, geometry data units, etc. is generated. That is, fine-grained slices that match one-to-one with the subgroups of each layer group are generated. In the present disclosure, geometry occupancy atlas is used interchangeably with geometry atlas or atlas.

The following is a detailed description of the steps (S2501-S2504) of determining layer groups and subgroups and saving and loading them.

As mentioned in the encoder process above, saving and loading the encoder state is necessary to ensure independent decoding of each subgroup. To provide flexible subgroup partitioning, this process is performed for each node in the tree layer.

For each tree layer, the layer group of that tree layer is determined and fixed for all tree depths within that layer group. Since a layer group is a set of contiguous tree layers, the layer group index changes at the beginning of the layer group. Subgroups are determined using the determined layer group. Since a subgroup is a group of nodes bounded by the subgroup bounding box, the subgroup of a node is found by comparing the node position with the subgroup bounding box. Whenever a subgroup or layer group changes, the encoder state of the previous subgroup is saved for later use, and the encoder state of the current subgroup is loaded for continuous encoding.

The pseudo code below is an example of the encoding process for each layer, layer group, and subgroup in the tree structure, including saving and loading the encoder state whenever each layer group and subgroup changes. Specifically, when the tree depth is 0, the first layer group and subgroup are initialized and the current encoder state is loaded. Then, when the tree depth reaches the number of layers in the current layer group, the encoder state is saved and loaded. This process processes the nodes within each layer group, saving and loading the encoder state whenever a subgroup changes.

pseudo code:

for (depth = 0; depth < maxDepth; depth++) {

/ determine layer-group index

if (depth == 0) {

curLayerGroupId = 0;

curSubgroupId = 0;

load current context state;

sum_layers = numLayersPerLayerGroup[curLayerGroupId];

}

else if (depth == sum_layers) {

prevLayerGroupId = curLayerGroupId++;

prevSubgroupId = curSubgroupId;

curSubgroupId = 0;

save previous encoder state;

load current encoder state;

sum_layers += numLayersPerLayerGroup[curLayerGroupId];

}

else if (numSubgroupsMinus1[curLayerGroupId] > 0) {

prevSubgroupId = curSubgroupId;

curSubgroupId = 0;

save previous encoder state;

reload current encoder state;

}

for (all nodes in curLayerGroupId) {

/ determine subgroup index

if (!(nodePos >= bbox_min && nodePos < bbox_max)) {

for (i = 0; i<=numSubgroupsMinus1[curLayerGroupId]; i++) {

if (nodePos >= bbox_min[i] && nodePos < bbox_max[i]){

prevSubgroupId = curSubgroupId;

curSubgroupId = i;

/ save and load encoder

save previous encoder state;

if (first node of the current subgroup)

load reference encoder state;

else

reload current encoder state;

break;

} } } }

}

The following is a detailed description of the Geometry Occupancy Atlas Update step (S2505).

To account for subgroup boundaries in a geometry occupancy atlas, _maxRange and _minRange are defined in the MortonMap3D class. When a geometry occupancy atlas is in a subgroup, the minimum and maximum ranges are set to 0 and the length of an edge of the cube, respectively. If the minimum boundary of the subgroup is greater than the minimum value of the geometry occupancy atlas, _minRange is set to the minimum value of the subgroup boundary. If the maximum boundary of the subgroup is less than the maximum value of the geometry occupancy atlas, _maxRange is set to the maximum value of the subgroup boundary. Using the ranges (_maxRange and _minRange), the part of the atlas that overlaps the subgroup bounding box is considered active, and the nodes in the active area are used as neighbors. This ensures that FGS (i.e. layer group-based slices) are decoded without nodes of neighboring subgroups in the decoder of the receiving device.

The pseudo code below is an example of the subgroup boundary process when updating the aforementioned geometry occupancy atlas. Specifically, the pseudo code below demonstrates the process of calculating the range for setting subgroup boundaries in the MortonMap3D class. The setRange method calculates the minimum and maximum boundaries of the subgroup and sets them in the _minRange and _maxRange variables. These ranges are calculated for each axis in 3D space. _maxRange[m] represents the maximum range for the mth axis of the subgroup, and is set based on the atlas origin and the cube size. _minRange[m] represents the minimum range for the mth axis of the subgroup. If it is greater than the atlas origin, it is set to that value; otherwise, it is set to 0. This process ensures that subgroups are located in specific parts of the atlas, and the portion of the atlas that overlaps with the subgroup bounding box is considered the active area, enabling efficient data processing.

pseudo code:

class MortonMap3D {

setRange( ) {

for (m = 0; m < 3; m++) {

/ _maxRange

if (bboxMax < atlasOrigin + _cubeSize)

_maxRange[m] = bboxMax - atlasOrigin;

else

_maxRange[m] = _cubeSize;

/ _minRange

if (bboxMin > atlasOrigin)

_minRange[m] = bboxMin - atlasOrigin;

else

_minRange[m] = 0;

} }

}

Through this operation, the range of the geometry occupancy atlas used for geometry coding is updated by considering the subgroup boundaries. The geometry occupancy atlas is a lookup table (LUT) created for a certain range of surrounding nodes to improve the speed of geometric neighbor search, etc., and must be updated if the current node's location is outside the range of the geometry occupancy atlas.

Looking at the encoder and decoder behavior, if a subgroup boundary exists within the atlas boundary, the atlas boundary (_maxRange, _minRange) is updated to the subgroup boundary. This allows for the atlas to be used without updating the entire atlas, even when a subgroup within the atlas changes.

Here, _maxRange and _minRange represent the minimum and maximum values of the actual usable range within the atlas. atlasOrigin and cubeSize represent the starting position and size of the geometry occupancy atlas. And bboxMin and _bboxMax represent the minimum and maximum values of the bounding box position of a specific layer group or subgroup.

Fig. 26 is a flowchart showing an example of a decoding method of a decoder according to embodiments. That is, Fig. 26 shows a decoder process for layer group slicing according to embodiments.

Fig. 26 can follow the reverse process of Fig. 25.

The decoding method of FIG. 26 can be performed by the receiving device of FIG. 1, the decoding of FIG. 2, the receiving device/method of FIG. 7, the receiving device/method of FIG. 9, the devices of FIG. 10, the decoder of FIG. 28, or a combination thereof.

According to embodiments, the decoder parses parameter information (e.g., SPS, GPS, LGSI, etc.), geometry data unit headers, and geometry data units from the received geometry bitstream. The decoding process of the layer group slicing reference SW for the first FGS (Fine Granularity Slice) is the same as the conventional geometry slice decoding (parameter set parsing, data unit header parsing, data unit decoding). In the present disclosure, parameter information, parameter(s), and parameter set(s) are used interchangeably with the same meaning.

After the above parsing process is performed, if layer group slicing is enabled (S2601), the next dependent geometry data unit is considered as the FGS for the first slice (i.e., if they have the same Slice_id). Considering the contextual reference and node inheritance between the parent (i.e., superior) subgroup and the child subgroup, the order of the FGS is assumed to be sorted in ascending order by layer_group_id and subgroup_id.

Before decoding a dependent data unit, the context state, output nodes, and layer group parameters of the previous slice are stored in a buffer for the next slices (S2602). That is, the buffers and layer group parameters are updated. Then, after parsing the dependent data unit header (S2603), a parent subgroup of the current subgroup is detected (S2604). According to embodiments, the parent subgroup of the current subgroup is detected by finding a subgroup whose subgroup bounding box is a superset of the current subgroup bounding box. Once the parent subgroup is determined, the parent (i.e., upper) nodes of the current dependent data unit are selected (S2605). Once the parent (i.e., upper) nodes of the current dependent data unit are selected in step S2605, the dependent data unit is parsed (S2606). At this time, by using the selected nodes as initial nodes of the decoding process, the current dependent data unit is decoded up to the tree layer covered by the current layer group. Then, the decoding process of the dependent data unit is repeatedly performed until the geometry bitstream ends (S2607). When the decoding process of the dependent data unit is completed, an output point cloud is generated and the decoding process is terminated (S2608).

The following describes in more detail the additional steps involved in finding parent subgroups and parent nodes.

A decoder according to embodiments parses parameter information such as SPS, GPS, and LGSI included in a bitstream. Parses information signaled in a data unit header included in a slice such as FGS. Parses point cloud data included in a data unit based on the header information. When layer group slicing is enabled, updates buffer and layer group parameters, and parses data unit header information of a dependent data unit dependent on an upper data unit. Detects a parent subgroup and selects a parent node. Parses the dependent data unit. The following is a detailed description of the parent subgroup detection step (S2604).

In decoding fine-grained slices (i.e., layer-group-based slices), nodes in a parent subgroup are used as inputs to child subgroups to provide continuous decoding at layer-group boundaries. This can be derived using parent-child spatial relationships due to the hierarchical structure of layer-group slicing. That is, a child subgroup is a subset of its parent subgroup whose bounding boxes are spatially exclusive from the bounding boxes of other child subgroups within the same layer group.

Based on these relationships, slices with parent subgroups can be detected by finding the spatial superset of the current slice. Furthermore, by utilizing the subgroup_bbox_origin and subgroup_bbox_size signaled in the data unit header, parent subgroups can be found by comparing them with the bounding box information of subgroups at the previous layer-group level.

The pseudo code below is an example of the parent subgroup detection process described above.

pseudo code:

parentLayerGroup = curLayerGroup - 1;

for (i = 0; i < numSubgroups[parentLayerGroup]; i++) {

if (_bboxMin[parentLayerGroup][i] <= curBboxMin

&& _bboxMax[parentLayerGroup][i] > curBboxMin) {

parentSubgroup = i;

break;

}

}

That is, the present disclosure can infer a parent subgroup without additional signaling based on the positional relationship between the parent subgroup and the child subgroup as described above.

From an encoder perspective, defining a child subgroup can be defined to partition the bounding box of the parent subgroup. The child subgroup is a subregion of the parent subgroup, and the encoder can infer the index of the parent subgroup using the method described above.

From the decoder's perspective, if the bounding box of a parent subgroup is a superset of the bounding boxes of a child subgroup, the subgroup index is determined as the parent subgroup index.

In the pseudo code above, parentLayerGroup is the index of the parent layer group relative to the current layer group.

parentSubgroup is the subgroup index of the subgroup that has a parent-child relationship with the current subgroup.

curLayerGroup is the current layer group.

numSubgroups is the number of subgroups belonging to the layer group.

_bboxMin, _bboxMax are the minimum and maximum values of the bounding box position of a specific layer group or subgroup.

curBboxMin is the minimum value of the bounding box position of the subgroup currently being coded.

The following is a detailed description of the input parent node selection step (S2605).

In decoding dependent slices, the output nodes of the parent subgroup are used as input for decoding the child subgroup. If the subgroup bounding boxes of the parent subgroup and the child subgroup are identical, all nodes generated in the parent subgroup are used. On the other hand, if the subgroup boundary box of the child subgroup is a subset of the parent subgroup, the decoder selects the actual parent nodes. To find the parent nodes, the present disclosure compares each node of the parent subgroup with the bounding box boundaries of the child subgroup.

The pseudocode below illustrates the parent node selection process described above. Specifically, only nodes within a specific range (bbox_min and bbox_max) are selected as parent nodes. For example, a node is added as a parent only if its position (node.Pos) is greater than or equal to the minimum (bbox_min) and less than the maximum (bbox_max) of the bounding box.

pseudo code:

for (node = inNodes.begin(); node != inNodes.end(); node++) {

if (node.Pos >= bbox_min && node.Pos < bbox_max)

fifo.emplace_back(node);

else

continue;

}

FIG. 27 is a diagram showing another example of a point cloud transmission device according to embodiments. The elements of the transmission device illustrated in FIG. 27 may be implemented by hardware, software, a processor connected to a memory, and/or a combination thereof. That is, the elements of the transmission device of FIG. 27 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories, although not illustrated in the drawing. One or more processors may perform at least one or more of the operations and/or functions of the elements of the transmission device of FIG. 27 described above. In addition, one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the transmission device of FIG. 27. The execution order of each block in FIG. 27 may be changed, some blocks may be omitted, and some new blocks may be added.

According to embodiments, the point cloud transmission device may include a data input unit (51001), a signaling processing unit (51002), a geometry encoder (51003), an attribute encoder (51004), and a transmission processing unit (51005).

The above geometry encoder (51003) and attribute encoder (51004) can perform some or all of the operations described in the point cloud video encoder (10002) of FIG. 1, the encoding (20001) of FIG. 2, the point cloud video encoder of FIG. 3, the point cloud video encoder of FIG. 8, and the encoding of FIG. 25 and FIG. 41.

The data input unit (51001) according to the embodiments receives or acquires point cloud data. The data input unit (51001) may perform part or all of the operations of the point cloud video acquisition unit (10001) of FIG. 1, or may perform part or all of the operations of the data input unit (8000) of FIG. 8.

The above data input unit (51001) outputs the positions of points of the point cloud data to the geometry encoder (51003) and outputs the attributes of the points of the point cloud data to the attribute encoder (51004). In addition, the parameters are output to the signaling processing unit (51002). According to embodiments, the parameters may be provided to the geometry encoder (51003) and the attribute encoder (51004).

The above geometry encoder (51003) performs geometry compression based on layer groups using the positions of input points. The geometry encoder (51003) performs entropy encoding on the compressed geometry information and outputs it to the transmission processing unit (51005) in the form of a geometry bitstream.

The above geometry encoder (51003) reconstructs geometry information based on positions changed through compression, and outputs the reconstructed (or decoded) geometry information to the attribute encoder (51004).

According to embodiments, the geometry encoder (51003) constructs an octree using positions of input points, performs layer group-based slicing on the octree, selects one or more slices, and then compresses geometry information of the selected one or more slices. Layer group-based slicing and slice-based geometry compression according to embodiments are described in detail in FIGS. 11 to 25, and thus are omitted here to avoid redundant description.

The attribute encoder (51004) compresses attribute information based on positions for which geometry encoding has not been performed and/or reconstructed geometry information. In one embodiment, the attribute information may be coded using one or a combination of one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. The attribute encoder (51004) performs entropy encoding on the compressed attribute information and outputs it to the transmission processing unit (51005) in the form of an attribute bitstream.

The signaling processing unit (51002) may generate and/or process signaling information necessary for encoding/decoding/rendering of geometry information and attribute information and provide the generated signaling information to the geometry encoder (51003), the attribute encoder (51004) and/or the transmission processing unit (51005). Alternatively, the signaling processing unit (51002) may receive signaling information generated from the geometry encoder (51003), the attribute encoder (51004) and/or the transmission processing unit (51005). The signaling processing unit (51002) may also provide information fed back from a receiving device (e.g., head orientation information and/or viewport information) to the geometry encoder (51003), the attribute encoder (51004) and/or the transmission processing unit (51005).

In this specification, signaling information (LGSI, Layer Group Structure Inventory) including layer group-based slicing-related information can be signaled and transmitted in units of parameter sets (SPS: sequence parameter set, GPS: geometry parameter set, APS: attribute parameter set, TPS: Tile Parameter Set (or tile inventory) etc.) and/or data units (i.e., slices). That is, it can also be signaled and transmitted in units of coding units (or compression units or prediction units) of each image, such as slices or tiles.

The above transmission processing unit (51005) may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmission processing unit (8012) of FIG. 8, and may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmitter (10003) of FIG. 1. A specific description will be omitted here, referring to the description of FIG. 1 or FIG. 8.

The above transmission processing unit (51005) multiplexes the geometry bitstream output from the geometry encoder (51003), the attribute bitstream output from the attribute encoder (51004), and the signaling bitstream output from the signaling processing unit (51002) into a single bitstream and transmits it as is, or encapsulates it into a file or segment and transmits it. In this document, as an example, the file is in the ISOBMFF file format.

According to embodiments, files or segments may be transmitted to a receiving device or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmission processing unit (51005) according to embodiments may communicate with the receiving device via a network such as 4G, 5G, or 6G, either wired or wirelessly. In addition, the transmission processing unit (51005) may perform necessary data processing operations depending on the network system (e.g., a communication network system such as 4G, 5G, or 6G). In addition, the transmission processing unit (51005) may also transmit encapsulated data in an on-demand manner.

FIG. 28 is a diagram showing another example of a point cloud receiving device according to embodiments. The elements of the receiving device illustrated in FIG. 28 may be implemented by hardware, software, a processor connected to a memory, and/or a combination thereof. That is, the elements of the receiving device illustrated in FIG. 28 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories, although not illustrated in the drawing. One or more processors may perform at least one or more of the operations and/or functions of the elements of the receiving device illustrated in FIG. 28 described above. Furthermore, one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the receiving device illustrated in FIG. 28. The execution order of each block in FIG. 28 may be changed, some blocks may be omitted, and some new blocks may be added.

According to embodiments, the point cloud receiving device may include a receiving processing unit (61001), a signaling processing unit (61002), a geometry decoder (61003), an attribute decoder (61004), a geometry buffer (61005), an attribute buffer (61006), and a post-processor (61007).

The receiving processing unit (61001) according to the embodiments may receive a single bitstream, or may receive a geometry bitstream, an attribute bitstream, and a signaling bitstream, respectively. When a file and/or segment is received, the receiving processing unit (61001) according to the embodiments may decapsulate the received file and/or segment and output it as a bitstream.

According to the embodiments, when one bitstream is received (or decapsulated), the reception processing unit (61001) can demultiplex a geometry bitstream, an attribute bitstream, and/or a signaling bitstream from the one bitstream, and output the demultiplexed signaling bitstream to the signaling processing unit (61002), the geometry bitstream to the geometry decoder (61003), and the attribute bitstream to the attribute decoder (61004).

According to the embodiments, when a geometry bitstream, an attribute bitstream, and/or a signaling bitstream are each received (or decapsulated), the receiving processing unit (61001) can transmit the signaling bitstream to the signaling processing unit (61002), the geometry bitstream to the geometry decoder (61003), and the attribute bitstream to the attribute decoder (61004).

The receiving processing unit (61001) according to the embodiments may divide the geometry bitstream and the attribute bitstream into slice units, layer groups, or subgroup units based on the signaling information processed by the signaling processing unit (61002), and may transmit the geometry bitstream in slice units/layer groups/subgroup units to the geometry decoder (61003), and the attribute bitstream in slice units/layer groups/subgroup units to the attribute decoder (61004). For example, the input of the geometry decoder (61003) may include a fine granularity slice bitstream (or layer group-based slice bitstream) and a layer-group structure.

The signaling processing unit (61002) may parse and process signaling information, for example, information included in SPS, GPS, APS, TPS, LGSI, metadata, etc., from the input signaling bitstream and provide the information to the receiving processing unit (61001), the geometry decoder (61003), the attribute decoder (61004), the geometry buffer (61005), the attribute buffer (61006), and the post-processing unit (61007). In another embodiment, signaling information included in a geometry data unit header and/or an attribute data unit header may also be pre-parsed by the signaling processing unit (61002) before decoding the corresponding slice (or subgroup) data. In the present disclosure, the geometry buffer (61005) may store nodes belonging to the current subgroup and/or nodes belonging to a parent subgroup of the current subgroup for geometry decoding. Additionally, before decoding a dependent data unit, the context state, output nodes, and layer group parameters of the previous slice are stored in the geometry buffer (61005) for the next slices (S2602). Additionally, the attribute buffer (61006) may include nodes belonging to the current subgroup and/or nodes belonging to the parent subgroup of the current subgroup for attribute decoding.

According to embodiments, the signaling processing unit (61002) may also parse and process information signaled in a sequence parameter set, a geometry parameter set, and/or a geometry data unit header (e.g., layer group-based slicing-related information) and provide the information to a geometry decoder (61003) and a geometry buffer (61005).

According to embodiments, the signaling processing unit (61002) may also parse and process information signaled in a sequence parameter set, an attribute parameter set, and/or an attribute data unit header (e.g., layer group-based slicing-related information) and provide the information to an attribute decoder (61004) and an attribute buffer (61006).

According to embodiments, the geometry decoder (61003) can restore the geometry by performing the reverse process of the geometry encoder (51003) of FIG. 27 based on signaling information (e.g., layer group-based slicing related information) for the compressed geometry bitstream. The geometry decoding performed when layer group slicing is enabled in the geometry decoder (61003) is described in detail in FIGS. 11 to 23 and FIG. 26, and thus is omitted here to avoid redundant description.

The geometry information restored (or reconstructed) from the geometry decoder (61003) is provided to the attribute decoder (61004).

The above attribute decoder (61004) can restore attributes by performing the reverse process of the attribute encoder (51004) of FIG. 27 based on signaling information and reconstructed geometry information for the compressed attribute bitstream.

According to embodiments, the attribute decoder (61004) can perform LoD generation and NN search on a subgroup basis.

According to embodiments, the post-processing unit (61007) can reconstruct and display/render point cloud data by matching the geometry information (i.e., positions) restored and output from the geometry decoder (61003) with the attribute information restored and output from the attribute decoder (61004).

Below, we describe the slice structure for the layer configuration described above and the signaling method for scalable transmission.

Figure 29 shows a bitstream configuration according to embodiments.

The method/device according to the embodiments can generate a bitstream as in FIG. 29. The bitstream includes encoded geometry data and attribute data, and may include parameter information.

The syntax and semantics for parameter information are as follows.

Information about separated slices according to embodiments can be defined in the parameter set and SEI message of the bitstream as follows.

According to embodiments, the bitstream may include a sequence parameter set, a geometry parameter set, an attribute parameter set, a geometry slice header (also called a geometry slice header or geometry data unit header), and an attribute slice header (also called an attribute data unit header). Depending on the application or system, the scope of application, the method of application, etc. may be used differently by defining them in corresponding locations or in separate locations. That is, the signal may have different meanings depending on the location where it is transmitted. If it is defined in SPS, it may be applied equally to the entire sequence. If it is defined in GPS, it may indicate that it is used for location recovery. If it is defined in APS, it may indicate that it is applied to attribute recovery. If it is defined in TPS, it may indicate that the signaling is applied only to points within a tile. If it is transmitted in slice units, it may indicate that the signal is applied only to the corresponding slice. In addition, depending on the application or system, the scope of application, the method of application, etc. may be used differently by defining them in corresponding locations or in separate locations. Additionally, if the syntax element defined below can be applied to multiple point cloud data streams as well as the current point cloud data stream, it can be conveyed through a parameter set of a higher concept, etc.

Each abbreviation means: SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometry bitstream = geometry slice header+ geometry slice data, Attr: Attrobite bitstream = attribute slice header + attribute slice data.

The embodiments define the information independently of the coding technique, but can be defined in conjunction with the coding method, and can be defined in the tile parameter set of the bitstream to support regionally different scalability. In addition, if the syntax element defined below can be applied to multiple point cloud data streams as well as the current point cloud data stream, it can be conveyed through a parameter set of a higher concept, etc.

Alternatively, a bitstream can be selected at the system level by defining a NAL (Network abstract layer) unit for the bitstream and passing relevant information for selecting a layer, such as a layer_id.

According to embodiments, parameters required for encoding and/or decoding of point cloud data may be newly defined in parameter sets of point cloud data (e.g., SPS, GPS, APS, and TPS (or referred to as tile inventory)) and/or headers of corresponding slices (i.e., referred to as data unit headers or slice headers). For example, when performing encoding and/or decoding of geometry information, parameters may be added to a geometry parameter set (GPS), and when performing tile-based encoding and/or decoding, parameters may be added to a tile and/or data unit header.

According to embodiments, layer group-based slicing related information may be signaled in a sequence parameter set and/or a geometry parameter set and/or an attribute parameter set and/or a layer group structure inventory (LGSI).

According to embodiments, layer group-based slicing related information may be signaled in a tile parameter set and/or a geometry data unit header and/or an attribute data unit header and/or a layer group structure inventory (LGSI).

According to embodiments, when the syntax element defined below can be applied to multiple point cloud data streams as well as the current point cloud data stream, layer group-based slicing-related information can be conveyed through a parameter set of a higher concept, etc.

According to embodiments, layer group-based slicing-related information may be defined in corresponding locations or separate locations depending on the application or system, and may be used differently in terms of scope of application, application method, etc. The term "field" used in the syntaxes of this specification described below may have the same meaning as a parameter or syntax element.

Below, parameters (which may be called various names such as metadata, signaling information, etc.) according to the embodiments may be generated in the process of the transmitter according to the embodiments, and may be transmitted to the receiver according to the embodiments and used in the reconstruction process.

According to embodiments, parameters (which may be variously referred to as metadata, signaling information, etc.) containing layer group-based slicing-related information may be generated by a metadata processing unit (or metadata generator) or a signaling processing unit of a transmitting device, and may be transmitted to a receiving device to be used in a decoding/reconstruction process. For example, parameters generated and transmitted by a transmitting device may be acquired by a metadata parser of the receiving device.

According to embodiments, parameters describing a layer group structure (e.g., parameters including layer group-based slicing-related information) are signaled at various levels. For example, in SPS, common structure information is described in the SPS, and details of each layer group or subgroup are signaled in a slice header (or data unit header). In addition, a layer group structure inventory and a dependent slice header (or dependent data unit header) are provided to describe the overall layer group structure. The definition of G-PCC slices and the signaling method of fine-granularity slicing are as follows.

1) Definition of G-PCC slices.

. Slice: A set of points coded as one independent fine-granularity slice and zero or more dependent fine-granularity slices. For example, a slice may contain multiple FGSs, and an FGS may be referred to as a segmented slice, a subdivided slice, etc.

. Dependent Fine-granularity Slice: A data unit in a slice that is dependent on the previous data unit within the same slice.

. Independent Fine-granularity slice: The first [geometry] data unit of the slice.

2) Fine-granularity slices are activated in SPS.

3) The essential information required to decode dependent fine-granularity slices is conveyed by the dependent data unit header, including context inheritance and slice-specific boundary boxes.

4) Define a layer group structure inventory to describe the relationships between fine-granularity slices.

Figures 30a and 30b illustrate an example syntax structure of a sequence parameter set (SPS) according to embodiments. The SPS may include sequence information of a point cloud data bitstream, and in particular, an example of including information related to layer group-based slicing is shown.

The syntaxes of FIGS. 30a and 30b are included in the bitstream of FIG. 29, and can be generated by a point cloud encoder according to embodiments and decoded by a point cloud decoder.

simple_profile_compatibility_flag: Indicates whether the bitstream conforms to the simple profile (if 1) or not (if 0).

dense_profile_compatibility_flag: Indicates whether the bitstream conforms to the Dense profile (if 1) or not (if 0).

predictive_profile_compatibility_flag: Indicates whether the bitstream complies with the prediction profile (if 1) or not (if 0).

main_profile_compatibility_flag: Indicates whether the bitstream complies with the main profile (if 1) or not (if 0).

slice_reordering_constraint_flag: Indicates whether the bitstream is sensitive to the reordering or removal of slices within the coded point cloud frame (if 1) or not (if 0). If slices are reordered or removed when slice_reordering_constraint is 1, the resulting bitstream may not be fully decoded.

unique_point_positions_constraint_flag: Equal to 1 indicates that every point in each coded point cloud frame must have a unique position. If unique_point_positions_constraint_flag is 0, it indicates that more than one point in the coded point cloud frame may have the same position.

sps_seq_parameter_set_id: Identifies the SPS so that other DUs (data units) can reference it.

seq_origin_bits: The length in bits of each seq_origin_xyz syntax element, excluding the sign bit.

seq_origin_xyz[k] and seq_origin_log2_scale: Together, these represent the XYZ origin of the sequence and the coding coordinate system in sequence coordinate units at the application-specific origin. If seq_origin_bits is 0, seq_origin_xyz[k] and seq_origin_log2_scale are inferred to be 0. The kth XYZ component of the origin is specified by the expression SeqOrigin[k] .

seq_bounding_box_size_bits: The length of each seq_bbox_size_minus1_xyz syntax element in bits.

seq_bounding_box_size_minus1_xyz[ k ]: plus 1 represents the kth XYZ component of the coded volume dimension in sequence coordinates.

seq_unit_numerator_minus1, seq_unit_denominator_minus1, and seq_unit_is_metres: Together, they represent lengths expressed as unit vectors in the sequence coordinate system.

sps_num_attribute_sets: Indicates the number of attributes listed in the SPS attribute list.

attribute_instance_id[ attrId ]: Indicates the instance identifier for the identified attribute.

attribute_bitdepth_minus1[ attrId ]: Adding 1 specifies the bit depth for all components of the identified attribute.

A value of layer_group_enabled_flag of 1 indicates that the geometry bitstream of a slice is contained in multiple slices that match a group of coding layers or its subgroups. A value of layer_group_enabled_flag of 0 indicates that the geometry bitstream is contained in a single slice.

num_layer_groups_minus1 + 1 represents the number of layer groups, where the layer group represents a group of contiguous tree layers that are part of the geometry coding tree structure. num_layer_groups_minus1 ranges from 0 to the number of coding tree layers.

layer_group_id represents the layer group ID of the slice (or the indicator of a layer group of a slice). layer_group_id ranges from 0 to num_layer_groups_minus1.

num_layers_minus1 + 1 represents the number of coding layers (or levels) included in the ith layer group. The total number of layer groups can be derived by adding all (num_layers_minus1[i] + 1) to num_layer_groups_minus1 when i is 0.

If the value of subgroup_enabled_flag is 1, it indicates that the ith layer group is divided into two or more subgroups. Here, the set of points included in the subgroups of the layer group is identical to the set of points of the layer group. When the subgroup_enabled_flag of the ith layer group is 1, the subgroup_enabled_flag of the jth layer group is equal to 1 when j is greater than or equal to i. If the value of subgroup_enabled_flag is 0, it indicates that the current layer group is not subdivided into multiple subgroups but is included in a single slice (subgroup_enabled_flag is equal to 1 specifies the i-th layer-group is divided into two or more subgroups where the set of points in the subgroups of a layer-group is identical to the set of points in the layer-group. When subgroup_enabled_flag of the i-th layer-group is equal to 1, subgroup_enabled_flag of the j-th layer-group shall be equal to 1 when j is larger than or equal to i. subgroup_enabled_flag is equal to 0 specifies that the current layer-group is not sub-divided into multiple subgroups and contained in a single slice).

subgroup_bbox_origin_bits_minus1 + 1 is the bit length of the subgroup_bbox_origin field (or syntax element).

subgroup_bbox_size_bits_minus1 + 1 is the bit length of the subgroup_bbox_size field.

In FIG. 30a and FIG. 30b, layer group-based slicing related information may include at least one of layer_group_enabled_flag, num_layer_groups_minus1, layer_group_id[i], num_layers_minus1[i], subgroup_enabled_flag[i], subgroup_bbox_origin_bits_minus1, and subgroup_bbox_size_bits_minus1.

Fig. 31 illustrates an example of a syntax structure of a dependent geometry data unit header according to embodiments. Fig. 31 shows an example in which layer group-based slicing-related information is included in the dependent geometry data unit header.

The syntaxes of FIG. 31 are included in the bitstream of FIG. 29, and can be generated by a point cloud encoder according to embodiments, transmitted to a decoder as a receiving device, and decoded by the point cloud decoder.

According to embodiments, a dependent geometry data unit header may be included in a geometry data unit. A geometry data unit may be used synonymously with a dependent geometry data unit or a geometry slice.

The geometry parameter set ID (dgsh_geometry_parameter_set_id) indicates the active GPS indicated by gps_geom_parameter_set_id. The dgsi_geometry_parameter_set_id value is equal to the gdu_geometry_parameter_set_id value for the corresponding slice.

dgsi_slice_id specifies the geometry slice to which the current dependent geometry data unit belongs.

layer_group_id can indicate an indicator of a layer group in a slice. layer_group_id can range from 0 to num_layer_groups_minus1. If not present, it is assumed to be 0.

subgroup_id indicates a subgroup of the layer group referenced by layer_group_id. The subgroup_id can range from 0 to num_subgroups_minus1[layer_group_id]. Here, subgroup_id can indicate the order of slices within the same layer_group_id. If it does not exist, it is assumed to be 0.

subgroup_bbox_origin indicates the origin position of the subgroup bounding box of the ith subgroup indicated by subgroup_id of the jth layer group indicated by layer_group_id.

subgroup_bbox_size represents the size of the subgroup bounding box of the i-th subgroup indicated by subgroup_id in the j-th layer group indicated by layer_group_id.

According to the embodiments, the bounding box of points in a subgroup is described by subgroup_bbox_origin and subgroup_bbox_size. In this case, the area in the bounding box of the i-th subgroup does not overlap with the bounding box of the j-th subgroup when i and j are not equal.

ref_layer_group_id indicates the layer group identifier of the context reference of the current dependent data unit. ref_layer_group_id ranges from 0 to the layer_group_id of the current dependent data unit.

ref_subgroup_id indicates a reference subgroup of the layer group pointed to by ref_layer_group_id. ref_subgroup_id ranges from 0 to num_subgroup_id_minus1 of the layer group pointed to by ref_layer_group_id.

A bitstream according to embodiments may include at least one slice or subdivided slices, and may include a geometry data unit and an attribute data unit. Each data unit includes a header. In addition, the data unit includes an independent data unit and a dependent data unit. The dependent data unit may indicate a subsumption relationship according to a dependency relationship between upper and lower nodes. In addition, FIG. 31 shows the syntax of a dependent geometry data unit header, and similarly, the syntax of a geometry data unit header may also be as shown in FIG. 31.

Fig. 32 illustrates a layer-group structure inventory (LGSI) according to embodiments. That is, the layer-group structure inventory of Fig. 32 describes the relationship between fine-grained slices as follows.

The sequence parameter set ID (lgsi_seq_parameter_set_id) represents the sps_seq_parameter_set_id value.

lgsi_frame_ctr_lsb_bits represents the length of the lgsi_frame_ctr_lsb field (or syntax element) in bits.

lgsi_frame_ctr_lsb represents the lgsi_frame_ctr_lsb_bits (least significant bits) of the FrameCtr for which the layer group structure inventory is valid. The layer group structure inventory remains valid until it is replaced by another layer group structure inventory.

lgsi_num_slice_ids_minus1 + 1 represents the number of slices in the layer group structure inventory.

lgsi_slice_id represents the slice ID of the sid-th slice within the layer group structure inventory.

lgsi_num_layer_groups_minus1 + 1 represents the number of layer groups.

lasi_subgroup_bbox_origin_bits_minus1 + 1 represents the length of the lgsi_subgroup_bbox_origin field in bits.

lgsi_subgroup_bbox_size_bits_minus1 + 1 represents the length of the lgsi_subgroup_bbox_size field in bits.

lgsi_layer_group_id represents the indicator of the layer group.

lgsi_num_layers_minus1 + 1 represents the number of coded layers in the slice of the i-th layer group of the sid-th slice. The total number of coded layers required to decode the n-th layer group is equal to the sum of lgsi_num_layers_minus1[sid][i] + 1, where i is from 0 to n.

lgsi_num_subgroups_minus1 + 1 represents the number of subgroups in the i-th layer group of the sid-th slice.

lgsi_subgroup_id represents the ID (or indicator) of a layer group (or subgroup). The value of lgsi_subgroup_id is between 0 and lgsi_num_subgroups_minus1.

lgsi_parent_subgroup_id indicates the identifier of a subgroup within the layer group indicated by lgsi_subgroup_id. The value of lgsi_parent_subgroup_id is between 0 and gi_num_subgroups_minus1 within the layer group indicated by lgsi_subgroup_id.

lgsi_subgroup_bbox_origin indicates the origin of the subgroup bounding box of the subgroup indicated by lgsi_subgroup_id of the layer group indicated by lgsi_layer_group_id.

lgsi_subgroup_bbox_size represents the size of the subgroup bounding box of the subgroup indicated by lgsi_subgroup_id in the layer group indicated by lgsi_layer_group_id.

lgsi_origin_bits_minus1 + 1 represents the length of the lgsi_origin_xyz field in bits.

lgsi_origin_xyz represents the origin of all partitions. The value of lgsi_origin_xyz[ k ] is equal to sps_bounding_box_offset[ k ].

lgsi_origin_log2_scale represents a scaling factor for scaling the lgsi_origin_xyz field. The value of lgsi_origin_log2_scale is the same as sps_bounding_box_offset_log2_scale.

As mentioned above, coding (i.e., compression) of geometry information can be performed based on an octree. In this case, the planar mode used in octree-based geometry coding probabilistically determines whether each node is planar. This requires updating the probability each time and tracking the local node density of each node, which carries the burden of this. To alleviate this, the present disclosure can determine whether to use planar mode based on the density at each octree level.

According to the embodiments, the density condition for determining whether to use the planar mode can be calculated as in the following mathematical expression 1. That is, the planar eligibility is determined for each tree depth based on the density as in mathematical expression 1.

[Mathematical Formula 1]

numPoints represents the number of points in the point cloud.

numPointsCodedByIdcm _i represents the number of IDCM nodes (or direct compression nodes) from the root to the i-th octree layer. That is, numPointsCodedByIdcm _i represents the number of points coded using IDCM from the root node layer to the i-th octree layer.

numSubnodes _i represents the number of occupied sub-nodes (i.e., the number of child nodes) generated in the i-th octree layer. That is, numSubnodes _i represents the number of occupied sub-nodes generated by nodes included in the i-th octree layer. And, realDensity _i+1 represents the real density of points for the i+1-th octree layer. Here, the octree layer can be referred to as a tree level or level.

According to embodiments, whether to use the planar mode in the i+1-th octree layer can be determined as follows. That is, before the coding process of the i+1-th octree layer, the eligibility of all nodes in the i+1-th octree layer for the xyz-planar mode is determined as follows.

planarEligibleKOctreeDepth(i+1) can be obtained and applied by the encoder and decoder, respectively. th is a predefined threshold, which can be set to 1.3, for example.

For example, if the density (realDensity _i+1 ) obtained by applying Equation 1 to the i+1-th octree layer is less than the threshold, planarEligibleKOctreeDepth _i+1 becomes 1, indicating that the planar mode is used in the i+1-th octree layer. Conversely, if the density (realDensity _i+1 ) is not less than the threshold, planarEligibleKOctreeDepth _i+1 becomes 0, indicating that the planar mode is not used in the i+1-th octree layer.

As mentioned above, when using layer group slicing, the mathematical expression 1 of realDensity for the m-th subgroup belonging to the n-th layer group above can be changed to the following mathematical expression 2.

[Equation 2]

numPoints(n,m) represents the final number of points included in the bounding box corresponding to the m-th subgroup belonging to the n-th layer group.

numPointsCodedByIdcm _i (n,m) represents the number of points included in the bounding box corresponding to the m-th subgroup belonging to the n-th layer group among the IDCM points from the root to the i-th octree layer.

numSubnodes _i (n,m) is It represents the number of occupied sub-nodes (= number of child nodes) generated in the i-th octree layer for the m-th sub-group belonging to the n-th layer group.

Based on this, the present disclosure determines whether to use the planar mode in the i+1-th octree layer for the m-th subgroup belonging to the n-th layer group as follows.

That is, the area is divided into subgroups due to layer group slicing, and the planar mode can be used adaptively according to the characteristics of the area through the above formula.

However, in the case of layer-group slicing, the octree depth can be divided into different slices, and in this case, it is difficult to directly apply the above method to the upper layer group. For example, in the case of the last layer group, numPoints can be estimated because the number of points generated in the subgroup included in the slice is transmitted through the header. However, since the number of points transmitted through the header means the number of points ultimately generated in each slice, it is difficult to estimate numPoints for the upper layer group. The embodiments may consider the following methods as a method to solve this problem.

1) How to pass the number of points finally generated at the leaf layer-group level:

For dependent slices of layer group slicing, the number of points finally generated at the leaf layer-group level can be passed from the encoder to the decoder.

2) How to use an arbitrary number of points:

If numPoints is unknown, any arbitrary number of points can be used equally in the encoder and decoder.

Here, numPoints(n, m) can mean the number of final points included in the bounding box corresponding to the m-th subgroup belonging to the n-th layer group. If there are no child subgroups (i.e., if it belongs to the last layer group), numSlicePoints passed through the header can be used. If there are child subgroups (if it belongs to the upper layer group), an arbitrary number of points can be agreed upon and used between the encoder and decoder.

For example, you can use a fixed value, such as the maximum number of points that can be included in each slice (MaxSlicePoint).

Another example is the use of predicted values. Equation 3 below illustrates an example where predictions are made using coefficients (e.g., coeff_a, coeff_b). That is, as shown in Equation 3 below, numPoints(n, m) can be estimated using a linear function.

[Equation 3]

estimated numPoints(n,m) = number of parent Subgroup nodes

Here, coeff_a + coeff_b represent the coefficients of the linear function. coeff_a and coeff_b can be predefined or passed directly to the decoder as needed.

That is, the number of final points included in the bounding box corresponding to the m-th subgroup belonging to the n-th layer group (numPoints(n,m)) can be obtained by adding the coefficients of the linear function, coeff_a and coeff_b, to the number of parent subgroup nodes.

3) How to pass planar EligibleKOctreeDepth directly

Since the encoder of the transmitting device knows the final number of points for each subgroup, it can calculate planarEligibleKOctreeDepth _i+1 (n, m) based on this and pass this value directly to the decoder of the receiving device. This reduces decoder complexity because the decoder does not need to calculate planarEligibleKOctreeDepth.

According to embodiments, the encoder of the transmitting device may transmit planar mode-related information related to the aforementioned planar mode to the decoder of the receiving device by signaling it in the dependent geometry data unit header.

Fig. 33 illustrates another example of the syntax structure of a dependent geometry data unit header according to embodiments. Fig. 33 illustrates an example in which a dependent geometry data unit header includes layer group-based slicing-related information and planar mode-related information.

The syntaxes of FIG. 33 are included in the bitstream of FIG. 29, and are generated by the point cloud encoder according to the embodiments, transmitted to the decoder of the receiving device, and can be decoded by the point cloud decoder.

Since the layer group-based slicing related information signaled in the dependent geometry data unit header of Fig. 33 is described in detail in Fig. 31, the description of Fig. 31 will be referred to, and will be omitted here to avoid redundant description. Fig. 33 describes information related to the planar mode.

In Figure 33, if checkPlanarEligibilityBasedOnOctreeDepth is true, it indicates that planar mode is used in the layer group, and in this case, the dependent geometry data unit header may include a planar mode eligibility type (planarEligibilityType).

The planar mode eligibility type (planarEligibilityType) can indicate how the planar mode is used. For example, a planarEligibilityType value of 0 indicates the traditional node-adaptive planar mode application method, a value of 1 indicates the method of passing the number of points finally generated at the leaf layer-group level, a value of 2 indicates the method of using an arbitrary number of points, and a value of 3 indicates the method of directly passing the planarEligibleKOctreeDepth.

That is, the dependent geometry data unit header contains the final number of points in the subgroup (numFinalOutputPointsInSubgroup) if the value of planarEligibilityType is 1, the estimate of the number of points in the slice (numSlicePointEstimationType) if it is 2, and the planar mode eligible octree depth flag (planarEligibleKOctreeDepthFlag) as many as the number of layers in the subgroup (numLayersInSubgroup) if it is 3.

The final number of points in a subgroup (numFinalOutputPointsInSubgroup) can indicate the number of final output points contained within the subgroup bounding box of the current slice.

The estimated number of points in a slice (numSlicePointEstimationType) can indicate how numSlicePoints (or numPoints) is estimated to obtain realDensity. For example, if the value of numSlicePointEstimationType is 0, it can indicate a predefined value (e.g., maxSlicePoint - the maximum number of points that can be included in a slice). If it is 1, it can indicate that it is estimated by a linear function. In this case, the coefficients of the linear function, coeff_a and coeff_b, can be defined in advance or passed directly as needed.

The number of layers in a subgroup (numLayersInSubgroup) can represent the number of octree depths contained in the current slice.

If the value of the planar mode eligible octree depth flag (planarEligibleKOctreeDepthFlag), which is repeated as many times as the value of numLayersInSubgroup, is 1, it can indicate that the planar mode is used for the ith octree depth among the octree depths included in the current slice. If it is 0, it can indicate that the planar mode is not used for the ith octree depth among the octree depths included in the current slice. The planar mode eligible octree depth flag (planarEligibleKOctreeDepthFlag) can correspond to planarEligibleKOctreeDepth_(i+1) (n,m) generated by directly passing planarEligibleKOctreeDepth.

The following mathematical expression 4 is another example for obtaining the density condition for determining whether to use the planar mode.

[Equation 4]

numPoints(s) represents the number of points in the points of subgroup s.

numPointsCodedByIdcm _i (s) represents the number of points that are coded using IDCM from the root node layer to the ith octree layer and within the subgroup boundary of the subgroup s.

numSubnodes _i (s) represents the number of occupied subnodes generated by nodes included in the i-th octree layer of subgroup s.

And, realDensity _i+1 (s) represents the real density of points for the i+1th octree layer of subgroup s.

According to embodiments, whether to use the planar mode in the i+1-th octree layer can be determined as follows. That is, before the coding process of the i+1-th octree layer, the eligibility of all nodes for the xyz-planar mode in the i+1-th octree layer of subgroup s is determined as follows.

Here, th is a predefined threshold, which can be set to, for example, 1.3.

At this time, the problem of using subgroup adaptive planar eligibility based on density is that the number of points in each subgroup is not known to the decoder until the subgroups belonging to the last layer group are delivered. The present disclosure can solve the above problem by signaling information that can indicate planar eligibility (e.g., planarEligibleKOctreeDepth _i+1 (s)) in the data unit header of each geometry data unit or the header of a dependent geometry data unit to match the encoder and decoder.

In the present disclosure, an IDCM node may be referred to as a direct compression node. That is, when the hierarchical structure for geometry decoding is in the form of a tree, some subgroups may include at least one IDCM node.

According to embodiments, the present disclosure defines a node satisfying the following conditions as an IDCM node, and a portion of a subgroup may include at least one IDCM node satisfying these conditions.

1) Parent-based eligibility condition: From the perspective of the parent node of the current node (point), there is only one occupied child, which is the current node, and from the perspective of the parent's parent (grand-parent), there is at most one occupied child (i.e., the parent's occupied sibling) (i.e., there are 2 occupied children).

2) 6N eligibility condition: From the perspective of the parent node, the current node is the only occupied child, and none of the six neighbors (nodes that are touching each other) are occupied.

In this disclosure, a node satisfying the above conditions is referred to as an IDCM node (or point), and a node not satisfying the above conditions is referred to as a non-IDCM node (or point). In each subgroup, one layer may include zero or one or more IDCM nodes.

The following describes another embodiment of the fine granularity slicing (FGS) related content described above. Some of the content may overlap with the FGS related content described above.

First, let's redefine some terms.

Fine granularity slices (FGS) are subsets of slices that carry the geometry or attributes of a subgroup.

A layer group is a group of consecutive tree levels of an occupancy tree or an attribute assigned occupancy tree.

A subgroup is a spatial subset of a layer-group where the bounding box of a subgroup shall not overlap with the other subgroups in the same layer-group.

A root layer group is a layer group that contains the root node of an occupancy tree or an attribute-assigned occupancy tree.

A parent subgroup is a subgroup in a layer-group adjacent to the top layer minimum depth of the current subgroup, where the bounding box of the parent subgroup is a superset of the bounding box of the current subgroup.

A child subgroup is a subgroup in a layer-group adjacent to the bottom layer maximum depth of the current subgroup, where the bounding box of the child subgroup is a subset of the bounding box of the current subgroup.

An attribute assigned occupancy tree is an occupancy tree where the attributes of a node in each tree level are assigned by the attributes of child nodes.

At this time, one slice can be composed of FGSs, and each FGS is mapped 1:1 to the FGS geometry or FGS attribute of a subgroup within the layer group.

FGS is indicated by a pair of layer group indices and subgroup indices.

FGSs within a slice are identified by a common slice identifier (slice_id).

Every FGS contains either a geometry data unit (GDU) or dependent geometry data unit (DGDU) that codes the FGS geometry, or an attribute data unit (ADU) or dependent attribute data unit (DADU) that codes the FGS attributes.

An FGS begins with a GDU. This FGS may be followed by FGSs of DGDUs, which depend on the GDU or previously decoded DGDU.

If present, the FGS attribute begins with an ADU. This FGS may be followed by FGSs of DADUs, which depend on the ADU or previously decoded DADUs. ADUs and DADUs occur after GDUs and DGDUs.

In the layer group structure of the present disclosure, nodes within the occupancy tree or the attribute-assigned occupancy tree are grouped into layer groups and subgroups.

A layer group is a group of consecutive tree levels, where each tree level belongs to exactly one layer group. The minimum depth of a subgroup is 1 plus the maximum depth of its parent subgroup, or 0 for the root layer group. The maximum depth of a subgroup is 1 minus the minimum depth of its child subgroups, or, for the last layer group, the maximum depth of the occupancy tree. Layer groups are identified by their layer group index (layer_group_idx).

A subgroup is a spatial subset of a layer group, and a node within a tree level belongs to one of the subgroups of the layer group.

The locations of the output nodes of a subgroup are described by a bounding box, and the range of the subgroup bounding box does not overlap with the bounding boxes of other subgroups within the same layer group.

The set of output nodes of subgroups within a layer group is identical to the set of output nodes within that layer group.

For the root layer group, there is only one subgroup. Subgroups within a layer group are identified by their subgroup index (subgroup_idx).

Figures 34(a) and 34(b) are diagrams showing the relationships between layer groups, subgroups, and FGSs according to embodiments. In particular, Figure 34(a) shows the layer group structure of an occupancy tree with a depth of 8, and Figure 34(b) shows the parent-child relationships between subgroups and the corresponding FGSs.

In Fig. 34(a), three layer groups are defined, where layer group 0 consists of depths 0-3, layer group 1 consists of depths 4-6, and layer group 2 consists of depths 7 and 8. That is, layer group 0 consists of one subgroup, layer group 1 consists of two subgroups, and layer group 2 consists of three subgroups. In other words, except for the root layer group, each layer group can consist of subgroups, and each subgroup is represented by a pair consisting of a layer group index and a subgroup index. For example, the root layer group is represented as (0, 0).

In Fig. 34(b), the subgroups within each layer group shown in Fig. 34(a) are depicted as spatial regions bounded in the xy plane. If the bounding box of a subgroup consists of one or more subgroups within the next layer group, they are in a parent-child relationship. That is, when the bounding box of a subgroup within a layer group is a superset of the bounding boxes of one or more subgroups within the next layer group, the subgroups within adjacent layer groups are in a parent-child relationship. For example, subgroup (0,0) is the parent of subgroups (1,0) and (1,1). Similarly, subgroups (2,0) and (2,1) are children of subgroup (1,0). Each subgroup, represented by a pair of layer group indices and subgroup indices, is in different FGSs.

In Figures 34(a) and 34(b), S _l,n represents a subgroup n associated with layer group l. And, d _i represents the octree tree depth i.

Figures 35a and 35b illustrate another example of the syntax structure of a sequence parameter set (SPS) according to embodiments. The SPS may include sequence information of a point cloud data bitstream, and in particular, an example of including information related to layer group-based slicing is shown.

The syntaxes of FIGS. 35a and 35b are included in the bitstream of FIG. 29, and can be generated by a point cloud encoder according to embodiments and decoded by a point cloud decoder.

The following describes information related to layer group-based slicing. For descriptions of information other than layer group-based slicing, refer to the descriptions of FIGS. 30a and 30b.

For example, in FIGS. 35a and 35b, simple_profile_compliant corresponds to simple_profile_compatibility_flag, dense_profile_compliant corresponds to dense_profile_compatibility_flag, and predictive_profile_compliant corresponds to predictive_profile_compatibility_flag in FIGS. 30a and 30b. main_profile_compliant corresponds to main_profile_compatibility_flag, slice_reordering_constraint corresponds to slice_reordering_constraint_flag, and unique_point_positions_constraint corresponds to unique_point_positions_constraint_flag. sps_seq_parameter_set_id corresponds to sps_seq_parameter_set_id, seq_origin_bits corresponds to seq_origin_bits, seq_origin_xyz[] and seq_origin_log2_scale correspond to seq_origin_xyz[] and seq_origin_log2_scale. seq_bbox_size_bits corresponds to seq_bounding_box_size_bits, and seq_bbox_size_minus1_xyz[] corresponds to seq_bounding_box_size_minus1_xyz[k].

In Figures 35a and 35b, if the value of sps_layer_group_slicing_flag is 1, it specifies that the slice is composed of multiple FGSs of FGS geometries or FGS attributes. If the value of sps_layer_group_slicing_flag is 0, it specifies that the slice is not composed of FGSs.

The value of num_layer_groups_minus1 plus 1 represents the number of layer groups, where a layer group represents a group of contiguous tree layers that are part of the geometry coding tree structure. num_layer_groups_minus1 must be in the range of 0 to the number of coding tree layers.

layer_group_id[i] specifies the layer group index, i.e., the indicator, that identifies the i-th layer group in the slice. layer_group_id must be in the range of 0 to num_layer_groups_minus1.

num_layers_minus1[i] + 1 represents the number of coding layers (or levels) included in the ith layer group. The total number of layer groups can be derived by adding all (num_layers_minus1[i] + 1) to num_layer_groups_minus1 when i is 0.

If the value of subgroup_enabled_flag[i] is 1, it indicates that the ith layer group is divided into two or more subgroups. Here, the set of points included in the subgroups of the layer group is identical to the set of points included in the layer group. When subgroup_enabled_flag of the ith layer group is 1, subgroup_enabled_flag of the jth layer group is equal to 1 when j is greater than or equal to i. If the value of subgroup_enabled_flag is 0, it indicates that the current layer group is not subdivided into multiple subgroups but is included in a single slice (subgroup_enabled_flag[i] is equal to 1 specifies the i-th layer-group is divided into two or more subgroups where the set of points in the subgroups of a layer-group is identical to the set of points in the layer-group. When subgroup_enabled_flag of the i-th layer-group is equal to 1, subgroup_enabled_flag of the j-th layer-group shall be equal to 1 when j is larger than or equal to i. subgroup_enabled_flag is equal to 0 specifies that the i-th layer-group is not sub-divided into multiple subgroups).

subgroup_bbox_origin_bits_minus1+1 is the bit length of the subgroup_bbox_origin field (or syntax element).

subgroup_bbox_size_bits_minus1+1 is the bit length of the subgroup_bbox_size field.

Fig. 36 is a diagram illustrating an example syntax structure of a geometry data unit header according to embodiments. The syntaxes of Fig. 36 are included in the bitstream of Fig. 29, and may be generated by a point cloud encoder according to embodiments, transmitted to a decoder as a receiving device, and decoded by the point cloud decoder.

In Figure 36, gdu_geometry_parameter_set_id specifies the GPS activated via gps_geom_parameter_set_id.

gdu_temporal_id specifies the temporal ID of the frame associated with the geometry data unit (GDU).

slice_id identifies the slice for reference by other DUs.

slice_tag identifies a slice as a member of a slice group with the same slice_tag value. If a tile inventory DU exists, the slice group is a tile identified by its tile id. If a tile inventory DU does not exist, the interpretation of slice_tag depends on the application.

frame_ctr_lsb specifies the frame_ctr_lsb_bits LSBs of FrameCtr, a conceptual frame counter.

If slice_entropy_continuation is 1, it specifies that the entropy parsing restoration process should be applied at the beginning of each GDU and all ADUs within the slice. If slice_entropy_continuation is 0, it specifies that parsing of each GDU and all ADUs within the slice is independent of other slices within the frame when slice_inter_entropy_continuation is 0. If slice_entropy_continuation is not present, it is assumed to be 0.

prev_slice_id must be identical to the GDU slice_id of the preceding slice in bitstream order.

The following explains information related to layer group-based slicing.

If sps_layer_group_slicing_flag included in the sequence parameter set has a value of true (i.e., 1), the geometry data unit header may contain num_subsequent_subgroups. A value of sps_layer_group_slicing_flag of 1 indicates that a slice consists of multiple FGSs of FGS geometries or FGS attributes.

num_subsequent_subgroups specifies the number of subsequent dependent data units that reference the current data unit or dependent data units.

If occtree_planar_enabled in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), the geometry data unit header can contain planar_eligibility_by_density[i] as many times as the value of num_layers_minus1[0].

occtree_planar_enabled specifies whether (when 1) or not (when 0) the coding of node occupancy bitmaps is performed, in part, by the signaling of occupied and unoccupied planes. When occtree_planar_enabled is not present, it shall be inferred to be 0.

geom_angular_enabled specifies whether (when 1) or not (when 0) slice geometry is coded using information about a set of beams located along and rotating around the V axis of the angular origin.

If planar_eligibility_by_density[i] is 1, it indicates that the planar eligibility flag is enabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 1, it indicates that planar eligibility is enabled for the ith depth of the current subgroup. If planar_eligibility_by_density is 0, it indicates that the planar eligibility flag is disabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 0, it indicates that planar eligibility is disabled for the ith depth of the current subgroup.

Fig. 37 is a diagram illustrating another example of the syntax structure of a dependent geometry data unit header according to embodiments. Fig. 37 shows an example in which layer group-based slicing-related information is included in the dependent geometry data unit header.

The syntaxes of FIG. 37 are included in the bitstream of FIG. 29, and can be generated by a point cloud encoder according to embodiments, transmitted to a decoder as a receiving device, and decoded by the point cloud decoder.

dgsh_geometry_parameter_set_id indicates the active GPS indicated by gps_geom_parameter_set_id. The dgsi_geometry_parameter_set_id value is equal to the gdu_geometry_parameter_set_id value for the corresponding slice.

slice_id specifies the geometry slice to which the current dependent geometry data unit belongs.

layer_group_id is an indicator of the layer group of the slice, i.e., a layer group index that identifies the layer group. layer_group_id can range from 0 to num_layer_groups_minus1. If it does not exist, it is assumed to be 0.

subgroup_id indicates a subgroup of the layer group referenced by layer_group_id, i.e., a subgroup index used to identify the subgroup. subgroup_id can range from 0 to num_subgroups_minus1[layer_group_id]. Here, subgroup_id indicates a subarea within the layer group identified by layer_group_id. If not present, subgroup_id is assumed to be 0.

subgroup_bbox_origin represents the minimum position of the subgroup bounding box of the i-th subgroup indicated by subgroup_id in the j-th layer group indicated by layer_group_id.

subgroup_bbox_size represents the size of the subgroup bounding box of the i-th subgroup indicated by subgroup_id in the j-th layer group indicated by layer_group_id.

According to the embodiments, the bounding box of points in a subgroup is described by subgroup_bbox_origin and subgroup_bbox_size. In this case, the area in the bounding box of the i-th subgroup does not overlap with the bounding box of the j-th subgroup when i and j are not equal.

ref_layer_group_id indicates the layer group identifier of the context reference of the current dependent data unit. ref_layer_group_id ranges from 0 to the layer_group_id of the current dependent data unit.

ref_subgroup_id indicates a reference subgroup of the layer group pointed to by ref_layer_group_id. ref_subgroup_id ranges from 0 to num_subgroup_id_minus1 of the layer group pointed to by ref_layer_group_id.

If context_reference_indication_flag is 1, it indicates that the context state of the current dependent data unit is inherited by one or more subsequent dependent data units. If context_reference_indication_flag is 0, it indicates that the context state of the current dependent slice (i.e., data unit) is not inherited by subsequent dependent slices (i.e., data units). If the flag does not exist, the context_reference_indication_flag of the data unit is considered to be 1.

Decoders of a receiving device can manage a context buffer using context_reference_indication_flag. If context_reference_indication_flag is 1, the context state of the current dependent slice (i.e., data unit) is stored in the context buffer at the end of decoding of the current data unit. If context_reference_indication_flag is 0, the context state of the current dependent slice (i.e., data unit) is not stored in the context buffer. Here, the decoders can be one of FIG. 1, FIG. 2, FIG. 7, FIG. 9, or FIG. 28.

If context_reference_indication_flag is true (i.e., 1), the dependent geometry data unit header may contain num_subsequent_subgroups.

num_subsequent_subgroups specifies the number of subsequent dependent data units that reference the current data unit or dependent data units.

If occtree_planar_enabled in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), then the dependent geometry data unit headers can contain planar_eligibility_by_density[i] as many times as the value of num_layers_minus1[layer_group_id].

If planar_eligibility_by_density[i] is 1, it indicates that the planar eligibility flag is enabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 1, it indicates that planar eligibility is enabled for the ith depth of the current subgroup. If planar_eligibility_by_density is 0, it indicates that the planar eligibility flag is disabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 0, it indicates that planar eligibility is disabled for the ith depth of the current subgroup.

A bitstream according to embodiments may include at least one slice, or subdivided slices, and may include a geometry data unit and an attribute data unit. Each data unit includes a header (e.g., a geometry data unit header, a geometry data unit header, an attribute data unit header, or a dependent attribute data unit header). In addition, the data unit includes independent data units and dependent data units. The dependent data units may indicate an inclusion relationship according to a dependency relationship between upper-lower nodes.

The decoding process performed in one of FIG. 1, FIG. 2, FIG. 7, FIG. 9, or FIG. 28 is as follows.

The following describes the slice decoding process.

According to embodiments, a slice within a point cloud frame encoded by an encoder of a transmitting device is decoded as follows:

1) Point locations are decoded from one GDU within a slice. If sps_layer_group_slicing_flag is 1, point locations are decoded from one GDU and zero or more DGDUs.

2) Default attribute values are set for each attribute.

3) Point attributes are decoded from each ADU within a slice. If sps_layer_group_slicing_flag is 1, point attributes are decoded from one ADU and zero or more DADUs. ADUs and DADUs are decoded after the GDUs and DGDUs indicated by the same subgroup index pair (layer_group_id and subgroup_id) are decoded.

4) The decoded point locations are offset and the output point count is incremented.

Additionally, for every set of slices within a coded point cloud frame with the same slice_id value, only one slice is decoded.

The following describes the process of decoding fien granularity slices.

A GDU is decoded before all DGDUs within the slice. DGDUs in child subgroups are decoded after the DGDUs in the parent subgroup are decoded.

When decoding GDU, startDepth and endDepth are set to 0 and num_layers_minus1[0] + 1, respectively.

When decoding a DGDU, startDepth is set to the cumulative value of num_layers_minus1[k] + 1 for each k in the range of 0 to (layer_group_id - 1), and endDepth is set to the cumulative value of num_layers_minus1[k] + 1 for each k in the range of 0 to layer_group_id.

And, the occupancy tree represents the slice geometry as occupancy tree nodes. Parsing or traversing the encoded occupancy tree implicitly generates a representation of the slice geometry.

If sps_layer_group_slicing_flag is 1, the partial occupancy tree represents fine-grained slice geometry in both spatial and depth dimensions as a subtree of occupancy tree nodes. Parsing or traversing an encoded partial occupancy tree implicitly generates a fine-grained slice geometry representation.

Individual point locations are represented by direct nodes that encode the positions of leaf nodes or node-relative positions in the occupancy tree.

The occupancy tree node identifies the presence of at least one point contained within the volume of an axis-aligned cuboid. If sps_layer_group_slicing_flag is 1, the occupancy tree node identifies the presence of at least one point contained within the volume of the cube. The volume is defined in the slice coordinate system by the inclusive lower corner p_min and the exclusive upper corner p_max.

The following describes planar occupancy coding.

In particular, the conditions for per-axis eligibility are as follows: planar occupancy coding eligibility can be determined on an axis-by-axis basis.

In the following mathematical expression 5, PointDensity[dpth] is a factor that identifies the density of points at a tree level of depth (dpth). In other words, mathematical expression 5 represents a method for calculating the density of points at a specific depth of the occupancy tree.

[Equation 5]

PointDensity[dpth] := (slice_num_points_minus1 + 1 - DirectNodePointCnt) × 10 / OccNodeCnt[dpth]

According to mathematical expression 5, PointDensity[dpth] can be obtained by subtracting 'DirectNodePointCnt' from 'slice_num_points_minus1 + 1', multiplying the result by the constant 10, and then dividing it by OccNodeCnt[dpth].

In the above mathematical expression 5, slice_num_points_minus1+1 specifies the number of points coded in the corresponding DU. slice_num_points_minus1 can be included in the Geometry Data Unit footer semantics of the Geometry Data Unit. According to the bitstream conformance requirement, slice_num_points_minus1 + 1 is equal to the number of decodable points in the corresponding DU. If sps_layer_group_slicing_flag is 1 and layer_group_id is greater than 0, slice_num_points_minus1+1 is equal to the number of nodes existing at the maximum depth of the tree levels in the corresponding DGDU.

DirectNodePointCnt represents the cumulative number of points coded in direct nodes. That is, the number of points coded in direct nodes is accumulated and counted as DirectNodePointCnt. This variable is initialized to 0 at the beginning of each slice or fine-grained slice. DirectNodePointCnt can be used to calculate the number of points remaining after directly coded points at that depth.

OccNodeCnt[dpth] represents the cumulative count (i.e., the cumulative number) of nodes existing at depth (dpth). If layer_group_slicing_flag is 1, OccNodeCnt[dpth] represents the cumulative count (i.e., the cumulative number) of nodes existing at depth (dpth) of the corresponding subgroup. OccNodeCnt[dpth] is used as the denominator in Equation 5 to normalize the density of points represented by each node.

The constant 10 can be used to normalize the point density or convert it to a specific scale.

According to embodiments, if sps_layer_group_slicing_flag (or fgs_layer_group_enabled) is 1, PointDensity[dpth] can be specified by planar_eligibility_by_density in the geometry data unit header (GDUH) or dependent geometry data unit header (DGDUH) as in Equation 6 below. If the value of sps_layer_group_slicing_flag included in the SPS is 1, it means that the slice is composed of multiple FGSs of FGS geometry or FGS attributes.

[Equation 6]

PointDensity[dpth] = planar_eligibility_by_density[dpth - startDepth]

That is, when a slice is divided into fine granularity slices, the planar_eligibility_by_density value signaled and transmitted in the geometry data unit header (GDUH) or dependent geometry data unit header (DGDUH) can be used to determine planar eligibility at each depth.

In Equation 6, [dpth - startDepth] represents the relative depth within the slice. By aligning the index based on the start depth (startDepth) of the slice, the planar eligibility value for the depth can be obtained.

In embodiments, if the value of sps_layer_group_slicing_flag is 1, it specifies that the slice is composed of multiple FGSs of FGS geometries or FGS attributes. If the value of sps_layer_group_slicing_flag is 0, it specifies that the slice is not composed of FGSs. As another example, if the value of sps_layer_group_slicing_flag is 1, it can specify that the slice is composed of multiple FGSs of partial slice geometries or partial slice attributes.

According to embodiments, if occtree_planar_enabled included in the geometry parameter set is true (i.e., 1) and geom_angular_enabled is false (i.e., 0), the geometry data unit header (GDUH) or dependent geometry data unit header (DGDUH) may include planar_eligibility_by_density as many times as the value of num_layers_minus1.

occtree_planar_enabled specifies whether coding of node occupancy bitmaps is performed (if 1) or not (if 0), through signaling of partially occupied and unoccupied planes.

geom_angular_enabled specifies whether the slice geometry is coded using information about a set of beams positioned and rotating along the V-axis of the angular origin (if 1), or not (if 0).

If planar_eligibility_by_density is 1, it indicates that the planar eligibility flag is enabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 1, it indicates that planar eligibility is enabled for the ith depth of the current subgroup. If planar_eligibility_by_density is 0, it indicates that the planar eligibility flag is disabled for the ith depth of the current subgroup. That is, if planar_eligibility_by_density[i] is 01, it indicates that planar eligibility is disabled for the ith depth of the current subgroup.

In this way, in the present disclosure, PointDensity[dpth], i.e., point density at each depth, can be obtained through direct calculation as in Equation 5 in the decoder of the receiving device, or can be obtained using planar_eligibility_by_density as in Equation 6.

For example, if the density of points (PointDensity[dpth]) at a specific depth (or layer or level) of the occupancy tree obtained through Equation 5 or Equation 6 is less than a threshold, it indicates that the planar mode is used at that depth, and if it is not less than the threshold, it indicates that the planar mode is not used at that depth. Here, the threshold can be predefined.

In some embodiments, the existing formula for calculating PointDensity[dpth] can be used as is to reduce the bits used due to planar_eligibility_by_density propagation. In this case, slice_num_points_minus1 may refer to the number of nodes or points generated when decoding FGS, rather than the number of points in the entire slice.

The following describes partial decoding of fine granularity slices.

The decoder can decode and reconstruct portions of FGSs. Segmented slices of FGSs support partial decoding in terms of density and/or spatial domain. When using partial decoding, unnecessary data units are filtered out before decoding the occupancy tree or attribute coefficients to generate partial outputs.

The following describes partial density decoding.

The decoder generates a low-density slice point cloud. The low-density slice point cloud is specified by the following variables:

The variable SkippedLayerGroup represents the application-specific number of layer groups skipped for partial decoding in the density direction. The value of SkippedLayerGroup must be in the range 0 to num_layer_groups_minus1.

The variable MinNodeSizeLog2 represents the minimum occupancy tree node size specified by SkippedLayerGroup.

The arrays SubgroupNodePos[layerGroupIdx][subgroupIdx][ptIdx][k] represent the output nodes of the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

The arrays SubgroupNodeCnt[layerGroupIdx][subgroupIdx] represent the number of output nodes in the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

The following describes the selection of FGS in partial density decoding.

If SkippedLayerGroup is greater than 0, layer groups whose indices are in the range from 0 to OutLayerGroup are selected for decoding. OutLayerGroup, which is the maximum value of the layer group index for partial decoding, is specified as the value obtained by subtracting SkippedLayerGroup from the total number of layer groups (num_layer_groups_minus1), as shown in the following mathematical expression 7.

[Equation 7]

OutLayerGroup := num_layer_groups_minus1 - SkippedLayerGroup

The code below indicates whether to decode or skip a data unit or dependent data unit.

For example, if layer_group_id == 0, decode the geometry data unit (GDU) or attribute data unit (ADU). If layer_group_id ≤ OutLayerGroup, decode the dependent geometry data unit (DGDU) or dependent attribute data unit (DADU). Otherwise, skip the dependent geometry data unit (DGDU) or dependent attribute data unit (DADU).

if (layer_group_id == 0)

decode GDU or ADU

else if (layer_group_id ≤ OutLayerGroup)

decode DGDU or DADU

else

skip DGDU or DADU

Therefore, the depth of the geometry occupancy tree of partial decoding (PartialDepth) is inferred as the sum of the number of layers (or levels) of each layer group whose index ranges from 0 to OutLayerGroup (num_layers_minus1[i] + 1), as in the code below.

PartialDepth = 0

for (i = 0; i < NumOutLayerGroup; i++)

PartialDepth += num_layers_minus1[i] + 1

The following describes geometry position correction.

When decoding all layer groups, the maximum depth (TotalDepth) of the geometry occupancy tree is inferred as the sum of the number of layers (or levels) in each layer group whose indices are in the range of 0 to num_layer_groups_minus1 (num_layers_minus1[i] + 1), as in the code below.

TotalDepth = 0

for (i = 0; i < num_layer_groups_minus1; i++)

TotalDepth += num_layers_minus1[i] + 1

And, MinNodeSizeLog2 is inferred from the difference between TotalDepth and PartialDepth as follows.

MinNodeSizeLog2 = TotalDepth - PartialDepth

If MinNodeSizeLog2 is greater than 1, the points are centered within the block, as shown in the code below. That is, the code below adjusts the coordinate values to center each point within the block when the MinNodeSizeLog2 value is greater than 1. In the code below, the first for loop iterates over all points (or nodes) belonging to the subgroup, and the second for loop iterates over each axis (x, y, z) of each point.

for (ptIdx = 0; ptIdx < SubgroupNodeCnt[layerGroupIdx][subgroupIdx]; ptIdx++)

for (k = 0; k < 3; k++)

SubgroupNode[layerGroupIdx][subgroupIdx][ptIdx][k] |= (MinNodeSizeLog2 > 1) << (MinNodeSizeLog2 - 1)

The following describes partial region decoding.

The decoder generates a point cloud of a partial region of a slice. The low-density slice point cloud is specified by the following variables.

The arrays RoiBoundingBoxMin and RoiBoundingBoxMax are application-specific arrays that specify the minimum and maximum positions of the bounding box for the Region of Interest (ROI).

The arrays SubgroupNodePos[layerGroupIdx][subgroupIdx] represent the output nodes of the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

The arrays SubgroupNodeCnt[layerGroupIdx][subgroupIdx] represent the number of output nodes in the subgroup identified by the layer group index (layerGroupIdx or layer_group_id) and the subgroup index (subgroupIdx or subgroup_id).

The following explains the choice of FGS in partial region decoding.

If RoiBoundingBoxMin and RoiBoundingBoxMax exist, subgroups whose subgroup bounding boxes overlap with the bounding boxes of the ROI are selected for decoding.

The code below indicates whether to decode or skip a data unit or dependent data unit.

For example, if layer_group_id == 0, decode a geometry data unit (GDU) or attribute data unit (ADU). If RoiBoundingBoxMin < SubgroupBoundingBoxMax[layerGroupIdx][subgroupIdx] && RoiBoundingBoxMax < SubgroupBoundingBoxMin[layerGroupIdx][subgroupIdx], decode a dependent geometry data unit (DGDU) or dependent attribute data unit (DADU). Otherwise, skip the dependent geometry data unit (DGDU) or dependent attribute data unit (DADU).

if (layerGroupIdx == 0)

decode GDU or ADU

else if (RoiBoundingBoxMin < SubgroupBoundingBoxMax[layerGroupIdx][subgroupIdx] &&

RoiBoundingBoxMax < SubgroupBoundingBoxMin[layerGroupIdx][subgroupIdx])

decode DGDU or DADU

else

skip DGDU or DADU

The present disclosure proposes a method of using a region-adaptive planar mode based on subgroups/slices as described above. That is, the present disclosure has described in detail above a method of calculating planarEligibleKOctreeDepth at a transmitter/receiver by transmitting the number of points (numPoints) in units of subgroups, a method of calculating planarEligibleKOctreeDepth at a transmitter/receiver by predicting the number of points (numPoints), a method of calculating a planarEligibleKOctreeDepth flag at a transmitter and then transmitting it to a receiver, and a method of calculating planarEligibleKOctreeDepth based on the number of output nodes/points in units of subgroups. In addition, the present disclosure has described in detail above a partial decoding process and a pre-/post-processing process of FGS decoding.

Therefore, the receiving method/device has the following effects.

The present disclosure describes a method for dividing and transmitting compressed data based on certain criteria for point cloud data. In particular, as one application of the present disclosure, layered coding can be used to divide and transmit compressed data according to layers, thereby increasing the storage and transmission efficiency of the transmitter.

Fig. 38 is a diagram showing an example of compressing and providing a service for the geometry and attributes of point cloud data. That is, in a point cloud compression (PCC)-based service, the compression ratio or the number of data can be adjusted and transmitted depending on the receiver performance or transmission environment. However, in the case where point cloud data is bundled into a single slice unit as in Fig. 38, if the receiver performance or transmission environment changes, 1) a bitstream suitable for each environment is converted in advance and stored separately and selected when transmitting, or 2) a process of converting (transcoding) is required before transmission. In this case, if the number of receiver environments to be supported increases or the transmission environment changes frequently, storage space issues or delays due to conversion may become a problem.

FIG. 39 is a diagram showing another example of compressing and serving the geometry and attributes of point cloud data according to embodiments.

As proposed in this disclosure, when compressed data is divided and transmitted according to layers, there is an advantage in that only the necessary portions of pre-compressed data can be selectively transmitted through a bitstream selector at the bitstream stage without a separate conversion process. This is efficient in terms of storage space, as only one storage space is required per stream, and efficient transmission is also possible in terms of bandwidth, as only the necessary layers are selectively transmitted through the bitstream selector before transmission.

If we describe the effects according to the features of the present disclosure from the perspective of a receiver, in one application of the present disclosure, when layered coding is used, compressed data can be divided and transmitted according to layers, in which case the efficiency of the receiver increases. In particular, when scalable attribute coding is applied, there is a disadvantage in that a delay occurs and a burden is placed on the receiver's computation by receiving and decoding the entire coded geometry data. However, through the proposal of the present disclosure, by decoding only the geometry layer that matches the tree level used in scalable attribute coding, the delay element is reduced, and the efficiency of the decoder can be increased by saving the computing power required for decoding.

Figure 40 is a diagram illustrating the operation of the transmitting and receiving ends when transmitting point cloud data composed of layers. In this case, if information capable of restoring the entire point cloud data is transmitted regardless of the performance of the receiver, the receiver requires a process (e.g., data selection or subsampling) to restore the point cloud data through decoding and then select only the point cloud data corresponding to the required layer. In this case, since the transmitted bitstream has already been decoded, a receiver targeting low delay may experience delay or may not be able to decode depending on the receiver's performance.

However, as proposed, if only the compressed data of the required layer is received according to the layer, the receiver can selectively decode a specific layer, thereby increasing decoder efficiency and supporting decoders of various performances.

Figure 41 shows a flowchart of a point cloud data transmission method according to embodiments.

A method for transmitting point cloud data according to embodiments may include a step of acquiring point cloud data (71001), a step of encoding point cloud data (71002), and a step of transmitting encoded point cloud data and signaling information (71003). At this time, a bitstream including the encoded point cloud data and signaling information may be encapsulated into a file and transmitted.

The step (71001) of acquiring point cloud data may perform part or all of the operations of the point cloud video acquisition unit (10001) of FIG. 1 or may perform part or all of the operations of the data input unit (8000) of FIG. 8.

The step of encoding point cloud data (71002) may perform part or all of the operations of the point cloud video encoder (10002) of FIG. 1, the encoding (20001) of FIG. 2, the point cloud video encoder of FIG. 3, the point cloud video encoder of FIG. 8, the encoder of FIG. 24, the attribute encoding of FIG. 25, and the geometry encoder and attribute encoder of FIG. 27 for encoding geometry information and attribute information.

The step (71002) of encoding point cloud data according to embodiments may include a step of compressing geometry information of input point cloud data and a step of compressing attribute information.

According to embodiments, the step of compressing geometry information may compress geometry information by layer group and/or subgroup. In addition, a slice for geometry may be compressed by being divided into one or more FGSs. In this case, the subgroups may correspond to FGSs. In addition, whether to use planar mode at a specific depth (or layer or level) of the occupancy tree may be determined based on the density of points at that depth. Since the compression of geometry information and FGS division have been described in detail above, a detailed description thereof will be omitted here to avoid redundant explanation.

The above compressed geometry information is entropy encoded and then output in the form of a geometry bitstream.

According to embodiments, the step of compressing attribute information compresses the attribute information based on positions for which geometry encoding has not been performed and/or reconstructed geometry information. In one embodiment, the attribute information may be coded using one or a combination of one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding. In addition, a slice for an attribute may be compressed by being divided into one or more FGSs. In this case, a subgroup may correspond to an FGS.

The above compressed attribute information is entropy encoded and then output in the form of an attribute bitstream.

In this specification, signaling information may include layer group-based slicing-related information for encoding of geometry information and attribute information at the layer group and/or subgroup level and decoding at the receiving end.

The above layer group-based slicing related information may be transmitted to the receiver by being included in at least one of SPS, APS, LGSI, geometry data unit header (or dependent geometry data unit header), and/or attribute data unit header (or dependent attribute data unit header). Detailed information included in the layer group-based slicing related information is omitted here, with reference to FIGS. 29 to 33 and FIGS. 34 to 37.

Figure 42 shows a flowchart of a method for receiving point cloud data according to embodiments.

A method for receiving point cloud data according to embodiments may include a step (81001) of receiving encoded point cloud data and signaling information, a step (81002) of decoding point cloud data based on the signaling information, and a step (81003) of rendering the decoded point cloud data.

The step (81001) of receiving point cloud data and signaling information according to the embodiments may be performed in the receiver (10005) of FIG. 1, the transmitter (20002) or decoding (20003) of FIG. 2, and the receiving unit (9000) or receiving processing unit (9001) of FIG. 9.

The step (81002) of decoding point cloud data according to embodiments may perform part or all of the operations of the point cloud video decoder (10006) of FIG. 1, the decoding (20003) of FIG. 2, the point cloud video decoder of FIG. 8, the point cloud video decoder of FIG. 9, the decoder of FIG. 24, the decoding of FIG. 26, and the geometry decoder and attribute decoder of FIG. 28 for decoding geometry information and attribute information.

The step (81002) of decoding point cloud data according to the embodiments includes a step of decoding geometry information and a step of decoding attribute information.

The step of decoding the above geometry information can decode (i.e., restore) the geometry information in units of layer groups and/or subgroups based on layer group-based slicing-related information included in the signaling information. In addition, when a slice for geometry is encoded by being divided into one or more FGSs at the transmitting side, the step of decoding the geometry information can decode (i.e., restore) the geometry in units of FGSs. At this time, a subgroup can correspond to an FGS. In addition, whether to use the planar mode at a specific depth (or layer or level) of the occupancy tree can be determined based on the density of points at the corresponding depth. Since a detailed description of the decoding of the geometry information has been sufficiently provided above, it will be omitted here to avoid redundant description.

The step of decoding the attribute information decodes (i.e., decompresses) the attribute information in units of layer groups and/or subgroups based on the layer group-based slicing-related information included in the restored geometry information and signaling information. In one embodiment, the attribute information may be decoded by using one or more of RAHT coding, LOD-based predictive transform coding, and lifting transform coding, or a combination of one or more. In addition, when a slice for an attribute is encoded by being divided into one or more FGSs at the transmitting side, the step of decoding the attribute information may decode (i.e., restore) the attribute in units of FGSs. In this case, a subgroup may correspond to an FGS. Since a detailed description of the decoding of the attribute information has been sufficiently described above, a detailed description will be omitted here to avoid redundant description.

The rendering step (81003) according to the embodiments may restore point cloud data based on restored (or reconstructed) geometry information and attribute information and render it according to various rendering methods. For example, points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a certain minimum size centered around the vertex position, or circles centered around the vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., VR/AR display, general display, etc.). The rendering step (81003) of the point cloud data according to the embodiments may be performed in the renderer (10007) of FIG. 1, the renderer (20004) of FIG. 2, or the renderer (9011) of FIG. 9.

Each of the parts, modules, or units described above may be software, processors, or hardware parts that execute sequential execution processes stored in memory (or storage units). Each of the steps described in the embodiments described above may be performed by processors, software, or hardware parts. Each of the modules/blocks/units described in the embodiments described above may operate as a processor, software, or hardware. In addition, the methods presented in the embodiments may be implemented as code. This code may be written on a processor-readable storage medium and thus may be read by a processor provided by an apparatus.

Furthermore, throughout the specification, when a part is said to "include" a component, this does not exclude other components, unless otherwise specifically stated, but rather implies the inclusion of other components. Furthermore, terms such as "part" described in the specification mean a unit that processes at least one function or operation, which may be implemented using hardware, software, or a combination of hardware and software.

For convenience of explanation, this specification has been described separately in each drawing. However, it is also possible to design new embodiments by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by those skilled in the art, is also within the scope of the embodiments.

The devices and methods according to the embodiments are not limited to the configurations and methods of the embodiments described above, but the embodiments may be configured by selectively combining all or part of each embodiment so that various modifications can be made.

Although preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and various modifications may be made by those skilled in the art to which the invention pertains without departing from the spirit or scope of the embodiments claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the embodiments.

The various components of the devices of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. The various components of the embodiments may be implemented by a single chip, for example, a single hardware circuit. The components according to the embodiments may be implemented by separate chips. At least one of the components of the devices of the embodiments may be configured with one or more processors capable of executing one or more programs, and the one or more programs may perform, or include instructions for performing, one or more of the operations/methods according to the embodiments. The executable instructions for performing the methods/operations of the devices of the embodiments may be stored in non-transitory CRMs or other computer program products configured to be executed by one or more processors, or may be stored in temporary CRMs or other computer program products configured to be executed by one or more processors. In addition, the memory according to the embodiments may be used as a concept including not only volatile memory (e.g., RAM, etc.), but also non-volatile memory, flash memory, PROM, etc. Additionally, it may include implementations in the form of carrier waves, such as transmissions via the Internet. Furthermore, processor-readable recording media may be distributed across network-connected computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

In this document, "/" and "," are interpreted as "and/or". For example, "A/B" is interpreted as "A and/or B", and "A, B" is interpreted as "A and/or B". Additionally, "A/B/C" means "at least one of A, B, and/or C". Also, "A, B, C" means "at least one of A, B, and/or C". Additionally, "or" in this document is interpreted as "and/or". For example, "A or B" can mean 1) "A" only, 2) "B" only, or 3) "A and B". In other words, "or" in this document can mean "additionally or alternatively".

Various elements of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various elements of the embodiments may be implemented on a single chip, such as a hardware circuit. In some embodiments, the embodiments may optionally be implemented on separate chips. In some embodiments, at least one of the elements of the embodiments may be implemented within one or more processors that include instructions for performing operations according to the embodiments.

Additionally, the operations according to the embodiments described in this document may be performed by a transceiver device including one or more memories and/or one or more processors according to the embodiments. One or more memories may store programs for processing/controlling the operations according to the embodiments, and one or more processors may control various operations described in this document. One or more processors may be referred to as a controller, etc. The operations according to the embodiments may be performed by firmware, software, and/or a combination thereof, and the firmware, software, and/or a combination thereof may be stored in a processor or a memory.

Terms such as "first," "second," etc. may be used to describe various components of the embodiments. However, the various components according to the embodiments should not be interpreted as limited by these terms. These terms are merely used to distinguish one component from another. For example, a first user input signal may be referred to as a second user input signal. Similarly, a second user input signal may be referred to as a first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although a first user input signal and a second user input signal are both user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.

The terminology used to describe the embodiments is for the purpose of describing particular embodiments and is not intended to be limiting of the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless the context clearly dictates otherwise. The expressions “and/or” are used to mean all possible combinations of the terms. The expression “comprises” or “includes” describes the presence of features, numbers, steps, elements, and/or components, but does not mean that additional features, numbers, steps, elements, and/or components are not included. Conditional expressions such as “if” or “when” used to describe the embodiments are not intended to be limited to only optional cases. When a specific condition is satisfied, a related action is performed in response to a specific condition, or a related definition is intended to be interpreted.

As described above, the relevant contents have been described in the best form for carrying out the embodiments.

As described above, the embodiments may be applied, in whole or in part, to point cloud data transmission and reception devices and systems. Those skilled in the art may variously modify or alter the embodiments within the scope of the embodiments. The embodiments may include modifications and variations, and such modifications and variations do not depart from the scope of the claims and their equivalents.

Claims (16)

A step of receiving geometry data, attribute data, and signaling information; a step of decoding the above geometry data; and A decoding method comprising a step of decoding the above attribute data. In the first paragraph, the step of decoding the geometry data comprises: A step of generating a geometry tree consisting of multiple depths; and A step of decoding the geometry data based on the geometry tree is included, The above decoding step is a decoding method that determines planar eligibility for each depth of the geometry tree and decodes geometry data of a depth determined to have planar eligibility based on a planar mode. In the second paragraph, A decoding method in which the planarity of each depth is determined based on the density of points at that depth. In the third paragraph, the density of the points is A decoding method determined based on the number of coded points in a data unit containing geometry data of the above depth, the number of coded points in direct nodes, and the number of points existing at the above depth. In the third paragraph, the density of the points is A decoding method determined based on information indicating whether a planner is qualified or not included in the above signaling information. In the second paragraph, the decoding step Decode the above geometry data by subgroup unit, The qualification of planners for each of the above depths is also determined by subgroup. A decoding method in which each subgroup is identified by a subgroup index. memory; and At least one processor connected to the memory; At least one processor of the above: Receive geometry data, attribute data, and signaling information; Decode the above geometry data, A decoding device configured to decode the above attribute data. In the seventh paragraph, the processor A geometry decoder for decoding the above geometry data is included, The above geometry decoder generates a geometry tree composed of multiple depths and decodes the geometry data based on the geometry tree, The above geometry decoder is a decoding device that determines whether the geometry tree is planner-qualified based on the depth of the geometry tree, and decodes the geometry data of the depth determined to be planner-qualified based on the planner mode. In paragraph 8, A decoding device that determines whether a planar at each depth is qualified based on the density of points at that depth. In the 9th paragraph, the density of the points is A decoding device determined based on the number of coded points in a data unit containing geometry data of the depth, the number of coded points in direct nodes, and the number of points existing at the depth. In the 9th paragraph, the density of the points is A decoding method determined based on information indicating whether a planner is qualified or not included in the above signaling information. In the 8th paragraph, the geometry decoder, Decode the above geometry data by subgroup unit, The qualification of planners for each depth above is also determined by subgroup. A decoding method in which each subgroup is identified by a subgroup index. Step of encoding geometry data; A step of encoding attribute data; and An encoding method comprising the steps of transmitting the encoded geometry data, the encoded attribute data, and signaling information. In the 13th paragraph, the step of encoding the geometry data comprises: A step of generating a geometry tree consisting of multiple depths; and A step of encoding the geometry data based on the geometry tree is included, The above encoding step is an encoding method that determines whether a planner qualifies for each depth of the geometry tree and encodes geometry data of a depth determined to be planner-qualified based on the planner mode. In paragraph 14, An encoding method in which the planarity of each depth is determined based on the density of points at that depth. A computer-readable storage medium storing a bitstream generated by the method according to claim 13.