WO2025155005A1 - 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 according to embodiments may comprise the steps of: receiving a bitstream including point cloud data; and decoding the point cloud data. An encoding method according to embodiments may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data.

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 methods and devices for processing point cloud content.

Point cloud content is content expressed as a point cloud, which is a collection of points belonging to a coordinate system expressing a three-dimensional space. 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), and autonomous driving services. However, tens of thousands to hundreds of thousands of point data are required to express point cloud content. Therefore, a method for efficiently processing a large amount of point data is required.

Embodiments provide a device and method for efficiently processing point cloud data. Embodiments provide a method and device for processing point cloud data to address latency and encoding/decoding complexity.

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.

A method for transmitting point cloud data according to embodiments may include a step of encoding point cloud data and a step of transmitting a bitstream including point cloud data. A method for receiving point cloud data according to embodiments may include a step of receiving a bitstream including point cloud data and a step of decoding point cloud data.

The device and method according to the embodiments can process point cloud data with high efficiency.

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

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

The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments. For a better understanding of the various embodiments described below, reference should be made to the following description of the embodiments in conjunction with the following drawings, in which like reference numerals correspond to corresponding parts throughout the drawings.

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

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

Figure 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.

Figure 7 illustrates an example of a Point Cloud Decoder according to embodiments.

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

Figure 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.

Figure 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

Figure 12 illustrates partial point cloud bitstream transmission and decoding according to embodiments.

Figure 13 illustrates scalable decoding of point cloud data according to embodiments.

Figure 14 illustrates scalable coding according to embodiments.

Figure 15 shows a layer group for scalable transmission according to embodiments.

Figure 16 illustrates partial geometry and partial attributes according to embodiments.

Figure 17 shows partial geometry and partial attributes according to embodiments.

Figure 18 shows partial geometry and partial attributes according to embodiments.

Figure 19 illustrates partial geometry and partial attributes according to embodiments.

Figure 20 illustrates a bitstream including point cloud data and parameters according to embodiments.

Figure 21 shows scalable lifting LoD generation information according to embodiments.

Figure 22 shows a set of attribute parameters according to embodiments.

Fig. 23 shows an encoder (transmitter) according to embodiments.

Fig. 24 shows a decoder (receiving device) according to embodiments.

Figure 25 illustrates scalable lifting according to embodiments.

Figure 26 shows subgroup LoD sampling considering the missing layer according to embodiments.

Figure 27 illustrates FGS-related encoder and decoder use cases according to embodiments.

Figure 28 illustrates FGS-related encoder and decoder use cases according to embodiments.

Figure 29 shows a sequence parameter set according to embodiments.

Figure 30 shows a sequence parameter set according to embodiments.

Figure 31 shows an encoding method according to embodiments.

Figure 32 shows a decryption method according to embodiments.

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

Most of the terms used in the examples are selected from those commonly used in the field, but some terms are arbitrarily selected by the applicant and their meanings are described in detail in the following description as needed. Therefore, the examples should be understood based on the intended meaning of the terms and not on the simple names or meanings of the terms.

Figure 1 illustrates an example of a point cloud content providing 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 and 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) may include a fixed station, a BTS (base transceiver system), a network, an AI (Ariticial Intelligence) 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) may 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 (Point Cloud Video Acquisition, 10001), a point cloud video encoder (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) can encode point cloud video data based on point cloud compression coding. The point cloud compression coding according to embodiments can 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) can output a bitstream including encoded point cloud video data. The bitstream can 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 as a file or a segment (e.g., a streaming segment) and transmitted through various networks such as a broadcasting 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 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. In addition, the transmitter (10003) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). In addition, the transmission device (10000) can also transmit encapsulated data according to an on demand method.

A receiving device (10004) according to embodiments includes a receiver (Receiver, 10005), a point cloud video decoder (Point Cloud Decoder, 10006), and/or a renderer (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 Thing) 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 including point cloud video data or a file/segment in which the bitstream is encapsulated, etc., from a network or a storage medium. The receiver (10005) may perform a data processing operation required according to a network system (for example, 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 including point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to a method in which the point cloud video data is encoded (for example, a 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 a 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. The renderer (10007) can render not only the point cloud video data but also audio data to output the point cloud content. According to embodiments, the renderer (10007) can 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 arrow indicated by the dotted line in the drawing represents the transmission path of feedback information acquired from the receiving device (10004). The feedback information is information for reflecting the interactivity with the user consuming the point cloud content, and includes information of 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. According to 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 is information about the position, direction, angle, movement, etc. of the user's head. The receiving device (10004) according to embodiments can calculate viewport information based on the head orientation information. The viewport information is information about an area of the point cloud video that the user is looking at. The viewpoint is a point where the user is looking at the point cloud video, and may mean 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. of the area can be determined by the FOV (Field Of View). Therefore, the receiving device (10004) can 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) performs gaze analysis, etc. to check the user's point cloud consumption method, the area of the point cloud video that the user is looking at, the gaze time, etc. According to embodiments, the receiving device (10004) can transmit feedback information including a gaze analysis result to the transmitting device (10000). The feedback information according to embodiments can be obtained during a rendering and/or display process. The feedback information according to embodiments can be secured by one or more sensors included in the receiving device (10004). In addition, according to embodiments, the feedback information can be secured by a renderer (10007) or a separate external element (or device, component, etc.). The dotted line in Fig. 1 represents a transmission process of feedback information secured by the renderer (10007). The point cloud content providing system can process (encode/decode) point cloud data based on the feedback information. Therefore, the point cloud video data decoder (10006) can perform a decoding operation based on the feedback information. In addition, the receiving device (10004) can transmit the feedback information to the transmitting device (10000). The transmission device (10000) (or the point cloud video data encoder (10002)) can perform an encoding operation based on feedback information. Accordingly, 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, etc., and the receiving device (10004) may be referred to as a decoder, a receiving device, a receiver, 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. When 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 geometry and/or attributes of points. The geometry includes 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, etc.). Attributes include attributes of points (e.g., texture information of each point, color (YCbCr or RGB), reflectivity (r), transparency, etc.). A point has one or more attributes (or properties). For example, a point may have one attribute of color, or may have two attributes of color and reflectivity. 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 (e.g., a transmission device (10000) or a point cloud video encoder (10002)) according to embodiments 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 that encodes geometry to output a geometry bitstream. The point cloud content providing system can perform attribute encoding that encodes attributes to output an attribute bitstream. The point cloud content providing system according to embodiments can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to embodiments can be multiplexed and output as one 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 (e.g., a receiving device (10004) or a receiver (10005)) according to embodiments can receive a bitstream including encoded point cloud data. Additionally, 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 the points. A point cloud content providing 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 geometries and attributes decoded through a decoding process according to various rendering methods. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered at the vertex position, or a circle centered at 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 providing system according to embodiments (e.g., a receiving device (10004)) can obtain feedback information (20005). The point cloud content providing system can encode and/or decode point cloud data based on the feedback information. Since the feedback information and the operation of the point cloud content providing system according to embodiments are the same as the feedback information and the operation described in FIG. 1, a detailed description thereof will be omitted.

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

FIG. 3 shows 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 in order to adjust the quality (e.g., lossless, lossy, near-lossless) of point cloud content depending on a network condition or an application, etc. When the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps in the case of 30 fps), the point cloud content providing system may not be able to stream the content in real time. Therefore, the point cloud content providing 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), an analyze surface approximation unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstructing 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 Encode (30012).

The coordinate 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, the coordinate system conversion unit (30000) according to the 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 the 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 a minimum position value of all points (for example, a minimum value on each axis for the X-axis, the Y-axis, and the Z-axis). The quantization unit (30001) performs a quantization operation of multiplying a 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 expressing 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 corresponding 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 expresses 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 to voxelize an area including a large number of points in order 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 scheme includes an arithmetic encoding method. A geometry bitstream is generated as a result of the encoding.

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 the embodiments is applied equally to 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, Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) coding, and lifting transform (interpolation-based hierarchical nearest-neighbour prediction with an update/lifting step (Lifting Transform)) coding. Depending on 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 that converts color values (or textures) included in attributes. For example, the color conversion unit (30006) can 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 can 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 result 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 an attribute of a point of a position based on a position value of the point included in a 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 attributes of one or more points. When try-sort geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the try-sort geometry encoding.

The attribute transformation unit (30007) can perform attribute transformation by calculating an average value of attributes or attribute values (e.g., 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. Therefore, each voxel has a position and a calculated attribute (or attribute value).

The attribute transformation unit (30007) can search for neighboring points existing 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 positions so that a quick nearest neighbor search (NNS) is possible. 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 the 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 becomes 1095. That is, the Moulton code value of the point whose coordinate value is (5, 9, 1) is 1095. The attribute transformation unit (30007) can sort the points based on the Moulton code value and perform a nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if a nearest neighbor search (NNS) is also required in another transformation process for attribute coding, a 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 an octree.

The LOD generation unit (30009) according to the embodiments generates LOD (Level of Detail) to perform predictive transformation coding. LOD according to the embodiments is a degree indicating the detail of point cloud content. The smaller the LOD value, the lower the detail of point cloud content, and the larger the LOD value, the higher the 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, the lifting transformation coding may be applied selectively.

A coefficient quantization unit (30011) according to 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 that are 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. In addition, 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 the embodiments may include a high-speed random access memory, or may include a nonvolatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other nonvolatile solid-state memory devices).

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

As described in FIGS. 1 to 3, a point cloud content provision system (point cloud video encoder (10002)) or a 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 poles (0,0,0) and (2 ^d , 2 ^d , 2 ^d ). 2d 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.

d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

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 divided again based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Therefore, each space is divided again into eight small spaces. The divided small 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 divided spaces generated by dividing one space contains at least one point. Therefore, one occupancy code is expressed by eight child nodes. Each child node indicates the occupancy of the divided space, and the 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 child node and the eighth child node among the eight child nodes each contain at least one point. As illustrated 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., the receiving device (10004) or the 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 positions of points. However, points in a three-dimensional space are not always evenly distributed, and thus, there may be a specific area where there are not many points. Therefore, it is inefficient to perform voxelization for the entire three-dimensional space. 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-subtract geometry encoding according to embodiments can be performed in combination with octree geometry coding (or octree coding).

In order to perform direct coding, the option to use direct mode to apply 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 less than a threshold within a specific node. In addition, the total number of points to be 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 positions (or position values) of points.

A point cloud encoder according to embodiments (e.g., a 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, 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 the tri-subject mode. That is, the point cloud encoder according to embodiments can operate in the tri-subject mode only when the specified level is smaller than the depth value of the octree. A three-dimensional hexahedral area of nodes of a specified level according to embodiments is called a block. One block can include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented by a surface. A surface according to embodiments may intersect each edge of a block at most once.

Since one block has 12 edges, there are at least 12 intersections in 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 that includes 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 can entropy code the start point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (the relative position value within the edge). When the trySoop geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., the 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 the block determine the surface passing through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by the 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, ③ Square the values, and add up all of the values to obtain the value.

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 output 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 for creating a triangle depending on the number of vertices. The vertices are sorted in order from 1 to n. The table below shows that two triangles can be formed depending on the combination of the vertices for four vertices. The first triangle can be formed by the 1st, 2nd, and 3rd vertices of the sorted vertices, and the second triangle can be formed by the 3rd, 4th, and 1st vertices of the sorted vertices.

Table 2-1. 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 by adding points in the middle along the edges of the triangle. Additional points are generated based on an upsampling factor and the width of the block. The additional points are called refined vertices. The point cloud encoder according to the embodiments can voxelize the refined vertices. In addition, the point cloud encoder can 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 in front of the point cloud data). When trySoup geometry encoding is applied, the geometry reconstruction process includes triangle reconstruction, upsampling, and voxelization processes. Since attributes are dependent 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 original point cloud content. The second drawing from the left in the drawing shows the distribution of points of the lowest LOD, and the rightmost drawing in the drawing shows the distribution of points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are densely distributed. That is, as the 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), a 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 in the point cloud decoder as well as the point cloud encoder.

The upper part of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in 3D space. The original order of Fig. 6 shows the order of points P0 to P9 before LOD generation. The LOD based order of Fig. 6 shows the order of points according to LOD generation. The points are rearranged by LOD. Additionally, a high LOD includes points belonging to a low 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 prediction transformation coding to generate a predictor for points and set a prediction attribute (or a prediction attribute value) of 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 an 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 an average value of the product of the attributes (or attribute values, for example, color, reflectance, etc.) of the 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 predicted 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 the following table.

graph. Attribute prediction residuals quantization pseudo code

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);

}

}

graph. Attribute prediction residuals inverse quantization pseudo code

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 for 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 corresponding points without performing the process described above when there are no neighboring points for 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 LOD calculated in the predictor, registering neighboring points, and setting weights according to distances to neighboring points. Lifting transformation coding according to embodiments is similar to the above-described prediction 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 value of each point. The initial value of all elements of QW is 1.0. Add the value obtained by multiplying the weight of the predictor of the current point to 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 the temporary arrays to 0.

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

5) Lift update process: For each predictor, divide the attribute values in the update array by the weight values in the update weight array of the predictor index, and then add the existing attribute values 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., a RAHT transform unit (30008)) can perform RAHT transform coding that predicts attributes of upper-level nodes using attributes associated with nodes in a lower level of an octree. RAHT transform coding is an example of attribute intra coding through an octree backward scan. A point cloud encoder according to embodiments scans from a voxel to the entire area, and repeats a 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 computed 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 (400012)). 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 through the last g _{1 0, 0, 0} and g _{1 0, 0, 1} as follows.

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

Figure 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 (arithmetic decode) 7000, a synthesize octree unit (synthesize octree) 7001, a synthesize surface approximation unit (synthesize surface approximation) 7002, a reconstruct geometry unit (reconstruct geometry) 7003, an inverse transform coordinates unit (inverse transform coordinates) 7004, an arithmetic decoder (arithmetic decode) 7005, an inverse quantize unit (inverse quantize) 7006, a RAHT transform unit (7007), a generate LOD unit (generate LOD) 7008, an inverse lifting unit (inverse lifting) 7009, and/or an inverse transform colors unit (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 inversion unit (7004) can perform geometry decoding. The geometry decoding according to the embodiments can include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied selectively. In addition, the 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 with reference to FIGS. 1 to 6.

The surface opoxidation synthesis unit (7002) according to the embodiments can synthesize a surface based on decoded geometry and/or a generated octree when tri-subtractive 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), for example, triangle reconstruction, up-sampling, and voxelization operations. Specific details are the same as described in FIG. 4, and thus are 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 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 the attribute decoding described in FIG. 10. The attribute decoding according to the embodiments can include RAHT (Region Adaptive Hierarchial Transform) decoding, Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbour 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. Additionally, attribute decoding according to embodiments is not limited to the examples described above.

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

The inverse quantization unit (7006) according to the embodiments inverse quantizes information about a decoded attribute bitstream or an attribute obtained as a result of decoding and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on 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) can 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) can 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 for inverse transforming 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 that are configured to communicate with one or more memories included in the point cloud providing device, although not illustrated 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 operations and methods identical or similar to 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) can perform operations and/or acquisition methods identical or similar to the operations and/or acquisition methods of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

The data input unit (8000), the quantization processing unit (8001), the voxelization processing unit (8002), the octree occupancy code generation unit (8003), the surface model processing unit (8004), the intra/inter coding processing unit (8005), and the arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is the same as 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 the geometry (e.g., position values of points, or position values). The operation and/or quantization of the quantization processing unit (8001) is the same as or similar to the operation and/or quantization of the quantization unit (30001) described in Fig. 3. The specific description is the same as described in Figs. 1 to 6.

The voxelization processing unit (8002) according to the embodiments voxels the position values of the quantized points. The voxelization processing unit (80002) can perform operations and/or processes identical or similar to the operations and/or voxelization processes of the quantization unit (30001) described in Fig. 3. The specific description is identical to that described in Figs. 1 to 6.

The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on positions of voxelized points based on an 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 the octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is identical to 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 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 identical to that described in FIGS. 1 to 6.

The intra/inter coding processing unit (8005) according to the embodiments can intra/inter code point cloud data. The intra/inter coding processing unit (8005) can perform coding identical to or similar to the intra/inter coding described in FIG. 7. The specific description is identical to that described in FIG. 7. The intra/inter coding processing unit (8005) according to the embodiments can be included in the 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 scheme 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 about 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 can generate and/or process signaling information related to geometry encoding and/or attribute encoding. The signaling information according to the embodiments can be encoded separately from geometry encoding and/or attribute encoding. In addition, the signaling information according to the embodiments can 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 is omitted.

The color conversion processing unit (8008) according to the embodiments performs color conversion coding that converts 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 in FIGS. 1 to 6. In addition, it performs the same or similar operation and/or method as that of the color conversion unit (30006) described in FIG. 3. A detailed description is omitted.

The attribute transformation processing unit (8009) according to the embodiments performs attribute transformation that transforms 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 the operations and/or methods of the attribute transformation unit (30007) described in FIG. 3. A detailed description is omitted. The prediction/lifting/RAHT transformation processing unit (8010) according to the embodiments can code the 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 the operations that are the same as or similar to the operations of the RAHT transformation unit (30008), the LOD generation unit (30009), and the lifting transformation unit (30010) described in FIG. 3. In addition, since 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, 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 an arithmetic encoder (300012).

The transmission processing unit (8012) according to the embodiments may transmit each bitstream including encoded geometry and/or encoded attribute, metadata information, or may configure the encoded geometry and/or encoded attribute, metadata information into one bitstream and transmit them. When the encoded geometry and/or encoded attribute, metadata information according to the embodiments are configured into one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments may include signaling information including a Sequence Parameter Set (SPS) for signaling of sequence level, a Geometry Parameter Set (GPS) for signaling of geometry information coding, an Attribute Parameter Set (APS) for signaling of attribute information coding, and a Tile Parameter Set (TPS) for signaling of tile level, and slice data. The slice data may include information on one or more slices. A slice according to embodiments may include a 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. A header of a geometry bitstream according to embodiments may include identification information of a parameter set included in a 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 the signaling information 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 can 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.

Figure 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 (or the point cloud decoder of FIGS. 10 and 11). The receiving device illustrated in FIG. 9 can perform at least one or more of the operations and methods identical or similar to the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.

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 a reverse process of a component of the encoding according to embodiments.

The receiving unit (9000) according to the embodiments receives point cloud data. The receiving unit (9000) can 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 the same as or similar to the geometry decoding described in FIGS. 1 to 10, 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 or similar to those of the arithmetic decoder (7000).

The occupancy code-based octree reconstruction processing unit (9003) according to the embodiments can obtain an occupancy code from a decoded geometry bitstream (or information about the geometry obtained as a result of the decoding) to reconstruct an octree. The occupancy code-based octree reconstruction processing unit (9003) performs operations and/or methods identical to or similar to those of the octree synthesis unit (7001) and/or the octree generation method. The surface model processing unit (9004) according to the embodiments can perform tri-subject geometry decoding and geometry reconstructing related thereto (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when tri-subject geometry encoding is applied. The surface model processing unit (9004) performs operations identical to or similar to those of the surface offoxidation 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 included in the received point cloud data, such as setting values. The metadata parser (9006) can pass the metadata to geometry decoding and/or attribute decoding. A specific 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 the attribute decoding is the same as or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description is omitted.

An arithmetic decoder (9007) according to embodiments can decode an attribute bitstream with arithmetic coding. The arithmetic decoder (9007) can perform decoding of 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 the embodiments can process the reconstructed geometry and inverse quantized attributes. The prediction/lifting/RAHT inverse transform processing unit (9009) performs at least one or more of the operations and/or decodings that are identical or similar to the operations and/or decodings of the RAHT transformation unit (7007), the LOD generation unit (7008), and/or the inverse lifting unit (7009). The color inverse transform processing unit (9010) according to the 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 operations and/or inverse transform coding that are identical or similar to the operations and/or inverse transform coding of the color inverse transform unit (7010). 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 a point cloud data (PCC) device according to embodiments or may be linked with a PCC device.

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

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).

HMD (Head-Mount Display)(1070) represents one of the types in which an XR device and/or a PCC device according to embodiments may be implemented. A device of the HMD type according to 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 or 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 an external device 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 by corresponding it to 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, an autonomous vehicle that is the subject of control/interaction within an XR image, etc. 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 may 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 provide passengers with XR/PCC objects corresponding to real objects or objects on a screen by having a HUD to output XR/PCC images.

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 that the passenger's gaze is directed toward. 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 in 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, and the like.

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 objects or backgrounds in the real world. 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 with 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 to supplement real objects, whereas in MR technology, virtual objects are considered to have the same characteristics as real objects, which makes it different from AR technology. A more specific example is the hologram service to which the aforementioned MR technology is applied.

However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are sometimes called XR (extended Reality) technologies. Therefore, the embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. This technology 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) transceiver according to embodiments, when connected to a vehicle to enable wired/wireless communication with the vehicle, can receive/process content data related to AR/VR/PCC services that can be provided together with an autonomous driving service and transmit the same to the vehicle. In addition, when the point cloud data transceiver device is mounted on a vehicle, the point cloud transceiver 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. The vehicle or the 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 encoding method/device according to the embodiments includes a transmitting device (10000) of Fig. 1, a point cloud video encoder (10002), a transmitter (10003), acquisition-encoding-transmission (20000-20001-20002) of Fig. 2, an encoder of Figs. 11 to 13, partial encoding of Figs. 14 to 19, bitstream and parameter generation of Figs. 20 to 22 and Figs. 29 to 30, encoding of Figs. 23 to 28, an encoding method of Fig. 31, etc.

The decoding method/device according to the embodiments includes the receiving device (10004) of Fig. 1, the receiver (10005), the point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of Fig. 2, the decoder of Fig. 7, the receiving device of Fig. 9, the device of Fig. 10, the decoder of Figs. 11 to 13, the partial decoding of Figs. 14 to 19, the bitstream and parameter parsing of Figs. 20 to 22 and Figs. 29 to 30, the decoding of Figs. 23 to 28, the decoding method of Fig. 32, etc.

Additionally, the encoding/decoding 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 encoding/decoding method according to the embodiments includes a subgroup LoD sampling direction signaling for multiple decoders.

The embodiments include a method for efficiently supporting selective decoding of a portion of data when transmitting and receiving point cloud data due to receiver performance or transmission speed. In this case, a scalable lifting method for scalable coding can be used when performing attribute coding, and additional considerations are required for use in a partial geometry coding environment.

The embodiments relate to techniques for constructing a data structure composed of a point cloud. The embodiments include a method for informing a receiver of a sampling position by taking into account a partial geometry coding environment when generating a scalable LoD for scalable attribute coding.

Referring to FIGS. 3 and 7, point cloud data consists of the location (geometry: e.g., XYZ coordinates) and attributes (e.g., color, reflectance, intensity, grayscale, opacity, etc.) of each data. In point cloud compression (PCC), octree-based compression is performed to efficiently compress distribution characteristics that are unevenly distributed in a three-dimensional space, and attribute information is compressed based on this.

The encoding/decoding method according to the embodiments assumes full octree depth geometry when generating LoD used in scalable lifting, and applies sampling positions alternately according to the octree depth, thereby increasing compression efficiency when compressing by using attributes located near the node center. If scalable transmission is supported, partial geometry is supported, in which case the sampling positions of the scalable lifting LoD generation may not match between the encoder and the decoder, which may cause a problem of inaccurately representing the partial point cloud. By changing or clearly signaling the sampling reference position according to the method according to the embodiments, the problem of mismatch between geometry and attributes during partial decoding can be solved.

Embodiments include a method for signaling subgroup LoD sampling directions considering single bitstream multiple decoders.

Figure 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

Point cloud data divides the location information of the data point and the feature information such as color/brightness/reflectivity into geometry and attribute information and compresses and transmits them respectively. At this time, the PC data can be configured according to the 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 receiver, but there is currently no method for removing unnecessary data in advance. In other words, in the case where only a part of the scalable PCC bitstream needs to be transmitted (in the case of decoding only some of the layers during scalable decoding), it is not possible to select and send only the necessary part, so 1) the necessary part is re-encoded after decoding, or 2) the entire thing is transmitted and then selectively applied at the receiver. However, in case 1), there may be a delay due to the time for decoding and re-encoding, and in case 2), bandwidth efficiency is reduced due to the transmission of unnecessary data, and when a fixed bandwidth is used, there is a disadvantage in that data quality must be lowered for transmission.

Figure 12 illustrates partial point cloud bitstream transmission and decoding according to embodiments.

If compressed data is divided and transmitted according to the layer, there is an advantage in that only the necessary parts can be selectively transmitted at the bitstream stage for pre-compressed data without a separate conversion process. This is efficient in terms of storage space because only one storage space is required per stream, and efficient transmission is also possible in terms of bandwidth because only the necessary layers are selectively transmitted before transmission (bitstream selector).

Figure 13 illustrates scalable decoding of point cloud data according to embodiments.

From the perspective of the receiver (decoder), if information capable of restoring the entire PCC data is transmitted regardless of the performance of the receiver, the receiver needs to restore the point cloud data through decoding and then select only the data corresponding to the required layer (data selection or sub-sampling). In this case, since the transmitted bitstream is already decoded, it may cause a delay in a receiver that aims for low delay or may not be able to decode depending on the performance of the receiver. However, if the bitstream is divided into slice units and transmitted, the receiver can selectively transmit the bitstream to the decoder depending on the decoder performance or the density of the point cloud data to be expressed according to the application field. In this case, since the selection is made before decoding, the decoder efficiency is increased, and there is an advantage of being able to support decoders with various performances.

Figure 14 illustrates scalable coding according to embodiments.

The encoding/decoding method according to the embodiments can scalably encode and decode point cloud data.

Scalable coding:

The encoding method according to the embodiments can compress and transmit point cloud data to enable scalable transmission. First, a tool that can support scalability can be used when using position and attribute compression. In addition, when transmitting compressed data, slices can be subdivided to enable partial decoding, and a basis for selecting each slice before decoding can be signaled. Information for selecting a slice required by a receiver can be transmitted. Scalable transmission may mean supporting a case where only a part of a bitstream is transmitted or decoded instead of decoding the entire bitstream, and the result may be low resolution point cloud data.

When applying scalable transmission to an Octree (same as an Accumancy Tree)-based geometry bitstream, point cloud data can be constructed with information only up to a specific Octree layer for the bitstream of each Octree layer from the root node to the leaf node. To this end, there is no dependency on the information of lower Octree layers for the target Octree layer (layer of the Accumancy Tree). The dependency condition can be a constraint that is commonly applied to geometry and attribute coding.

In the case of attribute coding, partial decoding may be possible according to octree depth when scalable lifting is used, similar to octree-based geometry coding. Each octree depth may belong to a LoD, and one or more octree depths may match a LoD. Fig. 14 is a description of a method for generating LoD when scalable lifting is used, and shows a method for selecting an attribute of a parent node as one of the attributes of a child node based on a geometry octree structure. As shown in Fig. 14, when the octree depth is 4, an attribute of a node belonging to octree depth 3 can select one of the child nodes belonging to octree depth 4. Similarly, an attribute of a node belonging to octree depth 2 can select one of the attributes of a child node belonging to octree depth 3. In scalable lifting, attributes can be assigned to nodes at all octree levels by recursively using a method of selecting representative attributes based on the attributes of child nodes in the direction from the leaf node to the root node.

Figure 14 shows a case where the location of a node selected among child nodes varies depending on the octree level. At the leaf node level (octree depth 4), the very first node can be used as an attribute of the parent node when sampling, and the very last node can be used as an attribute of the parent node when sampling at octree depth 3. Nodes (1400) identified by shading indicate locations selected as attributes of the parent node.

The encoding method according to the embodiments can encode the entire attribute data based on the level of detail related to the octree (accumulation tree). Among the nodes belonging to each depth (or layer) of the octree (accumulation tree), if the index of the depth (or layer) is even, the attribute of the first node in the sorted order among the child nodes belonging to one parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in the sorted order among the child nodes belonging to one parent node can be set (sampled) as the attribute of the parent node.

The decryption method according to the embodiments can decode the entire attribute data based on the level of detail related to the octree (accumulation tree). Among the nodes belonging to each depth (or layer) of the octree (accumulation tree), if the index of the depth (or layer) is even, the attribute of the first node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in the sorted order among the child nodes belonging to a parent node can be set (sampled) as the attribute of the parent node.

Figure 15 shows a layer group for scalable transmission according to embodiments.

The encoding method according to the embodiments can generate a layer group for scalable transmission.

Geometry and attribute coding schemes support layer-based scalability, and can support scalability before decoding, such as in scalable transmission applications, by configuring slices according to the layer structure. Fig. 15 shows a method for configuring layer groups according to an octree structure.

Layer group: Represents a group of layer structure units that occur in G-PCC coding, such as octree layers and LoD layers.

Sub-group: Represents a set of adjacent nodes based on location information for a layer group. Or, a group can be formed based on the lowest layer in the layer group (which can mean the layer closest to the root direction, for example, layer 6 in the case of group 3). A group of adjacent nodes can be formed by the order of the Moulton code, a group of adjacent nodes based on distance, or a group of adjacent nodes based on the coding order. Additionally, nodes in a parent-child relationship can be defined to exist in a subgroup.

When defining a subgroup, the boundary occurs in the middle of the layer, and whether to have continuity at the boundary, whether to use entropy continuously, such as the entropy continuity enable flag (sps_entropy_continuation_enabled_flag), the entropy continuity flag (gsh_entropy_continuation_flag), etc., can be used, and continuity with the previous slice can be maintained by indicating the reference slice ID (ref_slice_id).

When using the layer group and subgroup structure, it is necessary to convey the scalable structure for selecting the scalable layer in the transmitter/receiver (encoder/decoder). Considering the octree structure, all octree layers may support scalable transmission, but scalable transmission may be possible only for a specific octree layer or lower. When including some of the octree layers, it is possible to determine whether the slice is necessary or not at the bitstream stage by indicating which scalable layer the slice is included in. For example, starting from the root node, one scalable layer may be configured without supporting scalable transmission up to a certain depth, and the octree layers below may 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 as shown in FIG. 15, when multiple octree layers are included in a slice, one scalable layer can be configured for the layers.

The embodiments can be used to distinguish between scalable transmission and scalable decoding depending on the purpose. In the case of scalable transmission, the embodiments can be used for the purpose of selecting information up to a specific layer without going through a decoder at the transmitting and receiving ends. In the case of scalable decoding, a specific layer can be selected during coding. Scalable transmission can support selecting required information without going through a decoder in a compressed state (at the bitstream stage) so that it can be determined at the transmitting or receiving end. In the case of scalable decoding, it can be used in cases such as scalable representation by supporting cases where encoding/decoding is performed only up to the required part during the encoding/decoding process.

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

The encoding/decoding method according to the embodiments can encode and decode point cloud data based on slice level scalability and octree level scalability.

A layer-group can represent a unit for representing a set of one or more octree layers. A layer-group can represent a bundle of octree layers to be configured in slice units. Spatial scalability by actual octree layers (or scalable attribute layers) can be provided for each octree layer, but when scalability is configured in slice units before bitstream parsing, it can be selected in layer-group units. In Fig. 15, when utilizing slice-unit scalability such as scalable transmission, the provided scalable stages can include three stages of group 1, group 2, and group 3. The scalable stages that can be provided in the decoding stage by the octree structure can include eight stages from the root to the leaf.

In an embodiment, when groups 1 to 3 are each composed of slices, the receiver or transmitter can 1) select only group 1, 2) select group 1 and group 2, or 3) select group 1, group 2, and group 3.

1) If only group 1 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. The receiver (decoder) can consider the node size that can be obtained through the maximum octree depth as a leaf node. And the node size can be transmitted to the decoder as signaling information (parameter information).

2) When selecting Group 1 and Group 2, Layer 5 is added, so that the maximum octree level becomes 5, and one scalable layer among the octree layers from 0 to 5 can be selected during the decoding process. At this time, the receiver can consider the node size that can be obtained through the maximum octree depth as a leaf node. And the node size can be transmitted to the decoder as signaling information (parameter information).

3) When selecting Group 1, Group 2, and Group 3, 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 receiver can consider the node size that can be obtained through the maximum octree depth as a leaf node. And the node size can be transmitted to the decoder as signaling information (parameter information).

The decoding layers for geometry and attributes can be chosen identically or asymmetrically.

Figure 16 illustrates partial geometry and partial attributes according to embodiments.

The encoding method according to the embodiments can encode an attribute based on partial geometry. The decoding method according to the embodiments can decode an attribute based on partial geometry.

When scalable transport is used, partial decoding of geometry and attributes may be performed, which may result in differences between encoded and decoded octree depths. In the case of scalable lifting, which performs LoD generation based on the entire octree depth, a mismatch may occur due to differences in encoded/decoded octree depths.

When performing scalable lifting LoD generation for octree depth 4, the first child node is selected for the leaf node level, the last child node for the leaf node level +1, the first child node for the leaf node level +2, and so on, alternately as the octree depth decreases. The method for selecting a node closer to the center of the upper node has the advantage of improving coding efficiency. Since the entire octree depth is assumed, the following problems may occur in the partial geometry situation. Fig. 16 shows a case where scalable lifting LoD generation is performed in a situation where partial decoding is performed up to octree depth 3 in a situation where encoding is performed for octree depth 4. The scalable lifting LoD generation alternately performs the operation of selecting the first child node and the last child node starting from the leaf node level and going down to the root node level depending on the octree level. Considering the decoded geometry octree depth, the receiver (decoder) selects the first child node at octree level 3 and the last child node at octree level 2 in the scalable lifting LoD generation. However, this results in selecting different properties from the scalable lifting LoD generation performed based on the octree depth 4 at the time of encoding. This results in decoding a partial point cloud that is different from the intention of the encoder.

As shown in Fig. 16, the encoder can sample the attribute of the first child node among the child nodes belonging to one parent node at depth 4 out of the total four octree depths (octree layers or levels of level of detail) as the representative attribute of the parent node, and sample the attribute of the last child node among the child nodes belonging to one parent node at depth 3 as the representative attribute of the parent node. The decoder can decode point cloud data related to octree depths 1 to 3 for partial decoding. It knows that depth 3 is the last layer, so it can sample the attribute of the first child node among the child nodes belonging to one parent node at depth 2 as the representative attribute of the parent node. Since the encoder and the decoder perform sampling in different orders for depth (layer) 3, an error occurs in the restored point cloud.

Figure 17 shows partial geometry and partial attributes according to embodiments.

To address the issue of sampling order mismatch between encoder and decoder when generating scalable lifting LoD due to partial geometry, the encoder can signal the sampling position to the decoder. The sampling position for each level can be signaled separately. By transmitting information about the entire octree depth together, the exact sampling position can be used in the partial geometry context. This has the advantage of supporting partial geometry and attributes through signaling without changing the existing encoder/decoder.

The decoder can partially decode geometry related to octree depths (layers or levels of detail) 1 to 3, and partially decode attributes related to octree depths (layers or levels of detail) 1 to 3. When the encoder encodes attributes for depths 1 to 4, the point cloud data can be compressed by sampling the attribute of the first node among the child nodes belonging to a parent node at depth 4 as the representative attribute of the parent node. When restoring the compressed point cloud data, the decoder can partially decode the point cloud data up to depths (levels) 1 to 3, and when starting decoding at depth 3, the attribute of the last child node among the child nodes belonging to a parent node at depth 3, not the first child node, can be sampled as the representative attribute of the parent node based on the sampling order signaled by the encoder. The sampling positions for all depths 2 and 1 can be transmitted by the encoder to the decoder as signaling information (parameter information).

Figure 18 shows partial geometry and partial attributes according to embodiments.

The encoding method according to the embodiments can signal a sampling position for octree depth 1. Since partial geometry decoding is a method of discarding consecutive octree levels from a leaf node level, if sampling information for octree depth 1, which is always decoded, is transmitted, subsequent sampling positions can be applied alternately according to the octree depth. For example, if the sampling position for octree depth 1 is the last child node, the first child node and the last child node for the subsequent octree depth can be inferred. There is an advantage that partial geometry and attributes can be supported through signaling without changing the existing encoder/decoder. If the sampling position for depth 1 is the last node, the encoder can transmit information indicating that the sampling position for depth 1 is the last node to the decoder. The decoder can sequentially sample the first node at depth 2 and the last node at depth 3 as attributes of the parent node for depth 2 and depth 3, respectively.

Figure 19 illustrates partial geometry and partial attributes according to embodiments.

The encoding method according to the embodiments can fix the sampling position for octree depth 1. Unlike the existing method of fixing the sampling position for the leaf node level, there is an advantage in that the scalable LoD generation sampling position does not change even if partial geometry coding occurs by fixing it for octree depth 1.

As in Fig. 19, the sampling position among the child nodes belonging to one parent node at depth (layer or level) 1 can be fixed to the first node. The decoder can sample the first node at depth 1 according to the fixed order, sequentially infer the sampling position at depth 2 to sample the last node, and then sample the first node by inferring the sampling position at depth 2.

Furthermore, the encoding method according to the embodiments may use fixed sampling locations for all depths. The encoding method according to the embodiments may signal to use a specific order of points.

octree depth encoder (full octree depth 4) decoder (partial octree depth 3) 1 First (fixed) First (fixed) 2 First (fixed) First (fixed) 3 First (fixed) First (fixed) 4 First (fixed) -

octree depth encoder (full octree depth 4) decoder (partial octree depth 3) 1 Last (fixed) Last (fixed) 2 Last (fixed) Last (fixed) 3 Last (fixed) Last (fixed) 4 Last (fixed) -

As above, the sampling position can be fixed, such as sampling the first child node at each depth, or the sampling position can be fixed, such as sampling the last mode at each depth. The encoder can transmit signaling information indicating the use of fixed positions (orders) to the decoder.

In applications such as layer-group slicing or scalable coding, when skip layer-group or skip layer is used, the sampling direction used in the encoder can be inferred to maintain coding efficiency. The missing layer information can be inferred through the layer information, bounding box information or root node information of the actual decoded/encoded sub-data together with the full layer information, bounding box information or root node information of the input point cloud data of the encoder. The layer can be estimated in the encoder. The used sampling direction can be inferred as (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded).

if (layer_group_enabled_flag) {

octreeNodeSizeLog2 = lodIndex;

direction = (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded) &1;

subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

}

LoD Index (lodIndex): An index representing the current lod. The value of the index can increase or decrease sequentially from the upper lod to the lower lod.

Octree Node Size (octreeNodeSizeLog2): A log2 scale value for the node size of the current lod.

Root Node Size (rootNodeSizeLog2): A log2 scale value for the size of the node or bounding box that covers the entire input point cloud data of the encoder.

Coded root node size (rootNodeSizeLog2_coded): A log2 scale value for the size of the node or bounding box that covers the actual coded point cloud data.

Figure 20 illustrates a bitstream including point cloud data and parameters according to embodiments.

The encoding method according to the embodiments can transmit information related to a separated slice according to the embodiments by including it in a bitstream. A parameter set and/or an SEI message in the bitstream can include information related to the separated slice. A sequence parameter set (SPS), a geometry parameter set (GPS), an attribute parameter set (APS) and/or a geometry slice header (GSH) and/or an attribute slice header (ASH) in the bitstream can include information related to the separated slice. Depending on the application or system, information related to the separated slice can be defined at a corresponding location in the bitstream or at a separate location, and the scope of application, the method of application, etc. can be used differently. Depending on where a signal is transmitted in the bitstream, it can have different meanings. If defined in SPS, it can be applied equally to the entire sequence. If defined in GPS, it can indicate that it is used for location restoration. If defined in APS, it can indicate that it is applied to attribute restoration. If defined in TPS, it can indicate that the signaling is applied only to points within a tile. If transmitted in slice units, it can indicate that the signal is applied only to the corresponding slice. In addition, depending on the application or system, it can be defined in a corresponding location or a separate location and used differently in the application scope, application method, etc. 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, information related to a separated slice can be transmitted through a parameter set of a higher concept.

Each abbreviation stands for the following. Each abbreviation may be replaced by other terms within the same scope: 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: Attribute bitstream = attribute blick header + attribute brick data.

A slice contains point cloud data and represents a unit of encoding and decoding. A slice may be referred to as a data unit. A slice represents geometry and attributes for part or all of an encoded point cloud frame. A slice may be referred to as a brick. A data unit represents a sequence of bytes that conveys a syntax structure of a fixed length.

The embodiments define the information to be described later independently of the coding technique, and furthermore, the information below can be defined in conjunction with the coding method, and can be defined in the tile parameter set 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.

The encoding method according to the embodiments can define a NAL (Network abstract layer) unit and define related information for selecting a layer, such as a layer ID (layer_id), in a bitstream and transmit it to a decoder. The decoding method according to the embodiments can select and decode a bitstream at a system level.

Parameters (which may be referred to in various ways, such as metadata, signaling information, etc.) according to the embodiments may be generated in the process of a transmitter according to the embodiments described below, and may be transmitted to a receiver according to the embodiments and used in the reconfiguration process.

For example, parameters according to embodiments may be generated in a metadata processing unit (or metadata generator) of a transmitting device according to embodiments described below, and may be acquired in a metadata parser of a receiving device according to embodiments.

Figure 21 shows scalable lifting LoD generation information according to embodiments.

Figure 22 shows a set of attribute parameters according to embodiments.

Figures 21 and 22 illustrate the syntax of parameter information included in the bitstream of Figure 20. The information of Figures 21 and 22 can be generated by an encoding device and decoded by a decoding device.

Top-down LoD generation flag (top_down_LoD_generation_flag): If this value is 1, when sampling attributes from child nodes for scalable lifting LoD generation, it can indicate that sampling is performed while the sampling location alternately changes in the top-down direction based on the initial sampling depth (for example, octree depth (depth of the accumulation tree) 1). If this value is 0, when sampling attributes from child nodes for scalable lifting LoD generation, it can indicate that sampling locations are specified while alternately changing in the bottom-up direction based on the leaf node level. The criterion for determining the sampling position can be changed from the bottom to the top. Scalable lifting LoD generation can be performed in the bottom-up manner.

first_depth_sampling_direction_present_flag: If this value is 1, it can indicate that the sampling position is explicitly transmitted when sampling attributes from child nodes for the reference octree depth (e.g., octree depth 1). If this value is 0, it can indicate that the sampling position is implicitly transmitted when sampling attributes from child nodes. For example, either the first child node or the last child node can be the sampling position.

First depth sampling direction flag (first_depth_sampling_direction_forward_flag): If this value is 1, it can indicate that sampling is performed in the forward direction when sampling attributes from child nodes. That is, it can indicate that the first node among child nodes is sampled for the first depth of sampling. If this value is 0, it can indicate that sampling is performed in the reverse direction when sampling attributes from child nodes. That is, it can indicate that the last node among child nodes is sampled for the first depth of sampling.

Number of full octree depths (num_full_octree_depth): It can indicate the octree depths considered in encoding. When top_down_LoD_generation_flag is 0, the sampling direction of scalable lifted LoD generation is determined in the bottom-up direction, so the decoder can use num_full_octree_depth, which indicates the octree depths considered in encoding, rather than the decoded geometry depth, as a criterion for determining the directionality at each octree depth.

child_node_sampling_forward_direction_flag): If this value is 1, it can indicate that the sampling position is explicitly forwarded when sampling attributes from child nodes for each octree depth. If it is 0, it can indicate that the sampling position is implicitly forwarded when sampling attributes from child nodes. For example, the sampling position can be either the first child node or the last child node.

child_node_sampling_position: It can directly signal the sampling position at each octree level. If it is 0, the sampling position is the first node, and if it is 1, it can indicate the last node. If it is a value between 2 and 7, it can indicate that the 1st to 6th node (or the closest node) is used. For example, if it is a value between 2 and 7, it can indicate a node located between the first and last node.

lifting_scalability_enabled_flag: If 1, it indicates that the attribute decoding process allows pruned octree decoding for the input geometry points. If lifting_scalability_enabled_flag is 0, it indicates that the attribute decoding process requires complete octree decoding for the input geometry points. If this flag is not present, its value is inferred to be 0. If trisoup_enabled_flag is 0, lifting_scalability_enabled_flag is 0. If geom_tree_coded_axis_list_present_flag is 1, lifting_scalability_enabled_flag is 0.

If the geometry scaling enable flag (geom_scaling_enabled_flag) is 1, lifting_scalability_enabled_flag is 0 to ensure partial decoding of the geometry point cloud.

Lifting nearest neighbor range maximum (lifting_max_nn_range_minus1): If this value is 1 plus, it indicates the nearest neighbor maximum range value used to limit the distance of points registered as neighbors. The value of lifting_max_nn_range is the number of octree nodes around the point.

fixed_sampling_direction_forward_flag: If this value is 1, it indicates that a sampling method is used that selects points in a fixed order for the points included in the sampling unit.

Sampling direction type (sampling_direction_type): This can indicate the method used for sampling or the order of points. 0 indicates that the first point among the Morton code sorted points is used, 1 indicates that the last point among the Morton code sorted points is used, 2 indicates that the second point is used, and 3 indicates that the third point is used.

inferred_sampling_direction_enabled_flag: If this value is 1, it can indicate that the method of inferring sampling direction is used.

Root Node Size (rootNodeSizeLog2): This can represent a log2 scale value for the size of the node or bounding box that covers the entire input point cloud data of the encoder.

Coded root node size (rootNodeSizeLog2_coded): A log2 scale value for the size of the node or bounding box that covers the actual coded point cloud data. The encoder can signal the coded root node size. Instead of signaling it directly, the root node size can be inferred internally in the encoder or decoder based on the decoded geometry point cloud data.

The encoder and/or decoder can infer the number of missing layers via rootNodeSizeLog2 - rootNodeSizeLog2_coded and, if desired, can directly signal the number of missing layers as parameter information.

Fig. 23 shows an encoder (transmitter) according to embodiments.

FIG. 23 corresponds to the transmitting 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 encoder of FIGS. 11 to 13, the partial encoding of FIGS. 14 to 19, the bitstream and parameter generation of FIGS. 20 to 22 and FIGS. 29 to 30, the encoding of FIGS. 23 to 28, the encoding method of FIG. 31, etc.

Hereinafter, the components of the transceiver of the present invention will be described. Each component may correspond to a processor, software, or hardware. In addition, the components below may be combined with the PCC transceiver structure and/or signaling information. Each of the encoder (encoding device) and the decoder (decoding device) may be composed of a memory and a processor.

1) Encoder (encoding device):

The encoder acquires point cloud data as input. The encoder can encode position information (geometry data (e.g., XYZ coordinates, phi-theta coordinates, etc.)) and attribute information (attribute data (e.g., color, reflectance, intensity, grayscale, opacity, medium, material, glossiness, etc.)) respectively. The compressed data can be divided into units for transmission. The sub-bitstream generator can select necessary information from bitstream units according to layering structure information, divide them into appropriate units, and pack them.

Fig. 24 shows a decoder (receiving device) according to embodiments.

FIG. 24 corresponds to the receiving device (10004), the receiver (10005), the point cloud video decoder (10006) of FIG. 1, the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoder of FIGS. 11 to 13, the partial decoding of FIGS. 14 to 19, the bitstream and parameter parsing of FIGS. 20 to 22 and FIGS. 29 to 30, the decoding of FIGS. 23 to 28, the decoding method of FIG. 32, etc.

The decoder receives a bitstream as input from the encoder. The decoder can process the bitstream for position information and the bitstream for attribute information separately. At this time, the sub-bitstream classifier can pass the information of the bitstream header to the appropriate decoder. Or, the layer required by the receiver can be selected during this process. The classified bitstream can be converted into a format for final output by the renderer after the geometry decoder and the attribute decoder restore the geometry data and attribute data, respectively, according to the characteristics of the data.

Figure 25 illustrates scalable lifting according to embodiments.

A decoding device according to embodiments can decode point cloud data based on scalable lifting, as shown in FIG. 25. When slices are configured in layers for scalable transmission, scalable lifting according to metadata can be performed, as shown in FIG. 25. When scalable lifting is used, signaling information of scalable lifting LoD generation information (scalable_lifting_lod_generation_info()) transmitted through an SEI message or APS according to embodiments can be used. If the top-down LoD generation flag is 1, the sampling directionality of the top level can be explicitly signaled through first_depth_sampling_direction_forward_flag (when first_depth_sampling_direction_present_flag = 1), or the sampling directionality of the top level defined in the standard document can be used (when first_depth_sampling_direction_present_flag = 0). If top-down LoD generation is not used, the sampling positions can be estimated in the bottom-up direction using the octree depth information used during encoding (num_full_octree_depth), or the positions can be specified using the sampling positions of each octree depth given via child_node_sampling_forward_dicrection_flag or child_node_sampling_position.

The decryption method according to the embodiments estimates the sampling position in the bottom-up direction if scalable lifting enable is true and top-down LoD generation is false. If top-down LoD generation is true and sampling direction existence is false, the sampling position is estimated in the top-down direction. If sampling direction existence is true, the sampling position is estimated in the top-down direction.

Figure 26 illustrates subgroup LoD sampling considering missing layers according to embodiments.

The method according to the embodiments can change the LoD sampling direction for each layer in attribute layer-group slicing. In each layer, child nodes are arranged in Morton code order, and attributes of the first or last node can be selected (sampled) as attributes of the parent node according to the octree layer.

When the number of total octree depths is passed to the decoder, the decoder can sample points (nodes) in the octree in the same LoD sampling direction as the encoder. However, when a subset of the coded bitstream is decoded, the sampling direction of the bottom layer is fixed to the forward direction, and there may be different sampling directions (first or last node) depending on the encoding layer, so the sampling direction of the decoder may not be the same as that of the encoder.

To address this issue, the method according to the embodiments can compensate for the number of missing layers in the subgroup LoD generation as follows:

Number of missing layers = maximum depth of encoded LoD - maximum depth of the current subgroup LoD

The method according to the embodiments can calculate the number of missing layers through the difference between the maximum depth value of the LoD encoded by the encoder and the maximum depth value of the current subgroup LoD.

Additionally, the number of missing layers can be derived from two parameters.

The root node size (rootNodeSizeLog2) is a parameter that represents the maximum depth of the encoded LoD.

The encoded root node size (rootNodeSizeLog2_coded) is a derived value representing the maximum depth of the current subgroup LoD.

These parameters can be used to compensate for the octree node size (octreeNodeSizeLog2) in determining the LoD sampling direction.

if (layer_group_enabled_flag) {

int32_t octreeNodeSizeLog2 = lodIndex;

bool direction = (octreeNodeSizeLog2 + (rootNodeSizeLog2 - rootNodeSizeLog2_coded)) &1;

subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

}

The sampling direction can be calculated based on the octree node size (octreeNodeSizeLog2), root node size (rootNodeSizeLog2), and coded node size (rootNodeSizeLog2_coded).

OctreeNodeSizeLog2 can be the maximum depth value of LoD.

Based on this, the octreeNodeSizeLog2 parameter can be used as follows:

if (layer_group_enabled_flag) {

int32_t octreeNodeSizeLog2 = lodIndex;

bool direction = octreeNodeSizeLog2 &1;/ octreeNodeSizeLog2 = maxDepth - endDepth[layerId]

/ maxDepth += sps.num_layers_minus1[layerGroupId]+1;

subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

}

You can calculate the maximum number of layers (max.layer) using a method such as maxDepth += sps.num_layers_minus1[layerGroupId]+1.

The method of deriving the LoD sampling direction in the two methods of subgroup LoD generation described above is the same. However, the method of calculating the maximum depth value of the encoded LoD may be different. One is implemented to derive the value using a defined parameter for the number of layers in each layer group, and the other is to signal this value as explicit parameter information.

Figure 27 illustrates FGS-related encoder and decoder use cases according to embodiments.

Figure 28 illustrates FGS-related encoder and decoder use cases according to embodiments.

In progressive decoding and partial decoding, the target application can be considered as a single decoder. Fine-grained slicing (FGS) according to embodiments can be used to support multiple decoders with different capabilities due to memory, clock speed, display devices, etc.

For example, if we have decoders A, B and C with different capabilities in terms of resolution, one way to support this is to generate different sets of bitstreams according to the target device requirements. As shown in Fig. 27(b), an efficient way is to select fine-grained slices to generate bitstreams with fine-grained slicing that can support the three different decoders.

For example, an encoder can select a sub-layer group of a bitstream based on a decoder's request. If fine-grained slicing is not used, another bitstream must be generated by another encoder and optionally stored or transmitted. As shown in Fig. 28(b), a single bitstream with fine-grained slicing can support multiple decoders without additional encoders and storage.

To support these use cases, a granular slicing encoder can generate a different set of parameters for each target device. This is used to verify decoder capabilities and set decoder parameters. In this case, an encoder or transcoder can set different parameter values for a subset of bitstreams depending on the target device when the syntax definition is not clear.

If necessary, multiple parameter sets, such as SPS, GPS, and APS, can be generated for different target devices, and each parameter set can provide criteria for selecting a different level of bitstream subset that can be supported in the bitstream.

For example, the number of layer groups (num_layer_group_minus1) can mean the maximum depth that the target device must support. Since the number of layer groups (num_layer_group_minus1) has different values for decoders A, B, and C, the bitstreams that can be processed by each decoder can be selectively used.

For example, it is possible to determine whether a final displayable subset is present based on the number of points in a subgroup or layer group (in subgroup / in layer-group).

Referring to Fig. 27(a), there is a case of a multi-encoder and multi-decoder without FGS, where the multi-encoder down-samples and encodes the entire point cloud data, or encodes the entire point cloud data and transmits bitstreams with different sizes, and the multi-decoder can decode low-resolution and high-resolution point cloud data, respectively.

Referring to Fig. 27(b), there is a single encoder and multi-decoder based on FGS, and when a single encoder encodes and transmits point cloud data into multiple FGSs through layer group slicing, the multi-decoder can decode low-resolution to high-resolution point cloud data based on FGS.

Referring to Fig. 28(a), there is a multi-encoder and multi-decoder without FGS, and the multi-encoder can down-sample the entire point cloud data to encode partial point cloud data, or encode the entire point cloud data. The slice selector can select a low-resolution or high-resolution slice, and transcode the selected slice and transmit it to the multi-decoder. The multi-decoder can decode the point cloud data within the received low-resolution or high-resolution slice.

Referring to FIG. 28(b), there is a single encoder and a multi-decoder with FGS, the single encoder encodes the entire point cloud data based on the layer group slice, and transcodes low-resolution to high-resolution FSGs through a slice selector and transmits them to the decoder, and the multi-decoder can decode the point cloud data in the low-resolution to high-resolution FSGs.

Considering the use cases of Figs. 27 and 28, two possible examples for the maximum depth of encoded LoD are described below.

Figure 29 shows a sequence parameter set according to embodiments.

The encoding method according to the embodiments can generate a sequence parameter set of Fig. 29 and include it in a bit tree. The decoding method according to the embodiments can decode point cloud data based on information in the sequence parameter set of Fig. 29.

As shown in FIG. 29, the method according to the embodiments can generate and transmit FGS related parameter information through explicit signaling.

In an explicit way, the root subgroup bounding box size (root_subgroup_bbox_size), which represents the maximum depth of the LoD in the encoder, can be passed in the SPS.

The root node size (root_node_size_log2) represents the log 2 value of the size of the cubic root node of the encoded octree.

Root node size (root_node_size_log2) can mean the number of encoded tree layers from the root to the leaf layer.

In the sub-slicing, the number of missing layers in a subgroup can be calculated based on the difference between root_node_size_log2 and the maximum depth of the current subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in the subgroup LoD generation. The number of missing layers in a subgroup is used to compensate for the approximate location of the node in the intermediate layer.

Figure 30 shows a sequence parameter set according to embodiments.

The method according to the embodiments may further add descriptive information within the bitstream, for example within a sequence parameter set.

For example, one could add constraints on the maximum depth of the encoded LoD and the associated signaling information. Considering that EE SW uses num_layers_minus1 to derive the maximum depth of the encoded LoD, one could add the following semantics:

Number of Layer Groups (num_layer_groups_minus1): If this value is 1, it indicates the number of layer groups representing a contiguous tree layer group that is part of the geometry coding tree structure. num_layer_groups_minus1 is in the range of 0 to the number of coding tree layers. num_layer_groups_minus1 is the number of layer groups value of the encoder.

Number of layers (num_layers_minus1): Adding 1 to this value indicates the number of coding layers included in the i-th layer group. The maximum number of coded layers of the current frame is calculated by accumulating num_layers_minus1[i] + 1 for all i in the range from 0 to num_layer_groups_minus1.

The maximum number of layers derived from num_layers_minus1 is the number of encoded tree layers from the root to the leaf layers.

In fine-grained slicing, the number of missing layers in a subgroup can be the difference between the maximum number of layers and the maximum depth of the current subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in the subgroup LoD generation. The number of missing layers in a subgroup is used to compensate for the approximate location of nodes in the intermediate layers.

The number of layer groups (num_layer_groups_minus1) may change depending on the number of receivers when the transcoder supports various receivers.

Figure 31 shows an encoding method according to embodiments.

The encoding method according to the embodiments may include a step of encoding point cloud data (S3100) and/or a step of transmitting a bitstream including point cloud data (S3110).

Referring to FIGS. 23 and 15 together, the step of encoding point cloud data (S3100) includes the step of encoding geometry data of the point cloud data, the step of encoding attribute data of the point cloud data, wherein the geometry data is encoded based on a tree including a root layer to a leaf layer, the attribute data is encoded based on a tree including a root layer to a leaf layer, the tree for the geometry data includes a layer group including at least one layer, and the layer group includes at least one subgroup, the tree for the attribute data includes a layer group including at least one layer, and the layer group includes at least one subgroup, and the step of encoding may further include generating scalable lifting level of detail (LoD) information.

Referring also to FIG. 17, with respect to signaling sampling locations of nodes within a tree, the step of encoding point cloud data includes generating information indicating sampling locations of nodes within a specific depth of the tree of attribute data, wherein the sampling location may be either a first node or a last node.

Referring also to FIG. 18, with respect to signaling only the initial position of node sampling within the tree, the step of encoding point cloud data includes generating information indicating the sampling position of nodes within the initial depth of the tree of attribute data, wherein the sampling position can be either the first node or the last node.

Referring also to FIG. 19, with respect to fixing the initial position of sampling within the tree, the step of encoding the point cloud data includes sampling a node based on a sampling position within the initial depth of the tree of attribute data, wherein the sampling position may be either a first node or a last node.

The device according to the embodiments may further include a computer-readable storage medium storing a bitstream generated by the encoding method according to the embodiments.

A method according to embodiments may include a step of obtaining a bitstream for point cloud data, the bitstream being generated based on a step of encoding geometry data of the point cloud data and a step of encoding attribute data of the point cloud data; and a step of transmitting data including the bitstream.

Figure 32 shows a decryption method according to embodiments.

A decryption method according to embodiments may include a step of receiving a bitstream including point cloud data (S3200), and/or a step of decoding point cloud data (S3210).

Referring to FIG. 24 together, the step of decoding point cloud data (S3210) may include a step of decoding geometry data of point cloud data and a step of decoding attribute data of point cloud data.

Referring to FIGS. 22 and 21 together, with respect to lifting_scalability_enabled_flag, scalable_lifting_lod_generation_info( ), the bitstream includes a flag for scalable lifting, and based on a value of the flag for scalable lifting, the bitstream further includes scalable lifting level-of-detail (LoD) information, and the scalable lifting level-of-detail information may include at least one of a generation flag for level-of-detail (LoD) for attribute data of point cloud data, a sampling direction presence flag, a sampling rectitude* flag, or octree total depth count information.

Referring together to FIG. 21, with respect to top_down_LoD_generation_flag, first_depth_sampling_direction_present_flag, first_depth_sampling_direction_forward_flag, and num_full_octree_depth, a first value of a generation flag for level of detail for attribute data indicates that a sampling location for a node of the tree is changed in a top-down direction from a specific depth of the tree for the point cloud data, a second value of the generation flag for level of detail for attribute data indicates that a sampling location for a node of the tree is changed in a bottom-up direction from a leaf node of the tree for the point cloud data, a sampling direction present flag indicates whether a sampling location of a specific depth of the tree for the point cloud data is explicitly signaled, a sampling rect* flag indicates whether a first node is sampled at a specific depth of the point cloud data, and the octree full depth count information may indicate a total number of depths of an octree for geometry data of the point cloud data. there is.

Referring together to FIG. 21, with respect to fixed_sampling_direction_forward_flag, sampling_direction_type, inferred_sampling_direction_enabled_flag, rootNodeSizeLog2, and rootNodeSizeLog2_coded, the scalable lifting level of detail information includes at least one of a fixed sampling direction flag, a sampling direction type, an inferred sampling direction flag, a root node size of input point cloud data, or a root node size of encoded point cloud data, wherein the fixed sampling forward flag indicates whether a position for sampling a node in a tree for the point cloud data is fixed, the sampling direction type indicates whether a position for sampling a node in a tree for the point cloud data is a first node or a last node, and the sampling direction inferred flag may indicate whether a position for sampling a node in a tree for the point cloud data is inferred.

Referring together to FIG. 29, with respect to root_node_size_log2, the bitstream includes information related to the size of the root node of the encoded octree for the point cloud data, and the information related to the size of the root node of the encoded octree for the point cloud data can indicate the number of layers from the root layer to the leaf layer of the encoded octree.

Referring together to FIG. 30, with respect to num_layer_groups_minus1 and num_layers_minus1, the bitstream includes at least one of information indicating the number of layer groups, or information indicating the number of layers, and the information indicating the number of layer groups may indicate a value related to the number of layer groups including a continuous portion of layers of a tree with respect to geometry data of the point cloud data, and the information indicating the number of layers may indicate a value related to the number of layers included in the layer group.

The decryption method is performed by a decryption device, and the decryption device includes, with reference to FIG. 1, a memory; and at least one processor connected to the memory; and the at least one processor can be configured to: receive a bitstream including point cloud data; and decode the point cloud data.

The method and device according to the embodiments have the following technical effects.

The LoD generation used in scalable lifting assumes a full octree depth geometry and applies sampling positions alternately, such as the first or the last node among child nodes, depending on the octree depth, so that compression efficiency is achieved by using attributes located near the center of the nodes. If scalable transmission is supported, partial geometry is supported, in which case the sampling positions of the scalable lifting LoD generation may not match between the encoder and the decoder (due to at least one missing layer), which may cause inaccurate representation of the partial point cloud. In the case of partial decoding, the mismatch between geometry and attributes can be resolved by changing or explicitly signaling the sampling reference position according to the method according to the embodiments.

The point cloud data transmission/reception method/device according to the embodiments can scalably encode and/or decode point cloud data by considering partial geometry, and can efficiently compress and restore point cloud data based on operation and/or related signaling information according to the embodiments.

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

The operations according to the embodiments described above may be performed by a transmitting device and/or a receiving device according to the embodiments. The transmitting/receiving device may include a transmitting/receiving unit for transmitting and receiving media data, a memory for storing instructions (program codes, algorithms, flowcharts, and/or data) for a process according to the embodiments, and a processor for controlling operations of the transmitting/receiving device.

The processor may be referred to as a controller, etc., and may correspond to, for example, hardware, software, and/or a combination thereof. The operations according to the embodiments described above may be performed by the processor. In addition, the processor may be implemented as an encoder/decoder, etc. for the operations of the embodiments described above.

The embodiments have been described in terms of methods and/or devices, and the descriptions of methods and devices may be applied complementarily.

For the convenience of explanation, each drawing has been described separately, but it is also possible to design a new embodiment by combining the embodiments described in each drawing. In addition, designing a computer-readable recording medium in which a program for executing the previously described embodiments is recorded according to the needs of a person skilled in the art also falls 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 the embodiments so that various modifications can be made. Although the preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and various modifications can be made by a person skilled in the art without departing from the gist of the embodiments claimed in the claims, and such modifications should not be individually understood from the technical idea or prospect of the embodiments.

The various components of the device of the embodiments may be performed by hardware, software, firmware, or a combination thereof. The various components of the embodiments may be implemented as one chip, for example, one hardware circuit. According to embodiments, the components according to the embodiments may be implemented as separate chips, respectively. According to embodiments, at least one of the components of the device 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 device 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 transmission over the Internet. Additionally, the processor-readable recording medium may be distributed over network-connected computer systems, so that the processor-readable code may 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”.

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 limited in their interpretation by the above 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 the first user input signal and the 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 used 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” and “or” are used to mean including all possible combinations of the terms. The expression “includes” describes the presence of features, numbers, steps, elements, and/or components, and does not mean that additional features, numbers, steps, elements, and/or components are not included. Conditional expressions such as “if,” “when,” etc., used to describe the embodiments are not intended to be limited to only optional cases. When a particular condition is satisfied, a related action is performed in response to a particular condition, or a related definition is intended to be interpreted.

In addition, the operations according to the embodiments described in this document may be performed by a transceiver device including a memory and/or a processor according to the embodiments. The memory may store programs for processing/controlling the operations according to the embodiments, and the processor may control various operations described in this document. The processor 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 the processor or in the memory.

Meanwhile, the operations according to the embodiments described above may be performed by a transmitting device and/or a receiving device according to the embodiments. The transmitting/receiving device may include a transmitting/receiving unit for transmitting and receiving media data, a memory for storing instructions (program codes, algorithms, flowcharts, and/or data) for a process according to the embodiments, and a processor for controlling operations of the transmitting/receiving device.

The processor may be referred to as a controller, etc., and may correspond to, for example, hardware, software, and/or a combination thereof. The operations according to the embodiments described above may be performed by the processor. In addition, the processor may be implemented as an encoder/decoder, etc. for the operations of the embodiments described above.

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

As described above, the embodiments can be applied in whole or in part to a point cloud data transmission and reception device and system.

Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

Embodiments may include modifications/changes, which do not depart from the scope of the claims and their equivalents.

Claims (15)

A step of receiving a bitstream containing point cloud data; and A step of decoding the above point cloud data; comprising: How to decrypt. In the first paragraph, The step of decoding the above point cloud data is A step of decoding geometry data of the above point cloud data, Comprising a step of decoding attribute data of the above point cloud data, How to decrypt. In the first paragraph, The above bitstream contains a flag for scalable lifting, The above bitstream further includes scalable lifting level of detail (LoD) information based on the value of the flag for scalable lifting, The above scalable lifting level of detail information is At least one of a generation flag for level of detail (LoD) for attribute data of the above point cloud data, a sampling direction existence flag, a sampling correctness flag, or information on the total depth count of the octree, How to decrypt. In the third paragraph, The first value of the generation flag for the level of detail for the above attribute data indicates that the sampling location for the node of the tree is changed in the top-down direction from a specific depth of the tree for the above point cloud data, The second value of the generation flag for the level of detail for the above attribute data indicates that the sampling location for the nodes of the tree is changed in the bottom-up direction from the leaf nodes of the tree for the above point cloud data, The above sampling direction presence flag indicates whether the sampling location at a specific depth of the tree with respect to the point cloud data is explicitly signaled, The above sampling_definition* flag indicates whether to sample the first node at a specific depth with respect to the point cloud data. The above octree total depth count information indicates the total depth count of the octree for the geometry data of the point cloud data. How to decrypt. In the third paragraph, The above scalable lifting level of detail information is Contains at least one of a fixed sampling rate "*" flag, a sampling direction type, a sampling direction inference flag, a root node size of input point cloud data, or a root node size of encoded point cloud data, The above fixed sampling forward flag indicates whether the location for sampling nodes in the tree for the point cloud data is fixed. The above sampling direction type indicates whether the location where the node is sampled in the tree for the above point cloud data is the first node or the last node. The above sampling direction inference flag indicates whether the location for sampling a node in the tree for the above point cloud data is inferred. How to decrypt. In the first paragraph, The above bitstream includes information related to the size of the root node of the encoded octree for the point cloud data, Information related to the size of the root node of the encoded octree for the above point cloud data indicates the number of layers from the root layer to the leaf layer of the encoded octree. How to decrypt. In the first paragraph, The above bitstream includes at least one of information indicating the number of layer groups or information indicating the number of layers, The information indicating the number of the above layer groups indicates a value related to the number of layer groups including a continuous portion of layers of a tree regarding geometry data of the above point cloud data, The information indicating the number of layers above indicates a value related to the number of layers included in the layer group. How to decrypt. memory; and At least one processor coupled to said memory; wherein said at least one processor comprises: Receiving a bitstream containing point cloud data; and configured to decode the above point cloud data; Decryption device. Step of encoding point cloud data; and A step of transmitting a bitstream including the above point cloud data; comprising: Encoding method. In Article 9, The step of encoding the above point cloud data is A step of encoding geometry data of the above point cloud data, Comprising a step of encoding attribute data of the above point cloud data, The above geometry data is encoded based on a tree including a root layer and a leaf layer, The above attribute data is encoded based on a tree including a root layer and a leaf layer, The tree for the above geometry data comprises a layer group including at least one layer, and the layer group comprises at least one subgroup, The tree for the above attribute data includes a layer group including at least one layer, and the layer group includes at least one subgroup, The encoding step further comprises generating scalable lifting level of detail (LoD) information. Encoding method. In Article 10, The step of encoding the above point cloud data is Including generating information indicating the sampling locations of nodes within a specific depth of the tree of the above attribute data, The above sampling location is either the first node or the last node, Encoding method. In Article 10, The step of encoding the above point cloud data is Including generating information indicating the sampling positions of nodes within the initial depth of the tree of the above attribute data, The above sampling location is either the first node or the last node, Encoding method. In Article 10, The step of encoding the above point cloud data is Comprising sampling a node based on a sampling position within the initial depth of the tree of the above attribute data, The above sampling location is either the first node or the last node, Encoding method. A computer-readable storage medium storing a bitstream generated by the method according to Article 9. Step of obtaining bitstream for point cloud data; The bitstream is generated based on a step of encoding geometry data of the point cloud data and a step of encoding attribute data of the point cloud data; and Comprising a step of transmitting data including the bitstream, method.