WO2020197086A1 - 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 point cloud data transmission method, according to embodiments, comprises the steps of: encoding point cloud data; and transmitting a bitstream comprising the point cloud data. A point cloud data reception method, according to embodiments, comprises the steps of: receiving a bitstream comprising point cloud data; decoding the point cloud data; and rendering 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

The embodiments are directed to a method and apparatus for processing point cloud content.

Point cloud content is content expressed as a point cloud, which is a set of points (points) belonging to a coordinate system representing a three-dimensional space. Point cloud content can express media consisting of three dimensions, and provides various services such as VR (Virtual Reality, Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality, Mixed Reality), and autonomous driving services. Used to provide. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, a method for efficiently processing a vast amount of point data is required.

The embodiments provide an apparatus and method for efficiently processing point cloud data. Embodiments provide a point cloud data processing method and apparatus for solving latency and encoding/decoding complexity.

However, it is not limited only to the above-described technical problem, and the scope of the rights of the embodiments may be extended to other technical problems that can be inferred by those skilled in the art based on the entire description.

In order to achieve the above object and other advantages, a method for transmitting point cloud data according to embodiments includes: encoding point cloud data; And transmitting a bitstream including point cloud data. Includes.

In addition, a method for receiving point cloud data according to embodiments includes: receiving a bitstream including point cloud data; Decoding the point cloud data; And rendering the point cloud data. Includes.

The apparatus and method according to the embodiments may process point cloud data with high efficiency.

The apparatus and method according to the embodiments may provide a point cloud service of high quality.

The apparatus and method according to the embodiments may 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 represent embodiments together with a description related to the embodiments.

1 shows a system for providing point cloud content according to embodiments.

2 shows a process for providing Point Cloud content according to embodiments.

3 shows an arrangement of Point Cloud capture equipment according to embodiments.

4 shows a point cloud encoder according to embodiments.

5 illustrates voxels in a 3D space according to embodiments.

6 shows an example of an octree and an occupancy code according to embodiments.

7 illustrates an example of a neighbor node pattern according to embodiments.

8 shows an example of a point configuration of Point Cloud content for each LOD according to embodiments.

9 shows an example of a point configuration of Point Cloud content for each LOD according to embodiments.

10 shows an example of a block diagram of a point cloud decoder according to embodiments.

11 shows an example of a point cloud decoder according to embodiments.

12 shows components for encoding Point Cloud video of a transmitter according to embodiments.

13 shows components for decoding Point Cloud video of a receiver according to embodiments.

14 shows an architecture for G-PCC-based point cloud data storage and streaming according to embodiments.

15 shows point cloud data storage and transmission according to embodiments.

16 shows a device for receiving point cloud data according to embodiments.

17 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.

18 illustrates an example of an attribute information (attribute information) predictor according to embodiments.

19 shows a configuration of an encoded point cloud according to embodiments.

20 illustrates an example of information related to an APS neighbor point set generation option according to embodiments.

21 illustrates an example of information related to a neighbor point set generation option of a TPS according to embodiments.

22 illustrates an example of information related to a neighbor point set generation option in a Slice header of Attr according to embodiments.

23 shows an example of a PCC (Point Cloud Compression) encoder according to embodiments.

24 shows an example of a geometric information encoder according to embodiments.

25 shows an example of an attribute information encoder according to embodiments.

26 shows an example of a PCC decoder according to embodiments.

27 shows an example of a geometric information decoder according to embodiments.

28 shows an example of an attribute information decoder according to embodiments.

29 shows an example of a point cloud data transmission method according to embodiments.

30 shows an example of a method for receiving point cloud data according to embodiments.

The preferred embodiments of the embodiments will be described in detail, examples of which are shown in the accompanying drawings. The detailed description below with reference to the accompanying drawings is intended to describe preferred embodiments of the embodiments, rather than showing 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 is obvious to a person skilled in the art that the embodiments may be practiced without these details.

Most terms used in the embodiments are selected from general ones widely used in the relevant field, but some terms are arbitrarily selected by the applicant, and their meanings will be described in detail in the following description as necessary. Accordingly, the embodiments should be understood based on the intended meaning of the term, not the simple name or meaning of the term.

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

The point cloud content providing 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 or wireless communication to transmit and receive point cloud data.

. The transmission device 10000 according to the embodiments may secure, process, and transmit point cloud video (or point cloud content). According to embodiments, the transmission device 10000 is a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or server. And the like. In addition, according to embodiments, the transmission device 10000 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, Robots, vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, etc. may be included.

The 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 (Transmitter (or Communication module), 10003). Include)

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

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

The transmitter 10003 according to the embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (for example, 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. Also, according to embodiments, the encapsulation unit may be included in the transmitter 10003. According to embodiments, a file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter 10003 according to the embodiments may perform wired/wireless communication with the reception device 10004 (or a receiver 10005) through a network such as 4G, 5G, or 6G. In addition, the transmitter 10003 may perform necessary data processing operations according to a network system (for example, a communication network system such as 4G, 5G, or 6G). In addition, the transmission device 10000 may transmit encapsulated data according to an on demand method.

The reception device 10004 according to the embodiments includes a receiver 10005, a point cloud video decoder 10006, and/or a renderer 10007. According to embodiments, the receiving device 10004 uses a wireless access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a robot , Vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, and the like.

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

The point cloud video decoder 10006 decodes a bitstream including point cloud video data. The point cloud video decoder 10006 may decode the point cloud video data according to the encoding method (for example, a reverse process of the operation of the point cloud video encoder 10002). Accordingly, the point cloud video decoder 10006 may decode the point cloud video data by performing point cloud decompression coding, which is a reverse process of the point cloud compression. Point cloud decompression coding includes G-PCC coding.

The renderer 10007 renders the decoded point cloud video data. The renderer 10007 may output point cloud content by rendering audio data as well as point cloud video data. According to embodiments, the renderer 10007 may include a display for displaying point cloud content. According to embodiments, the display is not included in the renderer 10007 and may be implemented as a separate device or component.

An arrow indicated by a dotted line in the drawing indicates a transmission path of feedback information acquired by the receiving device 10004. The feedback information is information for reflecting an interaction ratio with a user who consumes point cloud content, and includes user information (eg, head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service that requires interaction with a user (for example, an autonomous driving service, etc.), the feedback information is the content sending side (for example, the transmission device 10000) and/or a service provider. Can be delivered to According to embodiments, the feedback information may be used not only in the transmitting device 10000 but also in the receiving device 10004, and may not be provided.

Head orientation information according to embodiments is information on a position, direction, angle, and movement of a user's head. The receiving device 10004 according to the embodiments may calculate viewport information based on the head orientation information. The viewport information is information on the area of the point cloud video that the user is viewing. A viewpoint is a point at which the user is watching a point cloud video, and may mean a center point of a viewport area. That is, the viewport is an area centered on a viewpoint, and the size and shape of the area may be determined by a field of view (FOV). Accordingly, the receiving device 10004 may extract viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device 10004 performs a gaze analysis and the like to check the point cloud consumption method of the user, the point cloud video area that the user gazes, and the gaze time. According to embodiments, the receiving device 10004 may transmit feedback information including the result of gaze analysis to the transmitting device 10000. Feedback information according to embodiments may be obtained during rendering and/or display. Feedback information according to embodiments may be secured by one or more sensors included in the receiving device 10004. Also, according to embodiments, the feedback information may be secured by the renderer 10007 or a separate external element (or device, component, etc.). A dotted line in FIG. 1 shows a process of transmitting feedback information secured by the renderer 10007. The point cloud content providing system may process (encode/decode) point cloud data based on feedback information. Accordingly, the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information. Also, the receiving device 10004 may transmit feedback information to the transmitting device 10000. The transmission device 10000 (or the point cloud video data encoder 10002) may perform an encoding operation based on feedback information. Therefore, the point cloud content providing system does not process (encode/decode) all point cloud data, but efficiently processes necessary data (e.g., point cloud data corresponding to the user's head position) based on feedback information. Point cloud content can be provided to users.

According to embodiments, the transmission device 10000 may be referred to as an encoder, a transmission device, a transmitter, and the like, and the reception device 10004 may be referred to as a decoder, a reception device, a receiver, or the like.

Point cloud data (processed in a series of acquisition/encoding/transmission/decoding/rendering) processed 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. I can. According to embodiments, the point cloud content data may be used as a concept including metadata or signaling information related to the point cloud data.

Elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, processor, and/or a combination thereof.

2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.

The block diagram of FIG. 2 shows the operation of the point cloud content providing system described in FIG. 1. As described above, the point cloud content providing system may process point cloud data based on point cloud compression coding (eg, G-PCC).

A point cloud content providing system according to embodiments (for example, the point cloud transmission apparatus 10000 or the point cloud video acquisition unit 10001) may 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. A point cloud video according to embodiments may 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 may include one or more Ply files. Ply files contain point cloud data such as the geometry and/or attributes of the point. The geometry includes the positions of the points. The position of each point may be expressed by parameters (eg, values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (eg, a coordinate system composed of XYZ axes). Attributes include attributes of points (eg, texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). A point has one or more attributes (or attributes). For example, one point may have an attribute of one color, or two attributes of a color and reflectance. Depending on embodiments, geometry may be referred to as positions, geometry information, geometry data, and the like, and attributes may be referred to as attributes, attribute information, attribute data, and the like. In addition, the point cloud content providing system (for example, the point cloud transmission device 10000 or the point cloud video acquisition unit 10001) provides points from information related to the acquisition process of the point cloud video (eg, depth information, color information, etc.). Cloud data can be secured.

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

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

The point cloud content providing system according to the embodiments (for example, the receiving device 10004 or the receiver 10005) may receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (for example, the receiving device 10004 or the receiver 10005) may demultiplex the bitstream.

The point cloud content providing system (e.g., the receiving device 10004 or the point cloud video decoder 10005) can decode the encoded point cloud data (e.g., geometry bitstream, attribute bitstream) transmitted as a bitstream. have. The point cloud content providing system (for example, the receiving device 10004 or the 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. have. The point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore positions (geometry) of points by decoding a geometry bitstream. The point cloud content providing system may restore the attributes of points by decoding an attribute bitstream based on the restored geometry. The point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore the point cloud video based on the decoded attributes and positions according to the restored geometry.

The point cloud content providing system according to the embodiments (for example, the receiving device 10004 or the renderer 10007) may render the decoded point cloud data (20004 ). The point cloud content providing system (for example, the receiving device 10004 or the renderer 10007) may render geometry and attributes decoded through a decoding process according to a rendering method 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 on the vertex position, or a circle centered on the vertex position. All or part of the rendered point cloud content is provided to the user through a display (eg VR/AR display, general display, etc.).

The point cloud content providing system according to the embodiments (for example, the receiving device 10004) may secure feedback information (20005). The point cloud content providing system may encode and/or decode point cloud data based on feedback information. Since the operation of the feedback information and point cloud content providing system according to the embodiments is the same as the feedback information and operation described in FIG. 1, a detailed description will be omitted.

3 shows an example of a point cloud video capture process according to embodiments.

3 shows an example of a point cloud video capture process in the point cloud content providing system described in FIGS. 1 to 2.

The point cloud content is an object located in various three-dimensional spaces (for example, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video (images and/or Videos). Therefore, the point cloud content providing system according to the embodiments includes one or more cameras (eg, an infrared camera capable of securing depth information, color information corresponding to the depth information) to generate the point cloud content. You can capture a point cloud video using an RGB camera that can extract the image), a projector (for example, an infrared pattern projector to secure depth information), and LiDAR. The point cloud content providing system according to the embodiments may obtain point cloud data by extracting a shape of a geometry composed of points in a 3D space from depth information, and extracting an attribute of each point from color information. An image and/or an image according to the embodiments may be captured based on at least one or more of an inward-facing method and an outward-facing method.

The left side of Fig. 3 shows an inword-facing scheme. The inword-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding a central object capture a central object. The in-word-facing method provides point cloud content that provides users with 360-degree images of key objects (e.g., provides users with 360-degree images of objects (eg, key objects such as characters, players, objects, actors, etc.) VR/AR content).

The right side of Fig. 3 shows the outword-pacing scheme. The outward-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the environment of the central object other than the central object. The outward-pacing method may be used to generate point cloud content (for example, content representing an external environment that may be provided to a user of a self-driving vehicle) to provide an environment that appears from a user's point of view.

As shown in the figure, the point cloud content may be generated based on the capture operation of one or more cameras. In this case, since the coordinate system of each camera may be different, the point cloud content providing system may calibrate one or more cameras to set a global coordinate system before the capture operation. In addition, the point cloud content providing system may generate point cloud content by synthesizing an image and/or image captured by the above-described capture method with an arbitrary image and/or image. In addition, the point cloud content providing system may not perform the capture operation described in FIG. 3 when generating point cloud content representing a virtual space. The point cloud content providing system according to embodiments may perform post-processing on the captured image and/or image. In other words, the point cloud content providing system removes an unwanted area (e.g., background), recognizes the space where captured images and/or images are connected, and performs an operation to fill in a spatial hole if there is. I can.

In addition, the point cloud content providing system may generate one point cloud content by performing coordinate system transformation on points of the point cloud video acquired from each camera. The point cloud content providing system may perform a coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing a wide range, or may generate point cloud content having a high density of points.

4 shows an example of a point cloud encoder according to embodiments.

4 shows an example of the point cloud video encoder 10002 of FIG. 1. The point cloud encoder uses point cloud data (for example, positions and/or positions of points) to adjust the quality of the point cloud content (for example, lossless-lossless, loss-lossy, near-lossless) according to network conditions or applications. Attributes) and perform an encoding operation. When the total size of the point cloud content is large (for example, a 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.

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

Point cloud encoders according to embodiments include a coordinate system transform unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface aproximation analysis unit ( Analyze Surface Approximation, 40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Transform Colors (40006), Transfer Attributes (40007), RAHT Transformation A unit 40008, an LOD generation unit (Generated LOD) 40009, a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011), and/or an Arithmetic Encode (40012).

The coordinate system transform unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface aproximation analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 perform geometry encoding. can do. Geometry encoding according to embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. Also, geometry encoding is not limited to the above example.

As shown in the drawing, the coordinate system conversion unit 40000 according to the embodiments receives positions and converts them into a coordinate system. For example, positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space represented by an XYZ coordinate system). The location information of the 3D space according to embodiments may be referred to as geometry information.

The quantization unit 40001 according to embodiments quantizes geometry. For example, the quantization unit 40001 may quantize points based on the minimum position values of all points (eg, minimum values on each axis with respect to the X-axis, Y-axis, and Z-axis). The quantization unit 40001 multiplies the difference between the minimum position value and the position value of each point by a preset quantum scale value, and then performs a quantization operation to find the nearest integer value by performing a rounding or a rounding. Thus, one or more points may have the same quantized position (or position value). The quantization unit 40001 according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. The minimum unit including the 2D image/video information is a pixel, and points of the point cloud content (or 3D point cloud video) according to the embodiments may be included in one or more voxels. have. Voxel is a combination of volume and pixel, and the three-dimensional space is a unit (unit=1.0) based on the axes representing the three-dimensional space (for example, X-axis, Y-axis, Z-axis). It refers to a three-dimensional cubic space that occurs when divided by. The quantization unit 40001 may match groups of points in a 3D space with 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, a position of a center point (ceter) of a corresponding voxel may be set based on positions of one or more points included in one voxel. In this case, attributes of all positions included in one voxel may be combined and assigned to a corresponding voxel.

The octree analysis unit 40002 according to the embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on an octal tree structure.

The surface aproxiation analysis unit 40003 according to the embodiments may analyze and approximate the octree. The octree analysis and approximation according to the embodiments is a process of analyzing to voxelize a region including a plurality of points in order to efficiently provide octree and voxelization.

The arithmetic encoder 40004 according to embodiments entropy encodes the octree and/or the approximated octree. For example, the encoding method includes an Arithmetic encoding method. As a result of encoding, a geometry bitstream is generated.

Color conversion unit 40006, attribute conversion unit 40007, RAHT conversion unit 40008, LOD generation unit 40009, lifting conversion unit 40010, coefficient quantization unit 40011 and/or Arismatic encoder 40012 Performs attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to attributes of one point. However, when one attribute (eg, color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments includes color transform coding, attribute transform coding, Region Adaptive Hierarchial Transform (RAHT) coding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, and interpolation-based hierarchical nearest -Neighbor prediction with an update/lifting step (Lifting Transform)) coding may be included. Depending on the point cloud content, the aforementioned RAHT coding, predictive 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 example.

The color conversion unit 40006 according to embodiments performs color conversion coding for converting color values (or textures) included in attributes. For example, the color conversion unit 40006 may convert the format of color information (eg, convert from RGB to YCbCr). The operation of the color conversion unit 40006 according to the embodiments may be selectively applied according to color values included in attributes.

The geometry reconstruction unit 40005 according to embodiments reconstructs (decompresses) an octree and/or an approximated octree. The geometry reconstruction unit 40005 reconstructs an octree/voxel based on a result of analyzing the distribution of points. The reconstructed octree/voxel may be referred to as reconstructed geometry (or reconstructed geometry).

The attribute conversion unit 40007 according to the embodiments performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed. As described above, since attributes are dependent on geometry, the attribute conversion unit 40007 may transform the attributes based on the reconstructed geometry information. For example, the attribute conversion unit 40007 may convert an attribute of the point of the position based on the position value of the point included in the voxel. As described above, when a position of a center point of a corresponding voxel is set based on positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of one or more points. When tri-soup geometry encoding is performed, the attribute conversion unit 40007 may convert attributes based on trisoup geometry encoding.

The attribute conversion unit 40007 is an average value of attributes or attribute values (for example, the color of each point or reflectance) of points neighboring within a specific position/radius from the position (or position value) of the center point of each voxel. Attribute conversion can be performed by calculating. The attribute conversion unit 40007 may apply a weight according to a distance from a central point to each point when calculating an average value. Thus, each voxel has a position and a calculated attribute (or attribute value).

The attribute conversion unit 40007 may search for neighboring points existing within a specific position/radius from the position of the center point of each voxel based on a K-D tree or a Molton code. The K-D tree is a binary search tree and supports a data structure that can manage points based on location so that the Nearest Neighbor Search (NNS) can be quickly performed. The Molton code represents a coordinate value (for example, (x, y, z)) representing a three-dimensional position of all points as a bit value, and is generated by mixing the bits. For example, if the coordinate value indicating the position of the point is (5, 9, 1), the bit value of the coordinate value is (0101, 1001, 0001). If the bit values are mixed according to the bit index in the order of z, y, x, it is 010001000111. If this value is expressed in decimal, it becomes 1095. That is, the Molton code value of the point whose coordinate value is (5, 9, 1) is 1095. The attribute conversion unit 40007 may sort points based on a Morton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, when the shortest neighbor search (NNS) is required in another transformation process for attribute coding, a K-D tree or a Molton code is used.

As shown in the figure, the converted attributes are input to the RAHT conversion unit 40008 and/or the LOD generation unit 40009.

The RAHT conversion unit 40008 according to embodiments performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT conversion unit 40008 may predict attribute information of a node at a higher level of the octree based on attribute information associated with a node at a lower level of the octree.

The LOD generation unit 40009 according to embodiments generates a level of detail (LOD) to perform predictive transform coding. The LOD according to the embodiments is a degree representing the detail of the point cloud content, and a smaller LOD value indicates that the detail of the point cloud content decreases, and a larger LOD value indicates that the detail of the point cloud content is high. Points can be classified according to LOD.

The lifting transform unit 40010 according to embodiments performs lifting transform coding that transforms attributes of a point cloud based on weights. As described above, the lifting transform coding can be selectively applied.

The coefficient quantization unit 40011 according to embodiments quantizes attribute-coded attributes based on coefficients.

Arismatic encoder 40012 according to embodiments encodes quantized attributes based on Arismatic coding.

The elements of the point cloud encoder of FIG. 4 are not shown in the drawing, but hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus. , Software, firmware, or a combination thereof. 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. 4 described above. Further, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of the elements of the point cloud encoder of FIG. 4. One or more memories according to embodiments may include high speed random access memory, and nonvolatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other nonvolatile solid state Memory devices (solid-state memory devices, etc.).

5 shows an example of a voxel according to embodiments.

5 shows voxels located in a three-dimensional space represented by a coordinate system composed of three axes of the X-axis, Y-axis, and Z-axis. As described with reference to FIG. 4, a point cloud encoder (eg, quantization unit 40001, etc.) may perform voxelization. The voxel refers to a three-dimensional cubic space generated when a three-dimensional space is divided into units (unit = 1.0) based on axes (eg, X-axis, Y-axis, and Z-axis) representing the three-dimensional space. FIG. 5 is an octree structure recursively subdividing a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^d ) Shows an example of a voxel generated through. One voxel includes at least one or more points. The voxel can estimate spatial coordinates from the positional relationship with the voxel group. As described above, the voxel has attributes (such as color or reflectance) like pixels of a 2D image/video. A detailed description of the voxel is the same as that described with reference to FIG. 4 and thus will be omitted.

6 shows an example of an octree and an occupancy code according to embodiments.

As described in FIGS. 1 to 4, a point cloud content providing system (point cloud video encoder 10002) or a point cloud encoder (for example, octree analysis unit 40002) efficiently manages the area and/or position of the voxel. To do this, octree geometry coding (or octree coding) based on an octree structure is performed.

The upper part of FIG. 6 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed by axes of a coordinate system (eg, X-axis, Y-axis, Z-axis). The octree structure is created by recursive subdividing of a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 ^d , 2 ^d , 2 ^d ). . 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video). d represents the depth of the octree. The d value is determined according to the following equation. In the following equation, (x ^int _n , y ^int _n , z ^int _n ) represents positions (or position values) of quantized points.

As shown in the middle of the upper portion of FIG. 6, the entire 3D space may be divided into eight spaces according to the division. Each divided space is represented by a cube with 6 faces. As shown on the right side of the upper part of FIG. 6, each of the eight spaces is divided again based on the axes of the coordinate system (eg, X axis, Y axis, Z axis). Thus, each space is further divided into eight smaller spaces. The divided small space is also represented as a cube with 6 faces. This division method is applied until a leaf node of an octree becomes a voxel.

The lower part of FIG. 6 shows the octree's ocupancy code. The octree's ocupancy code is generated to indicate whether each of the eight divided spaces generated by dividing one space includes at least one point. Therefore, one Okufanshi code is represented by 8 child nodes. Each child node represents the occupancy of the divided space, and the child node has a value of 1 bit. Therefore, the Ocufanshi code is expressed as an 8-bit code. That is, if at least one point is included in the space corresponding to the child node, the node has a value of 1. If the point is not included in the space corresponding to the child node (empty), the node has a value of 0. Since the ocupancy code shown in FIG. 6 is 00100001, it indicates that the spaces corresponding to the third child node and the eighth child node among the eight child nodes each include at least one point. As shown in the figure, the third child node and the eighth child node each have eight child nodes, and each child node is represented by an 8-bit ocupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the ocupancy code of the 8th child node is 01001111. A point cloud encoder (for example, the Arismatic encoder 40004) according to embodiments may entropy encode an ocupancy code. In addition, in order to increase the compression efficiency, the point cloud encoder can intra/inter code the ocupancy code. The reception device (for example, the reception device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs an octree based on an ocupancy code.

A point cloud encoder according to embodiments (for example, the point cloud encoder of FIG. 4 or the octree analyzer 40002) may perform voxelization and octree coding to store positions of points. However, since points in the 3D space are not always evenly distributed, there may be a specific area where there are not many points. Therefore, it is inefficient to perform voxelization over the entire 3D space. For example, if there are almost no points in a specific area, it is not necessary to perform voxelization to the corresponding area.

Therefore, the point cloud encoder according to the embodiments does not perform voxelization for the above-described specific region (or nodes other than the leaf nodes of the octree), but directly codes the positions of points included in the specific region. ) Can be performed. Coordinates of a direct coding point according to embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to embodiments may perform trisoup geometry encoding in which positions of points within a specific region (or node) are reconstructed based on voxels 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 embodiments may be selectively performed. In addition, direct coding and trisoup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

In order to perform direct coding, the option to use direct mode to apply direct coding must be activated, and the node to which direct coding is applied is not a leaf node, but below the threshold within a specific node. There must be points of. In addition, the number of all points subject to direct coding must not exceed a preset limit. When the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004) according to the embodiments may entropy-code the positions (or position values) of the points.

The point cloud encoder according to the embodiments (for example, the surface aproximation analysis unit 40003) determines a specific level of the octree (if the level is less than the depth d of the octree), and from that level, the node Trisoup geometry encoding that reconstructs the position of a point in the region based on voxels can be performed (tri-soup mode). A point cloud encoder according to embodiments may designate a level to which trisoup geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in the try-soup mode. That is, the point cloud encoder according to the embodiments may operate in the try-soup mode only when the specified level is less than the depth value of the octree. A three-dimensional cube area of nodes of a designated level according to the embodiments is referred to as a block. One block may include one or more voxels. The block or voxel may correspond to a brick. Within each block, the geometry is represented by a surface. The surface according to embodiments may intersect each edge (edge) of the block at most once.

Since one block has 12 edges, there are at least 12 intersection points within one block. Each intersection is called a vertex (vertex, or vertex). A vertex existing along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An occupied voxel according to embodiments refers to a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels among all blocks sharing the edge.

When the vertex is detected, the point cloud encoder according to the embodiments may entropy-code the starting point (x, y, z) of the edge and the direction vector (ΔΔΔ vertex position value (relative position value within the edge)) of the edge. When encoding is applied, the point cloud encoder (e.g., the geometry reconstruction unit 40005) according to the embodiments performs a triangle reconstruction, up-sampling, and voxelization process. You can create geometry (reconstructed geometry).

The vertices located at the edge of the block determine the surface that passes through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② calculate the squared values of the values subtracted from each vertex value by subtracting the center value, and calculate the sum of all the values.

Calculate the minimum value of the added value, and perform a projection process along the axis with the minimum value. For example, if the x element is the minimum, each vertex is projected on the x-axis based on the center of the block, and projected on the (y, z) plane. If the projected value on the (y, z) plane is (ai, bi), θ is obtained through atan2(bi, ai), and vertices are aligned based on the θ value. The table below shows a combination of vertices for generating a triangle according to the number of vertices. Vertices are ordered from 1 to n. The table below shows that for four vertices, two triangles may be formed according to a combination of vertices. The first triangle may be composed of 1st, 2nd, and 3rd vertices among the aligned vertices, and the second triangle may be composed of 3rd, 4th, and 1st vertices among the aligned vertices. .

Triangles formed from vertices ordered 1,…

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 edge of the triangle. Additional points are created based on the upsampling factor and the width of the block. The additional point is called a refined vertice. The point cloud encoder according to embodiments may voxelize refined vertices. In addition, the point cloud encoder may perform attribute encoding based on the voxelized position (or position value).

7 shows an example of a neighbor node pattern according to embodiments.

In order to increase the compression efficiency of the point cloud video, the point cloud encoder according to the embodiments may perform entropy coding based on context adaptive arithmetic coding.

As described in FIGS. 1 to 6, a point cloud content providing system or a point cloud encoder (for example, a point cloud video encoder 10002, a point cloud encoder or an Arismatic encoder 40004 of FIG. 4) directly converts the Ocufanshi code. Entropy coding is possible. In addition, the point cloud content providing system or point cloud encoder performs entropy encoding (intra encoding) based on the ocupancy code of the current node and the ocupancy of neighboring nodes, or entropy encoding (inter encoding) based on the ocupancy code of the previous frame. ) Can be performed. A frame according to embodiments means a set of point cloud videos generated at the same time. The compression efficiency of intra-encoding/inter-encoding according to embodiments may vary depending on the number of referenced neighbor nodes. The larger the bit, the more complicated it is, but it can be skewed to one side, increasing the compression efficiency. For example, if you have a 3-bit context, you have to code in 8 ways. The divided coding part affects the complexity of the implementation. Therefore, it is necessary to match the appropriate level of compression efficiency and complexity.

7 shows a process of obtaining an ocupancy pattern based on the ocupancy of neighboring nodes. A point cloud encoder according to embodiments determines occupancy of neighboring nodes of each node of an octree and obtains a value of a neighbor pattern. The neighboring node pattern is used to infer the occupancy pattern of the corresponding node. The left side of FIG. 7 shows a cube corresponding to a node (centered cube) and six cubes (neighbor nodes) that share at least one surface with the cube. Nodes shown in the figure are nodes of the same depth (depth). Numbers shown in the figure indicate weights (1, 2, 4, 8, 16, 32, etc.) associated with each of the six nodes. Each weight is sequentially assigned according to the positions of neighboring nodes.

The right side of FIG. 7 shows neighboring node pattern values. The neighbor node pattern value is the sum of values multiplied by weights of the occupied neighbor nodes (neighbor nodes having points). Therefore, the neighbor node pattern value has a value from 0 to 63. When the neighbor node pattern value is 0, it indicates that no node (occupied node) has a point among neighboring nodes of the corresponding node. If the neighboring node pattern value is 63, it indicates that all neighboring nodes are occupied nodes. As shown in the figure, since neighboring nodes to which weights 1, 2, 4, and 8 are assigned are occupied nodes, the neighboring node pattern value is 15, which is the sum of 1, 2, 4, and 8. The point cloud encoder may perform coding according to the neighboring node pattern value (for example, if the neighboring node pattern value is 63, 64 codings are performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing a neighbor node pattern value (for example, based on a table changing 64 to 10 or 6).

8 shows an example of a point configuration for each LOD according to embodiments.

As described with reference to FIGS. 1 through 7, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the placement of the direct coded points (eg, placing the direct coded points in front of the point cloud data). When trisoup geometry encoding is applied, the geometry reconstruction process is triangular reconstruction, upsampling, voxelization, and the attribute is dependent on geometry, so the attribute encoding is performed based on the reconstructed geometry.

The point cloud encoder (for example, the LOD generator 40009) may reorganize points for each LOD. The figure shows point cloud content corresponding to the LOD. The left side of the figure shows the original point cloud content. The second figure from the left of the figure shows the distribution of the lowest LOD points, and the rightmost figure in the figure shows the distribution of the highest LOD points. 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 according to the direction of the arrow indicated at the bottom of the drawing, the spacing (or distance) between points becomes shorter.

9 shows an example of a point configuration for each LOD according to embodiments.

As described in FIGS. 1 to 8, a point cloud content providing system or a point cloud encoder (for example, a point cloud video encoder 10002, a point cloud encoder in FIG. 4, or an LOD generator 40009) generates an LOD. can do. The LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distance). The LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of point cloud content distributed in a three-dimensional space. The original order of FIG. 9 represents the order of points P0 to P9 before LOD generation. The LOD based order of FIG. 9 represents the order of points according to LOD generation. Points are rearranged by LOD. Also, the high LOD includes points belonging to the low LOD. As shown in FIG. 9, LOD0 includes P0, P5, P4 and P2. LOD1 includes the points of LOD0 and P1, P6 and P3. LOD2 includes points of LOD0, points of LOD1 and P9, P8 and P7.

As described with reference to FIG. 4, the point cloud encoder according to embodiments may selectively or combine predictive transform coding, lifting transform coding, and RAHT transform coding.

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

The predicted attribute (or attribute value) according to the embodiments is a weight calculated based on the distance to each neighboring point to the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point. It is set as the average value multiplied by (or weight value). A point cloud encoder according to embodiments (e.g., the coefficient quantization unit 40011) subtracts a predicted attribute (attribute value) from an attribute (attribute value) of each point, residuals (residuals, residual attributes, residual attribute values, attributes) It can be called a prediction residual value, etc.) can be quantized and inverse quantized The quantization process is as shown in the following table.

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);

}

}

Attribute prediction residuals inverse quantization pseudo code

int PCCInverseQuantization(int value, int quantStep) {

if( quantStep ==0) {

return value;

} else {

return value * quantStep;

}

}

The point cloud encoder (for example, the arithmetic encoder 40012) according to the embodiments may entropy-code the quantized and dequantized residual values as described above when there are points adjacent to the predictors of each point. The point cloud encoder (for example, the arithmetic encoder 40012) according to embodiments may entropy-code the attributes of the corresponding point without performing the above-described process if there are no points adjacent to the predictor of each point.

The point cloud encoder (for example, the lifting transform unit 40010) according to the embodiments generates a predictor of each point, sets the calculated LOD to the predictor, registers neighboring points, and increases the distance to the neighboring points. Lifting transform coding can be performed by setting weights. Lifting transform coding according to embodiments is similar to the above-described predictive transform coding, but differs in that a weight is accumulated and applied to an attribute value. A process of cumulatively applying a weight to an attribute value according to embodiments is as follows.

1) Create an array QW (Quantization Wieght) that stores the weight value of each point. The initial value of all elements of QW is 1.0. The QW value of the predictor index of the neighboring node registered in the predictor is multiplied by the weight of the predictor of the current point.

2) Lift prediction process: In order to calculate the predicted attribute value, the value obtained by multiplying the attribute value of the point by the weight is subtracted from the existing attribute value.

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

4) The weights calculated by additionally multiplying the weights calculated for all predictors by the weights stored in the QW corresponding to the predictor indexes are cumulatively added to the update weight array by the indexes of neighboring nodes. In the update array, the value obtained by multiplying the calculated weight by the attribute value of the index of the neighboring node is accumulated and summed.

5) Lift update process: For all predictors, the attribute value of the update array is divided by the weight value of the update weight array of the predictor index, and the existing attribute value is added to the divided value.

6) For all predictors, the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight updated through the lift prediction process (stored in QW). A point cloud encoder (for example, the coefficient quantization unit 40011) according to embodiments quantizes a predicted attribute value. In addition, the point cloud encoder (for example, the Arismatic encoder 40012) entropy-codes the quantized attribute values.

The point cloud encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding that estimates the attributes of higher-level nodes by using an attribute associated with a node at a lower level of the octree. . RAHT transform coding is an example of attribute intra coding through octree backward scan. The point cloud encoder according to the embodiments scans from voxels to the entire area, and repeats the merging process up to the root node while merging the voxels into larger blocks in each step. The merging process according to the embodiments is performed only for an occupied node. The merging process is not performed for the empty node, and the merging process is performed for the node immediately above the empty node.

The gDC value is also quantized and entropy coded like the high pass coefficient.

10 shows an example of a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1, and may perform the same or similar operation as that of the point cloud video decoder 10006 described in FIG. 1. As shown in the figure, the point cloud decoder may receive a geometry bitstream and an attribute bitstream included in one or more bitstreams. The point cloud decoder includes a geometry decoder and an attribute decoder. The geometry decoder performs geometry decoding on the geometry bitstream and outputs decoded geometry. The attribute decoder outputs decoded attributes by performing attribute decoding on the basis of the decoded geometry and the attribute bitstream. The decoded geometry and decoded attributes are used to reconstruct the point cloud content.

11 shows an example of a point cloud decoder according to embodiments.

The point cloud decoder illustrated in FIG. 11 is an example of the point cloud decoder described in FIG. 10, and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud encoder described in FIGS. 1 to 9.

As described in FIGS. 1 and 10, the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed prior to attribute decoding.

The point cloud decoder according to the embodiments includes an arithmetic decoder (11000), an octree synthesis unit (synthesize octree, 11001), a surface optimization synthesis unit (synthesize surface approximation, 11002), and a geometry reconstruction unit (reconstruct geometry). , 11003), inverse transform coordinates (11004), arithmetic decode (11005), inverse quantize (11006), RAHT transform unit (11007), LOD generator (generate LOD, 11008) ), Inverse lifting (11009), and/or inverse transform colors (11010).

The arithmetic decoder 11000, the octree synthesis unit 11001, the surface opoxidation synthesis unit 11002, the geometry reconstruction unit 11003, and the coordinate system inverse transform unit 11004 may perform geometry decoding. Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are optionally applied. Further, the geometry decoding is not limited to the above example, and is performed in the reverse process of the geometry encoding described in FIGS. 1 to 9.

The Arismatic decoder 11000 according to embodiments decodes the received geometry bitstream based on Arismatic coding. The operation of the Arismatic decoder 11000 corresponds to the reverse process of the Arismatic encoder 40004.

The octree synthesizer 11001 according to the embodiments may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding). A detailed description of the OQFancy code is as described in FIGS. 1 to 9.

When trisoup geometry encoding is applied, the surface opoxidation synthesizer 11002 according to embodiments may synthesize a surface based on the decoded geometry and/or the generated octree.

The geometry reconstruction unit 11003 according to the embodiments may regenerate the geometry based on the surface and/or the decoded geometry. 1 to 9, direct coding and trisoup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly imports and adds position information of points to which direct coding is applied. In addition, when trisoup geometry encoding is applied, the geometry reconstruction unit 11003 performs a reconstruction operation of the geometry reconstruction unit 40005, such as triangle reconstruction, up-sampling, and voxelization, to restore the geometry. have. Details are the same as those described in FIG. 6 and thus will be omitted. The reconstructed geometry may include a point cloud picture or frame that does not include attributes.

The coordinate system inverse transform unit 11004 according to embodiments may acquire positions of points by transforming a coordinate system based on the restored geometry.

Arithmetic decoder 11005, inverse quantization unit 11006, RAHT conversion unit 11007, LOD generation unit 11008, inverse lifting unit 11009, and/or color inverse conversion unit 11010 are attributes described in FIG. Decoding can be performed. Attribute decoding according to embodiments includes Region Adaptive Hierarchial Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting. step (Lifting Transform)) decoding may be included. The above three decodings may be used selectively, or a combination of one or more decodings may be used. In addition, attribute decoding according to embodiments is not limited to the above-described example.

The Arismatic decoder 11005 according to the embodiments decodes the attribute bitstream by arithmetic coding.

The inverse quantization unit 11006 according to embodiments inverse quantizes information on the decoded attribute bitstream or the attribute obtained as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

According to embodiments, the RAHT conversion unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009 may process reconstructed geometry and inverse quantized attributes. As described above, the RAHT conversion unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may selectively perform a decoding operation corresponding thereto according to the encoding of the point cloud encoder.

The inverse color transform unit 11010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes. The operation of the color inverse transform unit 11010 may be selectively performed based on the operation of the color transform unit 40006 of the point cloud encoder.

Although elements of the point cloud decoder of FIG. 11 are not shown in the drawing, hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus , Software, firmware, or a combination thereof. 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. 11 described above. Further, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of elements of the point cloud decoder of FIG. 11.

12 is an example of a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or a point cloud encoder of FIG. 4 ). The transmission device illustrated in FIG. 12 may perform at least one or more of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in FIGS. 1 to 9. The transmission apparatus according to the embodiments includes a data input unit 12000, a quantization processing unit 12001, a voxelization processing unit 12002, an octree occupancy code generation unit 12003, a surface model processing unit 12004, an intra/ Inter-coding processing unit (12005), Arithmetic coder (12006), metadata processing unit (12007), color conversion processing unit (12008), attribute transformation processing unit (or attribute transformation processing unit) (12009), prediction/lifting/RAHT transformation A processing unit 12010, an Arithmetic coder 12011, and/or a transmission processing unit 12012 may be included.

The data input unit 12000 according to the embodiments receives or acquires point cloud data. The data input unit 12000 may perform the same or similar operation and/or an acquisition method to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described in FIG. 2 ).

Data input unit 12000, quantization processing unit 12001, voxelization processing unit 12002, Occupancy code generation unit 12003, surface model processing unit 12004, intra/inter coding processing unit 12005, Arithmetic The coder 12006 performs geometry encoding. The geometry encoding according to the embodiments is the same as or similar to the geometry encoding described in FIGS. 1 to 9, so a detailed description thereof will be omitted.

The quantization processing unit 12001 according to embodiments quantizes geometry (eg, a position value or position value of points). The operation and/or quantization of the quantization processor 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The voxelization processor 12002 according to embodiments voxelsizes the position values of the quantized points. The voxelization processor 120002 may perform the same or similar operation and/or process as the operation and/or the voxelization process of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The octree occupancy code generation unit 12003 according to embodiments performs octree coding on positions of voxelized points based on an octree structure. The octree ocupancy code generation unit 12003 may generate an ocupancy code. The octree occupancy code generation unit 12003 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (or octree analysis unit 40002) described in FIGS. 4 and 6. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The surface model processing unit 12004 according to embodiments may perform trisoup geometry encoding to reconstruct positions of points within a specific area (or node) based on a voxel based on a surface model. The face model processing unit 12004 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (eg, the surface aproxiation analysis unit 40003) described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.

The intra/inter coding processor 12005 according to embodiments may intra/inter code point cloud data. The intra/inter coding processing unit 12005 may perform the same or similar coding as the intra/inter coding described in FIG. 7. The detailed description is the same as described in FIG. 7. According to embodiments, the intra/inter coding processing unit 12005 may be included in the arithmetic coder 12006.

The arithmetic coder 12006 according to embodiments entropy encodes an octree and/or an approximated octree of point cloud data. For example, the encoding method includes an Arithmetic encoding method. . The arithmetic coder 12006 performs the same or similar operation and/or method to the operation and/or method of the arithmetic encoder 40004.

The metadata processing unit 12007 according to embodiments processes metadata related to point cloud data, for example, a set value, and provides it to a necessary processing such as geometry encoding and/or attribute encoding. In addition, the metadata processing unit 12007 according to embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. Signaling information according to embodiments may be encoded separately from geometry encoding and/or attribute encoding. In addition, signaling information according to embodiments may be interleaved.

The color conversion processing unit 12008, the attribute conversion processing unit 12009, the prediction/lifting/RAHT conversion processing unit 12010, and the Arithmetic coder 12011 perform attribute encoding. Attribute encoding according to embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 9, and thus a detailed description thereof will be omitted.

The color conversion processing unit 12008 according to embodiments performs color conversion coding that converts color values included in attributes. The color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry. Description of the reconstructed geometry is the same as described in FIGS. 1 to 9. In addition, the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described in FIG. 4 is performed. Detailed description will be omitted.

The attribute conversion processing unit 12009 according to embodiments performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed. The attribute conversion processing unit 12009 performs the same or similar operation and/or method to the operation and/or method of the attribute conversion unit 40007 described in FIG. 4. Detailed description will be omitted. The prediction/lifting/RAHT transform processing unit 12010 according to embodiments may code transformed attributes by using any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding. The prediction/lifting/RAHT conversion processing unit 12010 performs at least one of the same or similar operations as the RAHT conversion unit 40008, LOD generation unit 40009, and lifting conversion unit 40010 described in FIG. 4. do. In addition, descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described in FIGS.

The Arismatic coder 12011 according to embodiments may encode coded attributes based on Arismatic coding. The Arismatic coder 12011 performs the same or similar operation and/or method to the operation and/or method of the Arismatic encoder 400012.

The transmission processor 12012 according to the embodiments transmits each bitstream including the encoded geometry and/or the encoded attribute, and metadata information, or transmits the encoded geometry and/or the encoded attribute, and the metadata information in one piece. It can be configured as a bitstream and transmitted. When the encoded geometry and/or encoded attribute and metadata information according to the embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments is a sequence parameter set (SPS) for signaling of a 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. It may include signaling information including TPS (Tile Parameter Set) for level signaling and slice data. Slice data may include information on one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0 ⁰ ) and one or more attribute bitstreams (Attr0 ⁰ and Attr1 ⁰ ). The TPS according to the embodiments may include information about each tile (eg, coordinate value information and height/size information of a bounding box) with respect to one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), information on data included in the payload, and the like. I can. As described above, the metadata processing unit 12007 according to the embodiments may generate and/or process signaling information and transmit the generated signaling information to the transmission processing unit 12012. Depending on embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data/information with each other as dotted line processing. The transmission processor 12012 according to the embodiments may perform the same or similar operation and/or a transmission method as the operation and/or transmission method of the transmitter 10003. Detailed descriptions are the same as those described in FIGS.

13 is an example of a reception device according to embodiments.

The receiving device illustrated in FIG. 13 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. 13 may perform at least one or more of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.

The receiving apparatus according to the embodiments includes a receiving unit 13000, a receiving processing unit 13001, an arithmetic decoder 13002, an octree reconstruction processing unit 13003 based on an Occupancy code, and a surface model processing unit (triangle reconstruction). , Up-sampling, voxelization) (13004), inverse quantization processing unit (13005), metadata parser (13006), arithmetic decoder (13007), inverse quantization processing unit (13008), prediction A /lifting/RAHT inverse transformation processing unit 13009, a color inverse transformation processing unit 13010, and/or a renderer 13011 may be included. Each component of decoding according to the embodiments may perform a reverse process of the component of encoding according to the embodiments.

The receiving unit 13000 according to the embodiments receives point cloud data. The receiving unit 13000 may perform the same or similar operation and/or a receiving method as the operation and/or receiving method of the receiver 10005 of FIG. 1. Detailed description will be omitted.

The reception processing unit 13001 according to embodiments may obtain a geometry bitstream and/or an attribute bitstream from received data. The reception processing unit 13001 may be included in the reception unit 13000.

The arithmetic decoder 13002, the ocupancy code-based octree reconstruction processing unit 13003, the surface model processing unit 13004, and the inverse quantization processing unit 13005 may 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 will be omitted.

The Arismatic decoder 13002 according to embodiments may decode a geometry bitstream based on Arismatic coding. The Arismatic decoder 13002 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic decoder 11000.

The ocupancy code-based octree reconstruction processing unit 13003 according to the embodiments may obtain an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding) to reconstruct the octree. The ocupancy code-based octree reconstruction processing unit 13003 performs the same or similar operation and/or method as the operation and/or the octree generation method of the octree synthesis unit 11001. When the trisoup geometry encoding is applied, the surface model processing unit 13004 according to the embodiments decodes the trisoup geometry based on the surface model method and reconstructs the related geometry (e.g., triangle reconstruction, up-sampling, voxelization). Can be done. The surface model processing unit 13004 performs an operation identical or similar to that of the surface opoxidation synthesis unit 11002 and/or the geometry reconstruction unit 11003.

The inverse quantization processing unit 13005 according to embodiments may inverse quantize the decoded geometry.

The metadata parser 13006 according to embodiments may parse metadata included in the received point cloud data, for example, a setting value. The metadata parser 13006 may pass metadata to geometry decoding and/or attribute decoding. The detailed description of the metadata is the same as that described in FIG. 12 and thus will be omitted.

The arithmetic decoder 13007, the inverse quantization processing unit 13008, the prediction/lifting/RAHT inverse transformation processing unit 13009, and the color inverse transformation processing unit 13010 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 will be omitted.

The Arismatic decoder 13007 according to the embodiments may decode the attribute bitstream through Arismatic coding. The arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry. The Arismatic decoder 13007 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic decoder 11005.

The inverse quantization processing unit 13008 according to embodiments may inverse quantize the decoded attribute bitstream. The inverse quantization processing unit 13008 performs the same or similar operation and/or method as the operation and/or the inverse quantization method of the inverse quantization unit 11006.

The prediction/lifting/RAHT inverse transform processing unit 13009 according to embodiments may process reconstructed geometry and inverse quantized attributes. The prediction/lifting/RAHT inverse transform processing unit 13009 is the same or similar to the operations and/or decodings of the RAHT transform unit 11007, the LOD generator 11008 and/or the inverse lifting unit 11009, and/or At least one or more of the decodings is performed. The color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes. The color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding as the operation and/or inverse transform coding of the color inverse transform unit 11010. The renderer 13011 according to embodiments may render point cloud data.

14 illustrates an architecture for G-PCC-based point cloud content streaming according to embodiments.

The upper part of FIG. 14 shows a process in which the transmission device (for example, the transmission device 10000, the transmission device of FIG. 12, etc.) described in FIGS. 1 to 13 processes and transmits the point cloud content.

As described with reference to FIGS. 1 to 13, the transmission device may obtain audio Ba of the point cloud content (Audio Acquisition), encode the acquired audio, and output audio bitstreams Ea. In addition, the transmission device acquires a point cloud (Bv) (or point cloud video) of the point cloud content (Point Acqusition), performs point cloud encoding on the acquired point cloud, and performs a point cloud video bitstream ( Eb) can be output. The point cloud encoding of the transmission device is the same as or similar to the point cloud encoding (for example, the encoding of the point cloud encoder of FIG. 4) described in FIGS.

The transmission device may encapsulate the generated audio bitstreams and video bitstreams into files and/or segments (File/segment encapsulation). The encapsulated file and/or segment (Fs, File) may include a file of a file format such as ISOBMFF or a DASH segment. Point cloud-related metadata according to embodiments may be included in an encapsulated file format and/or segment. Meta data may be included in boxes of various levels in the ISOBMFF file format or may be included in separate tracks in the file. According to an embodiment, the transmission device may encapsulate the metadata itself as a separate file. The transmission device according to the embodiments may deliver the encapsulated file format and/or segment through a network. Since the encapsulation and transmission processing method of the transmission device is the same as those described in FIGS. 1 to 13 (for example, the transmitter 10003, the transmission step 20002 of FIG. 2, etc.), detailed descriptions are omitted.

The bottom of FIG. 14 shows a process of processing and outputting point cloud content by the receiving device (for example, the receiving device 10004, the receiving device of FIG. 13, etc.) described in FIGS. 1 to 13.

According to embodiments, the receiving device includes a device that outputs final audio data and final video data (e.g., loudspeakers, headphones, display), and a point cloud player that processes point cloud content ( Point Cloud Player). The final data output device and the point cloud player may be configured as separate physical devices. The point cloud player according to the embodiments may perform Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding and/or next-generation coding.

The receiving device according to the embodiments secures a file and/or segment (F', Fs') included in the received data (for example, a broadcast signal, a signal transmitted through a network, etc.) and decapsulation (File/ segment decapsulation). Since the reception and decapsulation method of the reception device is the same as those described in FIGS. 1 to 13 (for example, the receiver 10005, the reception unit 13000, the reception processing unit 13001, etc.), a detailed description thereof will be omitted.

The receiving device according to the embodiments secures an audio bitstream E'a and a video bitstream E'v included in a file and/or segment. As shown in the drawing, the receiving device outputs the decoded audio data B'a by performing audio decoding on the audio bitstream, and rendering the decoded audio data to final audio data. (A'a) is output through speakers or headphones.

In addition, the receiving device outputs decoded video data B'v by performing point cloud decoding on the video bitstream E'v. Since the point cloud decoding according to the embodiments is the same as or similar to the point cloud decoding described in FIGS. 1 to 13 (for example, decoding of the point cloud decoder of FIG. 11 ), a detailed description will be omitted. The receiving device may render the decoded video data and output the final video data through the display.

The receiving device according to the embodiments may perform at least one of decapsulation, audio decoding, audio rendering, point cloud decoding, and rendering operations based on metadata transmitted together. The description of the metadata is the same as that described with reference to FIGS. 12 to 13 and thus will be omitted.

As shown in the dotted line shown in the drawing, the receiving device according to the embodiments (for example, a point cloud player or a sensing/tracking unit (sensing/tracking) in a point cloud flare) may generate feedback information (orientation, viewport). . The feedback information according to the embodiments may be used in a decapsulation process, a point cloud decoding process and/or a rendering process of a receiving device, or may be transmitted to a transmitting device. The description of the feedback information is the same as that described with reference to FIGS. 1 to 13 and thus will be omitted.

15 shows an example of a transmission device according to embodiments.

The transmission device of FIG. 15 is a device that transmits point cloud content, and the transmission device described in FIGS. 1 to 14 (for example, the transmission device 10000 of FIG. 1, the point cloud encoder of FIG. 4, the transmission device of FIG. 12, 14). Accordingly, the transmission device of FIG. 15 performs the same or similar operation to that of the transmission device described in FIGS. 1 to 14.

The transmission device according to embodiments may perform at least one or more of point cloud acquisition, point cloud encoding, file/segment encapsulation, and delivery. Can be done.

Since the operation of acquiring and transmitting the point cloud shown in the drawing is the same as described in FIGS. 1 to 14, detailed descriptions are omitted.

As described with reference to FIGS. 1 to 14, the transmission device according to embodiments may perform geometry encoding and attribute encoding. Geometry encoding according to embodiments may be referred to as geometry compression, and attribute encoding may be referred to as attribute compression. As described above, one point may have one geometry and one or more attributes. Therefore, the transmission device performs attribute encoding for each attribute. The drawing shows an example in which a transmission device has performed one or more attribute compressions (attribute #1 compression, ...attribute #N compression). In addition, the transmission apparatus according to the embodiments may perform auxiliary compression. Additional compression is performed on the metadata. Description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted. In addition, the transmission device may perform mesh data compression. Mesh data compression according to embodiments may include the trisoup geometry encoding described in FIGS. 1 to 14.

The transmission device according to the embodiments may encapsulate bitstreams (eg, point cloud streams) output according to point cloud encoding into files and/or segments. According to embodiments, a transmission device performs media track encapsulation for carrying data other than metadata (for example, media data), and metadata tracak for carrying meta data. encapsulation) can be performed. According to embodiments, metadata may be encapsulated as a media track.

1 to 14, the transmitting device receives feedback information (orientation/viewport metadata) from the receiving device, and based on the received feedback information, at least one of point cloud encoding, file/segment encapsulation, and transmission operations. Any one or more can be performed. Detailed descriptions are the same as those described in FIGS. 1 to 14, and thus will be omitted.

16 shows an example of a receiving device according to embodiments.

The receiving device of FIG. 16 is a device that receives point cloud content, and the receiving device described in FIGS. 1 to 14 (for example, the receiving device 10004 of FIG. 1, the point cloud decoder of FIG. 11, the receiving device of FIG. 13, 14). Accordingly, the receiving device of FIG. 16 performs the same or similar operation to that of the receiving device described in FIGS. 1 to 14. In addition, the receiving device of FIG. 16 may receive a signal transmitted from the transmitting device of FIG. 15, and may perform a reverse process of the operation of the transmitting device of FIG. 15.

The receiving device according to embodiments may perform at least one or more of delivery, file/segement decapsulation, point cloud decoding, and point cloud rendering. Can be done.

Since the point cloud reception and point cloud rendering operations shown in the drawings are the same as those described in FIGS. 1 to 14, detailed descriptions are omitted.

As described with reference to FIGS. 1 to 14, the reception device according to the embodiments performs decapsulation on a file and/or segment acquired from a network or a storage device. According to embodiments, the receiving device performs media track decapsulation carrying data other than meta data (for example, media data), and metadata track decapsulation carrying meta data. decapsulation) can be performed. According to embodiments, when metadata is encapsulated into a media track, the metadata track decapsulation is omitted.

As described with reference to FIGS. 1 to 14, the receiving device may perform geometry decoding and attribute decoding on bitstreams (eg, point cloud streams) secured through decapsulation. Geometry decoding according to embodiments may be referred to as geometry decompression, and attribute decoding may be referred to as attribute decompression. As described above, a point may have one geometry and one or more attributes, and are each encoded. Therefore, the receiving device performs attribute decoding for each attribute. The drawing shows an example in which the receiving device performs one or more attribute decompressions (attribute #1 decompression, ...attribute #N decompression). In addition, the receiving device according to the embodiments may perform auxiliary decompression. Additional decompression is performed on the metadata. Description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted. Additionally, the receiving device may perform mesh data decompression. The mesh data decompression according to embodiments may include decoding the trisoup geometry described with reference to FIGS. 1 to 14. The receiving device according to the embodiments may render the output point cloud data according to the point cloud decoding.

As described in FIGS. 1 to 14, the receiving device secures orientation/viewport metadata using a separate sensing/tracking element, etc., and transmits feedback information including the same to a transmission device (for example, the transmission device of FIG. 15). Can be transmitted. In addition, the receiving device may perform at least one or more of a receiving operation, file/segment decapsulation, and point cloud decoding based on the feedback information. Detailed descriptions are the same as those described in FIGS. 1 to 14, and thus will be omitted.

17 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.

The structure of FIG. 17 includes at least one of a server 1760, a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770. A configuration connected to the cloud network 1710 is shown. The robot 1710, the autonomous vehicle 1720, the XR device 1730, the smartphone 1740, the home appliance 1750, and the like are referred to as devices. In addition, the XR device 1730 may correspond to a point cloud data (PCC) device according to embodiments or may be interlocked with a PCC device.

The cloud network 1700 may constitute a part of a cloud computing infrastructure or may mean a network that exists in the cloud computing infrastructure. Here, the cloud network 1700 may be configured using a 3G network, a 4G or long term evolution (LTE) network, or a 5G network.

The server 1760 includes at least one of a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770, and a cloud network 1700. The connected devices 1710 to 1770 may be connected through, and may assist at least part of the processing of the connected devices 1710 to 1770.

The HMD (Head-Mount Display) 1770 represents one of types in which an XR device and/or a PCC device according to embodiments may be implemented. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I/O unit, a sensor unit, and a power supply unit.

Hereinafter, various embodiments of the devices 1710 to 1750 to which the above-described technology is applied will be described. Here, the devices 1710 to 1750 shown in FIG. 17 may be interlocked/coupled with the point cloud data transmission/reception apparatus according to the above-described embodiments.

<PCC+XR>

The XR/PCC device 1730 is applied with PCC and/or XR (AR+VR) technology to provide a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, It may be implemented as a computer, wearable device, home appliance, digital signage, vehicle, fixed robot or mobile robot.

The XR/PCC device 1730 analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for 3D points, thereby Information can be obtained, and the XR object to be output can be rendered and output. For example, the XR/PCC device 1730 may output an XR object including additional information on the recognized object in correspondence with the recognized object.

<PCC+Autonomous Driving+XR>

The autonomous vehicle 1720 may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle by applying PCC technology and XR technology.

The autonomous driving vehicle 1720 to which the XR/PCC technology is applied may refer to an autonomous driving vehicle having a means for providing an XR image, an autonomous driving vehicle that is an object of control/interaction within the XR image. In particular, the autonomous vehicle 1720, which is the object of control/interaction in the XR image, is distinguished from the XR device 1730 and may be interlocked with each other.

The autonomous vehicle 1720 having a means for providing an XR/PCC image may acquire sensor information from sensors including a camera, and may output an XR/PCC image generated based on the acquired sensor information. For example, the autonomous vehicle 1720 may provide an XR/PCC object corresponding to a real object or an object in a screen to the occupant by outputting an XR/PCC image with a HUD.

In this case, when the XR/PCC object is output to the HUD, at least a part of the XR/PCC object may be output to overlap the actual object facing the occupant's gaze. On the other hand, when the XR/PCC object is output on a display provided inside the autonomous vehicle, at least a part of the XR/PCC object may be output to overlap the object in the screen. For example, the autonomous vehicle 1220 may output XR/PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, and buildings.

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.

That is, VR technology is a display technology that provides objects or backgrounds in the real world only as CG images. On the other hand, AR technology refers to a technology that shows a virtually created CG image on a real object image. Furthermore, MR technology is similar to the AR technology described above in that virtual objects are mixed and combined in the real world. However, in AR technology, the distinction between real objects and virtual objects made from CG images is clear, and virtual objects are used in a form that complements the real objects, whereas in MR technology, the virtual objects are regarded as having the same characteristics as the real objects. It is distinct from technology. More specifically, for example, it is a hologram service to which the aforementioned MR technology is applied.

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

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

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

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

In the present specification, the method/device according to the embodiments refers to a point cloud data transmission/reception method and/or a point cloud data transmission/reception apparatus.

In this specification, geometric information may be referred to as geometry information, and attribute information may be referred to as attribute information.

In this specification, the point cloud refers to point cloud data.

In this specification, a method/apparatus according to embodiments for increasing the compression efficiency of attributes of Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data will be described.

The point cloud according to embodiments is data composed of a set of points. Each point may have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.) and/or reflection value. The G-PCC encoding process may consist of compressing geometry and compressing attribute information based on geometry reconstructed with positional information changed through compression (reconstructed geometry = decoded geometry). The G-PCC decoding process may consist of a process of receiving an encoded geometry bitstream and an attribute bitstream, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process. (Details are described below.)

In the process of compressing attribute information, a predictive transform technique, a lifting transform technique, or a RAHT technique is used. (Details are explained below)

In this specification, an encoder and/or encoder refers to an encoder, and a decoder and/or decoder refers to a decoder.

Referring to FIG. 8, the prediction transformation method and/or the lifting transformation method according to embodiments may divide and group points by level of detail (hereinafter referred to as LOD).

This is referred to as ①LOD generation process, and hereinafter, groups having different LODs may be referred to as LODl sets.

l represents the LOD and is an integer starting from 0. LOD0 is a set consisting of points with the largest distance between points, and as l increases, the distance between points belonging to LODl decreases.

After creating the LODl set, ② based on the LODl set, find X(>0) nearest neighbors in a group with the same or smaller LOD (the distance between nodes is large), and use the predictor to set the neighboring points. You can register with X is the maximum number that can be set as a neighboring point and can be input as a user parameter.

Referring to FIG. 9, a neighboring point of P3 belonging to LOD1 is found in LOD0 and LOD1 as shown in the following figure. The three nearest neighbor nodes can be P2 P4 P6. These three nodes are registered as a set of neighboring points to the predictor of P3.

Every point can have one predictor. The property is predicted from neighboring points registered in the predictor. The predictor may have a set of neighboring points and register it as ½ distance = weight based on a distance value from each neighboring point.

When the set of neighboring points of the predictor is set, the weights of each neighboring point can be normalized with the total sum of weights of the neighboring points.

④ The property can be predicted through the predictor. An average of a value obtained by multiplying the properties of registered neighboring points by a weight may be used as a predicted result, or a specific point may be used as a predicted result. Which method to use, after pre-calculating the compressed result value, you can select the method that can generate the smallest stream.

⑤ The attribute value of the point and the residual of the attribute value predicted by the predictor of the point can be signaled to the receiver by encoding it together with the method selected by the predictor.

In the decoder, the same process is performed from steps ① to ③, and in ④, the transmitted prediction method is decoded and attribute values can be predicted according to the method. By decoding the residual value transmitted from ⑤ and adding the predicted value through ④, the attribute value can be restored.

The method/apparatus according to the embodiments proposes a method for step ② described above, that is, a method of configuring a set of neighboring points, which can be applied to both a transmitter and a receiver. Since the configuration of the neighboring point set generates a prediction value based on the neighboring point set and signals the residual with the generated prediction value by encoding, the configuration of the neighboring point set can have a great influence on the attribute compression efficiency of the point cloud. Since the geometry-based nearby relationship of a point cloud can have similar properties, a neighboring set can be configured based on a distance value when predicted by a predictor, but may differ according to the characteristics of the point cloud content. Therefore, we propose an upgraded neighbor point set configuration method for better compression efficiency.

For example, when the category of point cloud data is 3, properties between neighboring points may not be similar (eg, when the distance between points is long). Accordingly, the method/apparatus according to the embodiments provides a method of selecting a point having similar attributes.

The method/apparatus according to the embodiments provides a method of generating a set of neighboring points based on geometric adjacency and similar attributes in order to increase attribute compression efficiency. This is to construct a set of neighboring points well, and thereby increase the attribute compression rate. The probability that a point having similar properties is in the neighborhood is high, and the probability that the predicted property and the residual of the point property through the neighboring points having similar properties are close to 0 is high, thereby increasing the compression rate of the value to be protected.

In addition, the method/apparatus according to the embodiments proposes a method of generating a neighboring point set based on geometric adjacency and similarity in order to increase compression efficiency by changing a neighboring point set configuration method in the G-PCC attribute encoding/decoding process.

The method/apparatus according to embodiments calculates a first candidate of a set of neighboring points based on distance, and sets a final candidate as a set of neighboring points based on similarity of attributes within the first candidate. Through this, the assumption that the probability that a point with similar attributes is in the neighborhood is highly likely to be filtered through the first-order candidate, and if the neighboring point is selected through the attribute similarity of the first-choice candidates, the probability that the residual between the predicted attribute and the point attribute is close to 0 Since is high, the compression rate of the value to be encoded can be increased.

A method of generating a set of neighboring points based on geometric proximity and similarity properties according to embodiments will be described.

The PCC attribute encoding/decoding according to the embodiments is performed in the PCC decoder/encoder according to the embodiments.

When a predictive transformation technique and/or a lifting transformation technique are used in the attribute encoding/decoding process, an LODl set may be generated, and a neighboring point set of the predictor may be generated based on the generated LODl set.

The method/apparatus according to the embodiments may select first order neighboring point candidates based on a distance to generate a neighboring point set of the predictor.

For example, the method/device according to the embodiments may be selected by multiplying the number of primary points based on distance. Among them, the similarity can be calculated using the following equation. Furthermore, it is possible to find neighboring points based on similarity. Here, a point with high similarity (similarity) can be selected as a neighboring point. According to embodiments, there is an effect of further supplementing similarities that may be missed by the distance-based method.

The maximum number of distance-based neighbor points according to embodiments may be signaled by a decoder/encoder as a user parameter.

The method/apparatus according to the embodiments performs attribute similarity verification based on the selected neighboring point candidates to finally select a neighboring point.

The maximum number of neighboring points selected as neighboring points according to embodiments may be signaled by a decoder/encoder using a user parameter.

To verify the attribute similarity, Euclidean Color Distance, Correlated Color Temperature, or CIE94 distance metric defined in CIE (Commission on Illumination) can be selectively used.

18 illustrates an example of an attribute information (attribute information) predictor according to embodiments.

The attribute information prediction unit 18000 according to the embodiments may include each component according to the embodiments as follows. Each component may be corresponded to by hardware, software and/or a processor.

It may be included in both the attribute information predictor encoder and the decoder. The attribute information prediction unit corresponds to a prediction/lifting conversion processing unit according to embodiments, and reference will be made to descriptions of prediction conversion according to embodiments and lifting conversion according to embodiments.

The neighbor point set configuration unit of the attribute information prediction unit may process embodiments.

The LOD configuration unit 18001 may configure the LOD using a method of configuring the LOD (lod_type) and transmit it to the decoder.

The LOD configuration unit 18001 may receive the reconstructed location information, configure the LOD in consideration of how many LOD sets (num_detail_levels_minus1) are to be configured, and transmit the LOD to the decoder.

The LOD configuration unit 18001 may determine at which level of the octree the LOD0 set configuration is to be performed (lod_0_depth), and transmit this to the decoder.

The LOD configuration unit 18001 may configure the LOD using a sampling rate (sampling_rate[i], 0≦i<≦num_detail_levels_minus1) for each LODl set and transmit it to the decoder.

The neighbor point set construction unit 18002 may select a nearby neighbor as a candidate based on distance information. By calculating the distance with the maximum number (num_pred_nearest_neighbours) that can be selected, the nearest neighbor point can be registered as a neighbor candidate. Neighbor point candidate construction based on geometry neighbor information is basically performed.

The neighbor point set configuration unit 18002 may set whether to check attribute similarity of selected neighbor point candidates based on the first geometry neighbor information through an attribute similarity-based neighbor point set configuration flag (attribute_similarity_enable_flag) and transmit it to the decoder.

When the neighbor point set configuration flag based on attribute similarity is set, the neighbor point set configuration unit 18002 may transmit the finally selected number of neighbor points (num_pred_neighbours) to the decoder.

The neighbor point set configuration unit 18002 may set a type (similarity_check_method_type) for which algorithm to apply for the attribute similarity check, and may be transmitted to the decoder. Neighboring points with similar attributes can be registered in the neighboring point set through values obtained through attribute similarity check.

Based on the set of neighboring points, the predictive transform/inverse transform unit 18003 and/or the lifting transform/inverse transform unit 18004 perform attribute encoding/decoding.

The attribute information prediction unit or the attribute information prediction unit according to the embodiments may be included in both a transmitter (encoder) and/or a receiver (decoder) according to the embodiments. In the case of the transmitting side, predictive transform and/or lifting transform are performed, and in the case of the receiving side, predictive inverse transform and/or lifting inverse transform are performed.

Details of the predictive transform/inverse transform unit and/or the lifting transform/inverse transform unit are as described above.

The method/apparatus according to the embodiments is for enhancing the attribute compression efficiency of an encoder (encoder)/decoder (decoder) of Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. As a solution, it provides the effect of generating a set of neighboring points based on the proximity and similarity of geometry.

Therefore, the method/apparatus according to the embodiments can improve the attribute compression efficiency of an encoder (encoder)/decoder (decoder) of Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. It can be increased to provide a smaller stream of point cloud content.

The encoding of the point cloud data transmission method according to the embodiments includes encoding attribute information for the point cloud data, and encoding the attribute information further includes predicting the attribute information.

The encoding of the attribute information of the point cloud data transmission method according to the embodiments further includes predicting the attribute information, and the predicting of the attribute information is based on the attribute information and geometry information reconstructed from the encoded geometry information. Generating a Level of Detail (LOD); Generating a set of neighboring points based on the LOD; And encoding attribute information based on a set of neighboring points. Includes.

The generating of the neighboring point set of the point cloud data transmission method according to the embodiments includes registering a nearest neighbor among points for LOD as a neighboring point set, wherein the neighboring point set is It is generated based on the similarity to the candidate of the neighboring point set from the candidate of the neighboring point set selected based on the distance.

The decoding of the method for receiving point cloud data according to embodiments includes decoding attribute information for the point cloud data, and decoding the attribute information further includes predicting the attribute information.

The decoding of the attribute information of the method for receiving point cloud data according to the embodiments further includes predicting the attribute information, and the predicting of the attribute information is based on the attribute information and the geometry information reconstructed from the decoded geometry information. Generating a Level of Detail (LOD); Generating a set of neighboring points based on the LOD; And decoding attribute information based on a set of neighboring points. Includes.

The generating of the neighboring point set of the point cloud data receiving method according to the embodiments includes registering a nearest neighbor among points for LOD as a neighboring point set, wherein the neighboring point set is It is generated based on the similarity to the candidate of the neighboring point set from the candidate of the neighboring point set selected based on the distance.

For example, the attribute information prediction unit or the LOD constructing unit determines the level of the octree for the LOD 0 set configuration according to the LOD depth. LOD 0 to LOD I can be generated.

The neighbor point set construction unit according to embodiments finds a neighbor from the generated LOD, and finds a neighbor to the object within the LOD group to which the object belongs and the LOD group having a low index.

The method/apparatus according to the embodiments is effective for reducing the time to find a neighbor. In particular, this is important because the neighbor point search range and the search method have an effect of reducing the complexity of the process, and signaling for the neighbor point search is also important.

In addition, since the types/types of point cloud data are diverse, in order to prevent the problem that the neighboring points are not actually close to each other, the method/device according to the embodiments considers both distance adjacency and/or attribute adjacency. Can provide.

For example, the method/apparatus according to the embodiments may primarily select neighboring point candidates by a predetermined number. Here, the number may be signaled based on signaling information described below.

Since the types of data vary according to the category of point cloud data, when a neighboring point is searched based on a distance, there may be a case where the nearest neighboring point is not accurately selected.

Accordingly, the method/apparatus according to the embodiments may secondarily select the nearest neighboring point as many as a certain number based on the similarity among the primarily selected candidates.

In other words, attribute coding and/or neighbor point configuration may take up a lot of process time because of high complexity in the entire encoding or decoding process. The method/apparatus according to the embodiments can solve this problem.

19 shows a configuration of an encoded point cloud according to embodiments.

The method/apparatus according to the embodiments may signal information for the embodiments.

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

Point cloud data according to embodiments may have a bitstream form as shown in the drawing. The point cloud data may include a sequence parameter set (SPS), a geometry parameter set (GPS), an attribute parameter set (APS), and a tile parameter set (TPS) including signaling information according to embodiments. Point cloud data may include one or more geometry and/or attributes. The point cloud data may include geometry and/or attributes in units of one or more slices. The geometry may have a structure of a geometry slice header and geometry slice data. For example, the TPS including signaling information is Tile(0). It may include tile_bounding_box_xyz0, Tile(0)_tile_bounding_box_whd, and the like. The geometry may include geom_geom_parameter_set_id, geom_tile_id, geom_slice_id, geomBoxOrigin, geom_box_log2_scale, geom_max_node_size_log2, geom_num_points, and the like.

Signaling information according to embodiments may be signaled in addition to SPS, GPS, APS, TPS, and the like.

Depending on embodiments, the signaling information may be signaled by being added to the TPS or Geom for each Slice or Attr for each Slice.

The structure of the point cloud data according to the embodiments may provide an efficient effect in terms of encoding/decoding/data accessing parameter set(s), geometry(s), and attribute(s) including signaling information.

Point cloud data related to the point cloud data transmitting/receiving apparatus according to embodiments may include at least one of a sequence parameter, a geometry parameter, an attribute parameter, a tile parameter, a geometry bitstream, or an attribute bitstream.

20 illustrates an example of information related to an APS neighbor point set generation option according to embodiments.

In this specification, APS means Attribute Parameter Set, and may be referred to as signaling information, metadata, parameters, and the like.

The method/apparatus according to the embodiments may signal by adding information related to the neighbor point set generation option to the APS. A description of each field according to embodiments is as follows.

Attribute_similarity_enable_flag: Indicates whether to refer to attribute similarity when generating a neighboring point set.

num_pred_neighbours: Indicates the maximum number of neighboring points registered in the predictor when referring to attribute similarity.

Similarity_check_method_type: Indicates the attribute similarity check method when referring to attribute similarity.

1=Euclidian color distance

2=Correlated Color Temperature

3= CIE94

4~ =reserved for future usage

aps_attr_parameter_set_id: Represents an identifier for an APS for reference by other syntax elements. The value of aps_attr_parameter_set_id may be in the range of 0 to 15 (inclusive).

aps_seq_parameter_set_id represents the value of sps_seq_parameter_set_id for active SPS. The value of aps_seq_parameter_set_id may be in the range of 0 to 15 (inclusive).

attr_coding_type represents a coding type for an attribute in a table for a given value of attr_coding_type. The value of attr_coding_type may be the same as 0, 1, or 2 in bitstreams according to the version of the present specification. Other values of attr_coding_type may be reserved for future use of ISO/IEC. Decoders conforming to the version of the present specification may ignore reserved values of attr_coding_type. Specifically, each value can be expressed as follows: 0 = Predicting weight lifting), 1 = Region Adaptive Hierarchical Transferm (RAHT), 2= Fixed weight lifting

num_pred_nearest_neighbours represents the maximum number of nearby neighbors used for prediction. The value of numberOfNearestNeighboursInPrediction may be in the range of 1 to xx.

max_num_direct_predictors represents the maximum number of predictors used for direct prediction. The value of max_num_direct_predictors may be in the range of 0 to num_pred_nearest_neighbours. The value of the variable MaxNumPredictors is used in the decoding process and is as follows:

MaxNumPredictors = max_num_direct_predicots + 1

lifting_search_range represents the search range for lifting.

lifting_quant_step_size represents the quantization step size for the 1 ^st component of the attribute. The value of quant_step_size may be in the range of 1 to xx.

lifting_quant_step_size_chroma indicates the quantization step size for the chroma component of the attribute when the attribute is color. The value of quant_step_size_chroma may be in the range of 1 to xx.

lod_binary_tree_enabled_flag indicates whether or not the binary tree is enabled for log generation.

num_detail_levels_minus1 represents the number of levels of details for attribute coding. The value of num_detail_levels_minus1 may be in the range of 0 to xx.

sampling_distance_squared [idx] represents the square of the sampling distance for idx. The value of sampling_distance_squared[] may be in the range of 0 to xx.

adaptive_prediction_threshold represents the threshold of the prediction.

raht_depth represents the number of level of details for RAHT. The value of depthRAHT may be in the range of 1 to xx.

raht_binarylevel_threshold represents the level of detail for cutting out the RAHT coefficient. The value of binaryLevelThresholdRAHT may be in the range of 0 to xx.

raht_quant_step_size represents the quantization step size for the 1 ^st component of the attribute. The value of quant_step_size may be in the range of 1 to xx.

When aps_extension_present_flag is 1, it indicates that the aps_extension_data syntax structure exists in the APS RBSP syntax structure. When aps_extension_present_flag is 0, it indicates that this syntax structure does not exist. If not present, the value of aps_extension_present_flag may be interpreted as 0.

aps_extension_data_flag can have any value. Its presence and value do not affect the decoder following a particular profile. Decoders can follow a specific profile.

21 illustrates an example of information related to a neighbor point set generation option of a TPS according to embodiments.

In this specification, TPS means Tile Parameter Set, and may be referred to as signaling information, metadata, and parameters.

The method/apparatus according to the embodiments may signal by adding information related to the neighbor point set generation option to the TPS. A description of each field according to embodiments is as follows.

num_pred_nearest_neighbours represents the maximum number of near neighbors used for prediction. The value of numberOfNearestNeighboursInPrediction may be in the range of 1 to xx.

num_tiles represents the number of tiles signaled for the bitstream. If not present, num_tiles may be interpreted as 0.

tile_bounding_box_offset_x[ i] represents the x offset of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the number of tile_bounding_box_offset_x[ 0] can be inferred as sps_bounding_box_offset_x.

tile_bounding_box_offset_y[i] represents the y offset of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_offset_y[ 0] can be inferred as sps_bounding_box_offset_y.

tile_bounding_box_offset_z[ i] represents the z offset of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_offset_z[ 0] can be inferred as sps_bounding_box_offset_z.

tile_bounding_box_scale_factor[ i] represents the scale factor of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_scale_factor[ 0] can be inferred as sps_bounding_box_scale_factor.

tile_bounding_box_size_width[ i] represents the width of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_size_width[ 0] can be inferred as sps_bounding_box_size_width.

tile_bounding_box_size_height[ i] represents the height of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_size_height[ 0] can be inferred as sps_bounding_box_size_height.

tile_bounding_box_size_depth[ i] represents the depth of the i-th tile in the coordinate system (e.g. Cartesian). If not present, the value of tile_bounding_box_size_depth[ 0] can be inferred as sps_bounding_box_size_depth.

attribute_similarity_enable_flag, num_pred_neighbours, and similarity_check_method_type are as described above.

22 illustrates an example of information related to a neighbor point set generation option in a Slice header of Attr according to embodiments.

In this specification, Attr means an attribute.

The method/apparatus according to the embodiments may signal by adding information related to the neighbor point set generation option to the slice header of Attr. A description of each field according to embodiments is as follows.

abh_attr_parameter_set_id represents the value of aps_attr_parameter_set_id of Actif APS.

abh_attr_sps_attr_idx represents an attribute set in the active SPS. The value of abh_attr_sps_attr_idx may be in the range of 0 to sps_num_attribute_sets in the active SPS.

abh_attr_geom_slice_id represents the value of geom slice id.

num_pred_nearest_neighbours, attribute_similarity_enable_flag, num_pred_neighbours, and similarity_check_method_type are as described above.

23 shows an example of a PCC (Point Cloud Compression) encoder according to embodiments.

The PCC encoder according to the embodiments may include a geometric information encoder 23001 and/or an attribute information encoder 23002.

The above-described geometry coding corresponds to the geometric information encoder of this drawing, and the above-described attribute coding corresponds to the attribute information encoder of this drawing.

Hereinafter, both geometry and geometric information are referred to as geometric information.

The PCC data may include geometric information and/or attribute information of a point.

Attribute information is obtained from one or more sensors, such as a vector representing the color of a point (R, G, B) and/or a brightness value and/or a reflection coefficient of a lidar and/or a temperature value obtained from a thermal imaging camera. It can be a vector of one value.

The spatial division unit 23000 may divide the input PCC data into at least one 3D block. In this case, the block may mean a tile group, a tile, a slice, or a coding unit (CU), a prediction unit (PU), or a transformation unit (TU). The partitioning may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, and a k-d tree. Alternatively, it can be divided into blocks of a predetermined horizontal and vertical height. Alternatively, it can be divided by selectively determining various positions and sizes of blocks. Corresponding information may be entropy-encoded and transmitted to a decoder.

The geometric information encoding unit 23001 generates an encoded geometric information bitstream and reconstructed geometric information on the received geometric information. The generated bitstream may be transmitted to the PCC decoder. In addition, the generated reconstructed geometric information may be input to the attribute information encoding unit.

The attribute information encoding unit 23002 receives the received attribute information and generates an attribute information bitstream. The generated attribute information bitstream may be transmitted to the PCC decoder.

The encoding of the point cloud data transmission method according to the embodiments may include encoding geometric information of the point cloud data; And encoding attribute information for the point cloud data. Includes.

24 shows an example of a geometric information encoder according to embodiments.

The geometric information encoder according to the embodiments may generate a geometric information bitstream and reconstructed (reconstructed = reconstructed) geometric information by performing a process as shown in the following diagram.

The geometric information encoding unit may include a coordinate system transforming unit, a geometric information transforming quantization unit, a residual geometric information quantizing unit, a geometric information entropy encoding unit, a residual geometric information inverse quantizing unit, a memory, and a geometric information predicting unit.

The above-described coordinate conversion unit 24000 corresponds to the coordinate system conversion unit of the geometric information encoder of this drawing, and the quantization processing unit, voxelization processing unit, octree code generation unit, and surface model processing unit are combined to correspond to the geometric information conversion quantization unit of this drawing. do. The above-described intra/inter coding processing unit corresponds to the geometric information prediction unit of this drawing, and the Arithmetic coder corresponds to the geometric information entropy coding unit.

The coordinate system conversion unit 24000 may receive geometric information as an input and convert it into a coordinate system different from the existing coordinate system. Alternatively, the coordinate system transformation may not be performed. The geometric information converted by the coordinate system may be input to the geometric information conversion quantization unit.

Whether or not the coordinate system is transformed and the coordinate system information can be signaled in units such as sequence, frame, tile, slice, block, etc., or whether the coordinate system of neighboring blocks is transformed or not, block size, number of points, quantization value, block splitting depth, unit position It can be derived using the unit and the distance from the origin.

If the coordinate system information to be converted is converted to the coordinate system after checking whether the coordinate system has been converted, the coordinate system information can be signaled in units such as sequence, frame, tile, slice, block, etc. or whether the coordinate system of neighboring blocks is converted, size of block, number of points , Quantization value, block splitting depth, unit location, distance between unit and origin, etc.

The geometric information transform quantization unit 24001 receives geometric information as input, applies one or more transforms such as position transform and/or rotation transform, divides the geometric information by a quantization value, and quantizes the transformed quantized geometric information. The transformed quantized geometric information may be input to a geometric information entropy encoding unit and a residual geometric information quantizing unit.

The geometric information prediction unit 24002 predicts geometric information through geometric information of points in the memory and generates the predicted geometric information. The prediction information used for prediction may be encoded by performing entropy encoding.

The residual geometric information quantization unit 24003 receives residual geometric information obtained by differentiating the transformed-quantized geometric information and the predicted geometric information, and quantizes it into a quantized value to generate quantized residual geometric information. Quantized residual geometric information may be input to a geometric information entropy encoding unit and a residual geometric information inverse quantization unit.

The geometric information entropy encoder 24004 may receive quantized residual geometric information and perform entropy encoding. Entropy coding may use various coding methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC).

The residual geometry information inverse quantization unit 24005 receives the quantized residual geometry information and restores the residual geometry information by scaling it by a quantized value. The restored residual geometric information may be restored as geometric information in addition to the predicted geometric information and stored in a memory.

The filtering unit 24006 may perform filtering on the restored geometric information. The filtering unit may include a deblocking filter, an offset correction unit, and an ALF.

The memory 24007 may store geometric information calculated through a filtering unit. The stored geometric information may be provided to the geometric information prediction unit when performing prediction.

25 shows an example of an attribute information encoder according to embodiments.

The attribute information encoder according to embodiments may generate an attribute information bitstream by performing a process as shown in the following diagram.

The attribute information encoder may include an attribute characteristic transform unit, a geometric information mapping unit, a transform unit, a quantization unit, an entropy encoding unit, an inverse quantization unit, an inverse transform unit, a memory, an attribute information prediction unit, and the like.

The above-described color conversion processing unit corresponds to the attribute information conversion unit of the attribute information encoder of this drawing, and the attribute conversion processing unit corresponds to the geometric information mapping unit of this drawing. The above-described prediction/lifting/RAHT conversion processing unit is divided into an attribute information prediction unit, a vehicle attribute information conversion unit, and a residual attribute information quantization unit in the drawing. The above-described Arithmetic coder corresponds to the attribute information entropy encoding unit of this drawing.

The attribute characteristic conversion unit 25000 may convert a characteristic of the received attribute information. For example, if the attribute information includes color information, the attribute characteristic conversion unit may convert the color space of the attribute information. The converted attribute information may be input to the geometric information mapping unit. Alternatively, it may be input to the geometric information mapping unit without conversion.

The geometric information mapping unit 25001 reconstructs attribute information by mapping the attribute information received from the attribute information conversion unit and the received restored geometric information. The attribute information reconstruction may derive an attribute value based on attribute information of one or a plurality of points based on the restored geometric information. The reconstructed attribute information may be input to the residual attribute information conversion unit by being differentiated from the predicted attribute information generated by the attribute information prediction unit.

The residual attribute information conversion unit 25002 may convert a residual 3D block including the received residual attribute information using a transformation type such as DCT, DST, DST, SADCT, RAHT, or the like. The converted residual attribute information may be input to the residual attribute information quantization unit. Alternatively, the residual attribute information may be input to the quantization unit without performing transformation. The transformation type may be transmitted to a decoder by performing entropy encoding in an entropy encoder.

The residual attribute information quantization unit 25003 generates transform quantized residual attribute information based on the quantized value of the received transformed residual attribute information. The transform quantized residual attribute information may be input to the attribute information entropy encoding unit and the residual attribute inverse quantization unit.

The attribute information entropy encoding unit 25004 may receive transformed quantized residual attribute information and perform entropy encoding. Entropy coding may use various coding methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC).

The residual attribute inverse quantization unit 25005 receives the received transformed quantized residual attribute information and generates transformed residual attribute information based on the quantization value. The generated transform residual attribute information may be input to a residual attribute inverse transform unit.

The residual attribute inverse transform unit 25006 may inverse transform a residual 3D block including the received transform residual attribute information by using a transform type such as DCT, DST, DST, SADCT, RAHT, or the like. The inversely transformed residual attribute information may be combined with predicted attribute information input from the attribute information predictor to generate restored attribute information. Alternatively, the reconstructed attribute information can be generated by directly adding the predicted attribute information without performing inverse transformation.

The filtering unit 25007 may include a deblocking filter, an offset correction unit, an adaptive loop filter (ALF), and the like. The filtering unit may perform filtering on the restored attribute information. Filtering is filtering on geometric information (XYZ) instead of attribute information (RGB, etc.). The filtering algorithm can be used as it is, only the input is different.

The memory 25008 may store attribute information calculated through a filtering unit. The stored attribute information may be provided to the attribute information predictor when performing prediction.

The attribute information predictor 25009 generates predicted attribute information based on attribute information of points in the memory. The prediction information may be encoded by performing entropy encoding.

The attribute information encoder according to the embodiments may include an attribute characteristic transform unit, a geometric information mapping unit, an attribute information transform unit, an attribute information quantization unit, and an attribute information entropy encoder, as shown in the lower part of the drawing. As shown in the upper part of the drawing, attribute information can be encoded based on the restored geometric information without processing the residual attribute information. Detailed description of each component is as described above.

26 shows an example of a PCC decoder according to embodiments.

The PCC decoder of the figure shows a detailed structural diagram of the above-described geometry information decoding and/or attribute decoding.

The spatial division unit may divide a space based on division information provided from an encoder or derived from a decoder.

The geometric information decoder 26000 restores the geometric information by decoding the received geometric information bitstream. The restored geometric information may be input to the attribute information decoder.

The attribute information decoder 26001 receives the received attribute information bitstream and the restored geometric information received from the geometry information decoder and restores the attribute information. The restored attribute information may consist of restored PCC data together with the restored geometric information.

27 shows an example of a geometric information decoder according to embodiments.

The geometry information decoder according to the embodiments may receive an encoded geometry information bitstream and perform a process as shown in the following diagram to restore the geometry information.

The geometric information decoder may include a geometric information entropy decoding unit, a residual geometric information inverse quantization unit, a geometric information prediction unit, and an inverse coordinate system transform unit.

The above-described Arithmetic decoder corresponds to the geometric information entropy decoding unit of the geometric information decoder of this drawing, and the octree reconstruction processing unit based on the occupancy code, the surface model processing unit, and the inverse quantization processing unit correspond to the residual geometric information inverse quantization unit of this drawing.

The geometric information entropy decoder 27000 may perform entropy decoding on an input bitstream. For example, for entropy decoding, various methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be applied. The geometric information entropy decoder may decode information related to geometric information prediction performed by the encoding apparatus. Quantized residual geometric information generated through entropy decoding may be input to the residual geometric information inverse quantization unit.

The residual geometric information inverse quantization unit 27001 may generate residual geometric information by performing inverse quantization on the basis of the quantization parameter and the received quantized residual geometric information.

The geometric information predictor 27002 may generate predicted geometric information based on information related to generation of predicted geometric information provided from the geometric information entropy decoder and previously decoded geometric information provided from a memory. The geometric information prediction unit may include an inter prediction unit and an intra prediction unit. The inter prediction unit uses information required for inter prediction of the current prediction unit provided by the encoding device, and determines the current prediction unit based on information included in at least one of a space before or after the current space including the current prediction unit. Inter prediction can be performed. The intra prediction unit may generate predicted geometric information based on geometric information of a point in the current space. When the prediction unit performs intra prediction, intra prediction may be performed based on intra prediction mode information of the prediction unit provided by the encoding device. The reconstructed geometric information may be generated by adding the reconstructed residual geometric information to the predicted geometric information.

The reconstructed geometric information may be provided to the filtering unit 27003. The filtering unit may perform filtering based on the filtering-related information provided from the decoder or the characteristics of the reconstructed geometric information derived from the decoder.

The memory 27004 may store the reconstructed geometric information calculated through the filtering unit.

The coordinate system inverse transform unit 27005 may perform the coordinate system inverse transform based on the coordinate system transformation related information provided from the geometric information entropy decoding unit and the restored geometric information stored in the memory.

28 shows an example of an attribute information decoder according to embodiments.

The attribute information decoder according to embodiments may receive an encoded attribute information bitstream and perform a process as shown in the following diagram to restore the attribute information.

The attribute information decoder may include an attribute information entropy decoding unit, a geometric information mapping unit, a residual attribute information inverse quantization unit, a residual attribute information inverse transform unit, an attribute information prediction unit, a memory, and an attribute information inverse transform unit.

The above-described Arithmetic decoder corresponds to the attribute information entropy decoding unit of the attribute information decoder of this drawing, and the inverse quantization processing unit corresponds to the residual attribute information inverse quantization unit of this drawing. The above-described prediction/lifting/RAHT inverse transformation processing unit is divided into a residual attribute information inverse transformation unit and an attribute information prediction unit, and the color inverse transformation processing unit corresponds to the attribute information inverse transformation unit of the present specification.

The attribute information entropy decoding unit 28000 may entropy-decode the received attribute information bitstream to generate transformed quantized attribute information. The generated transformed quantized attribute information may be input to the geometric information mapping unit.

The geometric information mapping unit 28001 maps the transformed quantized attribute information input from the attribute information entropy decoding unit and the restored geometric information received. The attribute information mapped to the geometric information may be input to the residual attribute information inverse quantization unit.

The residual attribute information inverse quantization unit 28802 performs inverse quantization on the received transformed quantized attribute information based on the quantization value. The inverse quantized transform residual attribute information may be input to the residual attribute information inverse transform unit.

The residual attribute information inverse transform unit 28803 may inverse transform a residual 3D block including the received transform residual attribute information using a transform type such as DCT, DST, DST, SADCT, RAHT, and the like. The inversely transformed residual attribute information may be combined with predicted attribute information generated from the attribute information prediction unit and stored in a memory. Alternatively, it may be stored in a memory by adding prediction attribute information without performing inverse transformation.

The attribute information predictor 28004 generates predicted attribute information based on attribute information of points in the memory. The prediction information can be obtained by performing entropy decoding.

The memory 28805 stores attribute information for predicting attribute information, residual attribute information, and summation information of predicted attribute information.

The attribute information inverse transform unit 28006 may receive the type of attribute information and transformation information from the entropy decoding unit and perform various color space inverse transformations such as RGB-YUV and RGB-YUV.

29 shows an example of a point cloud data transmission method according to embodiments.

In S29000, the point cloud data transmission method according to the embodiments encodes the point cloud data. The specific encoding process according to the embodiments includes the point cloud video encoder 10002 of FIG. 1, the encoding 20001 of FIG. 2, the process of encoding the geometry and/or attributes of FIG. 4, and the geometry and/or attributes of FIG. The encoding process, audio encoding of FIG. 14, point cloud encoding, and point cloud encoding of FIG. 15 may be combined. In addition, the attribute information predictor of FIG. 18, the PCC encoder of FIG. 23, the geometric information encoder of FIG. 24, and the attribute information encoder of FIG. 25 may be expressed in detail.

In S29001, the point cloud data transmission method according to the embodiments transmits a bitstream including point cloud data. Specific transmission processes according to the embodiments include the transmitter 10003 of FIG. 1, the transmission 20002 of FIG. 2, the transmission processing unit 12012 of FIG. 12, the file/segment encapsulation and delivery of FIG. It can be combined with delivery and the like. In addition, the transmitted data includes the point cloud bitstream of Fig. 19, signaling information (metadata) shown in Figs. 20 to 22, and the like.

30 shows an example of a method for receiving point cloud data according to embodiments.

In S30000, the method of receiving point cloud data according to the embodiments receives a bitstream including point cloud data. Specific reception processes according to embodiments include the receiver 10005 of FIG. 1, the transmission 20002 of FIG. 2-the decoding 20003, the geometry/attribute bitstream reception of FIG. 11, the receiver 13000 of FIG. 13, and 14 It may be combined with the file/segment reception of the point cloud player of, and the file/segment reception of FIG. 16. In addition, the received data includes the point cloud bitstream of Fig. 19, signaling information (metadata) shown in Figs. 20 to 22, and the like.

In S30001, the method for receiving point cloud data according to embodiments decodes the point cloud data. Specific decoding processes according to embodiments include a point cloud video decoder 10006 of FIG. 1, decoding 20003 of FIG. 2, a geometry/attribute bitstream decoder of FIG. 10, and a geometry/attribute bitstream decoding of FIG. Geometry/attribute bitstream decoding process and/or metadata parsing (13006) process of FIG. 14, file/segment decapsulation of FIG. 14, audio decoding, point cloud decoding, file/segment decapsulation of FIG. 16, point cloud decoding, etc. Can be combined. In addition, the attribute information predictor of FIG. 18, the PCC decoder of FIG. 26, the geometric information decoder of FIG. 27, and the attribute information decoder of FIG. 28 may be specifically combined.

In S30002, the method for receiving point cloud data according to embodiments renders the point cloud data. The specific rendering process according to the embodiments includes the renderer 10007 of Fig. 1, the rendering 20004 of Fig. 2, the audio rendering of Fig. 14, the point cloud rendering, the display, the point cloud rendering of Fig. 16, and the network of Fig. 17. It can be combined with device linkage.

In the present specification, both apparatus and method inventions are mentioned, and descriptions of both apparatus and method inventions may be applied to complement each other.

Each of the above-described parts, modules or units may be software, processor, or hardware parts that execute successive processes stored in a memory (or storage unit). Each of the steps described in the above-described embodiment may be performed by processor, software, and hardware parts. Each module/block/unit described in the above-described embodiment may operate as a processor, software, or hardware. In addition, the methods suggested by the embodiments may be executed as code. This code can be written to a storage medium that can be read by the processor, and thus can be read by a processor provided by the apparatus.

For convenience of explanation, each drawing has been described separately, but it is also possible to design a new embodiment by merging 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 is also within the scope of the rights of the embodiments according to the needs of the skilled person.

The apparatus and method according to the embodiments are not limitedly applicable to the configuration and method of the described embodiments as described above, but the above-described embodiments are all or part of each of the embodiments so that various modifications can be made. It may be configured in combination.

Although preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and common knowledge in the technical field to which the present invention belongs without departing from the gist of the embodiments claimed in the claims. Of course, various modifications may be implemented by a person having the same, and these modifications should not be understood individually from the technical idea or prospect of the embodiments.

It is understood by those skilled in the art that various changes and modifications are possible in the embodiments without departing from the spirit or scope of the embodiments. Accordingly, the embodiments are intended to cover variations and modifications of the embodiments provided within the appended claims and their equivalents.

In the present specification, both apparatus and method inventions are mentioned, and descriptions of both apparatus and method inventions may be applied to complement each other.

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”. In addition, “A, B, C” also means “at least one of A, B and/or C”. (In this document, the term “/”and “,”should be interpreted to indicate "and/or". For instance, the expression "A/B" may mean "A and/or B". Further, "A, B" may mean "A and/or B". Further, "A/B/C" may mean "at least one of A, B and/or C". Also, "A/B/C" may mean "at least one of A, B, and/or C")

Additionally, in this document “or” is to be interpreted as “and/or”. For example, “A or B” may mean 1) only “A”, 2) only “B”, or 3) “A and B”. In other words, “or” in this document may mean “additionally or alternatively”. (Further, in the document, the term “or” should be interpreted to indicate “and/or.” For instance, the expression “A or B” may comprise 1) only A, 2) only B, and/or 3) both A and B. In other words, the term “or” in this document should be interpreted to indicate “additionally or alternatively.”)

Various components of the apparatus of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. 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. Depending on the embodiments, at least one or more of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and one or more programs may be implemented. It may include instructions for performing or performing any one or more of the operations/methods according to the examples. Executable instructions for performing the method/operations of the apparatus according to the embodiments may be stored in a non-transitory CRM or other computer program products configured for execution by one or more processors, or may be stored in one or more It may be stored in a temporary CRM or other computer program products configured for execution by the processors. In addition, the memory according to the embodiments may be used as a concept including not only volatile memory (for example, RAM, etc.) but also nonvolatile memory, flash memory, PROM, and the like. In addition, it may be implemented in the form of a carrier wave such as transmission through the Internet. In addition, the recording medium readable by the processor may be distributed over a computer system connected through a network, so that code readable by the processor may be stored and executed in a distributed manner.

Terms such as first and second may be used to describe various elements of the embodiments. However, the interpretation of various components according to the embodiments should not be limited by the above terms. These terms are only used to distinguish one component from another. It's just a thing. For example, a first user input signal may be referred to as a second user input signal. Similarly, the second user input signal may be referred to as a first user input signal. The use of these terms should be construed as not departing from the scope of various embodiments. The first user input signal and the second user input signal are both user input signals, but do not mean the same user input signals unless clearly indicated in context.

The terms used to describe the embodiments are used for the purpose of describing specific embodiments, and are not intended to limit 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 indicates. And/or the expression is used in a sense including all possible combinations between terms. The include expression describes the existence of features, numbers, steps, elements, and/or components, and does not imply that no additional features, numbers, steps, elements, and/or components are included. .

Conditional expressions such as when, when, and when used to describe the embodiments are not limited to an optional case. When a specific condition is satisfied, it is intended to perform a related operation in response to a specific condition or to interpret the related definition.

As described above, related contents have been described in the best mode for carrying out the embodiments.

As described above, the embodiments may be applied wholly or partially to the point cloud data transmission/reception apparatus and system.

Those skilled in the art may variously change or modify the embodiments within the scope of the embodiments.

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

Claims (20)

Encoding point cloud data; And Transmitting a bitstream including the point cloud data; Containing, Point cloud data transmission method. The method of claim 1, The encoding may include encoding geometry information for the point cloud data; And Encoding attribute information for the point cloud data; Containing, Point cloud data transmission method. The method of claim 1, The encoding step includes encoding attribute information for the point cloud data, Encoding the attribute information further comprises predicting the attribute information, Point cloud data transmission method. The method of claim 2, The encoding of the attribute information further comprises predicting the attribute information, The predicting of the attribute information may include generating a level of detail (LOD) based on the attribute information and geometry information reconstructed from the encoded geometry information; Generating a set of neighboring points based on the LOD; And Encoding the attribute information based on the set of neighboring points; Containing, Point cloud data transmission method. The method of claim 4, Generating the set of neighboring points, Including the step of registering a nearest neighbor point among the points for the LOD as a neighbor point set, The neighboring point set is generated based on similarity to the candidate of the neighboring point set from candidates of the neighboring point set selected based on distance, Point cloud data transmission method. An encoder for encoding point cloud data; And A transmitter for transmitting a bitstream including the point cloud data; Containing, Point cloud data transmission device. The method of claim 6, The encoder includes a geometry encoder that encodes geometry information for the point cloud data; And An attribute encoder encoding attribute information on the point cloud data; Containing, Point cloud data transmission device. The method of claim 6, The encoder includes an attribute encoder for encoding attribute information on the point cloud data, The attribute encoder further comprises a prediction unit that predicts the attribute information, Point cloud data transmission device. The method of claim 7, The attribute encoder further includes a prediction unit predicting the attribute information, The prediction unit generates a level of detail (LOD) based on the attribute information and geometry information reconstructed from the encoded geometry information, Generate a set of neighboring points based on the LOD, Encoding the attribute information based on the set of neighboring points, Point cloud data transmission device. The method of claim 9, The prediction unit, Among the points for the LOD, a nearest neighbor is registered as a neighboring point set, The neighboring point set is generated based on similarity to the candidate of the neighboring point set from candidates of the neighboring point set selected based on distance, Point cloud data transmission device. Receiving a bitstream including point cloud data; Decoding the point cloud data; And Rendering the point cloud data; Containing, How to receive point cloud data. The method of claim 11, The decoding may include decoding geometry information for the point cloud data; And Decoding attribute information for the point cloud data; Containing, How to receive point cloud data. The method of claim 11, The decoding step includes decoding attribute information for the point cloud data, The step of decoding the attribute information further comprises predicting the attribute information, How to receive point cloud data. The method of claim 12, The decoding of the attribute information further includes predicting the attribute information, The predicting of the attribute information may include generating a Level of Detail (LOD) based on the attribute information and geometry information reconstructed from the decoded geometry information; Generating a set of neighboring points based on the LOD; And Decoding the attribute information based on the set of neighboring points; Containing, How to receive point cloud data. The method of claim 14, Generating the set of neighboring points, Including the step of registering a nearest neighbor point among the points for the LOD as a neighbor point set, The neighboring point set is generated based on similarity to the candidate of the neighboring point set from candidates of the neighboring point set selected based on distance, How to receive point cloud data. A receiver for receiving a bitstream including point cloud data; A decoder for decoding the point cloud data; And A renderer for rendering the point cloud data; Containing, Point cloud data receiving device. The method of claim 16, The decoder includes a geometry decoder for decoding geometry information on the point cloud data; And An attribute decoder for decoding attribute information on the point cloud data; Containing, Point cloud data receiving device. The method of claim 16, The decoder includes an attribute decoder that decodes attribute information on the point cloud data, The attribute decoder further comprises a prediction unit that predicts the attribute information, Point cloud data receiving device. The method of claim 17, The attribute decoder further includes a prediction unit that predicts the attribute information, The prediction unit generates a level of detail (LOD) based on the attribute information and geometry information reconstructed from the decoded geometry information, Generating a set of neighboring points based on the LOD; Decoding the attribute information based on the set of neighboring points, Point cloud data receiving device. The method of claim 19, The prediction unit, Among the points for the LOD, a nearest neighbor is registered as a neighboring point set, The neighboring point set is generated based on similarity to the candidate of the neighboring point set from candidates of the neighboring point set selected based on distance, Point cloud data receiving device.

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