WO2021025546A1

WO2021025546A1 - Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

Info

Publication number: WO2021025546A1
Application number: PCT/KR2020/095022
Authority: WO
Inventors: 허혜정; 오세진
Original assignee: 엘지전자 주식회사
Priority date: 2019-08-08
Filing date: 2020-03-05
Publication date: 2021-02-11

Abstract

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

The 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 a person skilled in the art based on the entire description.

In order to achieve the above-described technical problem, a method of transmitting point cloud data according to embodiments includes: encoding point cloud data; Transmitting a bitstream including point cloud data; Includes.

A method of receiving point cloud data according to embodiments includes: receiving a bitstream including point cloud data; Decoding the point cloud data; 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. For a better understanding of the various embodiments described below, reference should be made to the description of the following embodiments with reference to the following drawings in which like reference numerals include corresponding parts throughout the following drawings.

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

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

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

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

5 shows an example of a voxel according to embodiments.

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

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

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

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

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

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

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

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

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

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

16 shows an example of a reception device 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 distribution of the x-axis, y-axis, and z-axis of point cloud data according to embodiments.

19 illustrates an example of a division method according to a range of each axis according to embodiments.

20 illustrates an example of variance per octree node according to embodiments.

21 illustrates an example of a spatial division unit of a tile-based division method according to embodiments.

22 illustrates an example of a spatial division unit of a slice-based division method according to embodiments.

23 illustrates an example of a spatial division unit of a block-based division scheme according to embodiments.

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

25 shows an example of a point cloud bitstream according to embodiments.

26 illustrates an example of region division signaling information (SPS) according to embodiments.

27 illustrates an example of region division signaling information (SPS) according to embodiments.

28 illustrates an example of region division signaling information and tile information (TPS) according to embodiments.

29 illustrates an example of region division signaling information and geometry QP according to embodiments.

30 illustrates an example of quantization information (GSH) according to embodiments.

31 illustrates an example of quantization information (GSH) according to embodiments.

32 illustrates an example of attribute QP information (ASH) according to embodiments.

33 illustrates an example of attribute QP information according to embodiments.

34 illustrates an example of block parameter information (BPS) according to embodiments.

35 shows an example of parameter information of a block according to embodiments.

36 shows an example of a method for transmitting point cloud data according to embodiments.

37 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 apparent to those 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.

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.

The point cloud data transmission method/apparatus according to the embodiments may encode point cloud data and transmit a bitstream including the point cloud data.

The method/apparatus for receiving point cloud data according to embodiments may receive a bitstream including point cloud data, decode point cloud data, and render point cloud data.

For this reason, the method/apparatus according to the embodiments has an effect of efficiently providing point cloud content to a user with a low delay suitable for a service environment.

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 system for providing feedback information and point cloud content 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 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 (e.g., 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 Nearest Neighbor Search (NNS) is possible. 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 as a decimal number, 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.

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

As shown in the middle of the upper 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 a vertex is detected, the point cloud encoder according to the embodiments entropycodes the starting point (x, y, z) of the edge, the direction vector of the edge (Δx, Δy, Δz), and vertex position values (relative position values within the edge) I can. When trisoup geometry encoding is applied, the point cloud encoder (e.g., the geometry reconstruction unit 40005) according to embodiments performs a triangle reconstruction, up-sampling, and voxelization process. By doing so, you can create reconstructed 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.

table. 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).

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.

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 according to embodiments (for example, the arithmetic encoder 40012) may entropy-code 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 combining 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 following equation represents the RAHT transformation matrix. g _{lx, y, and z} denote average attribute values of voxels at level l. g _{lx, y, z} can be calculated from g _{l+1 2x, y, z} and g _{l+1 2x+1, y, z} . The weights of g _{l 2x, y, z} and g _{l 2x+1, y, z} are w1=w _{l 2x, y, z} and w2=w _{l 2x+1, y, z} .

g _{l-1 x, y, and z} are low-pass values and are used in the merging process at the next higher level. h _{l-1 x, y, and z} are high-pass coefficients, and the high-pass coefficients in each step are quantized and entropy-coded (for example, encoding of the arithmetic encoder 400012). Weights are _{calculated as} w _{l-1 x, y, z} =w _{l 2x, y, z} +w _{l 2x+1, y, z} . The root node is created through the last g _{1 0, 0, 0} and g _{1 0, 0, 1} as follows,

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.

The elements of the point cloud decoder of FIG. 11 are not shown in the figure, 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 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 processing unit 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, since descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described in FIGS. 1 to 9, detailed descriptions are omitted.

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) for 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), and information about data included in the payload. 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. 1 to 2 and thus will be omitted.

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.

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 an 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 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 illustrated in the drawing is the same as described in FIGS. 1 to 14, detailed descriptions will be 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. Accordingly, 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 reception device according to the embodiments may perform auxiliary decompression. Additional decompression is performed on the metadata. The description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted. Also, 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 reception 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.

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 help at least part of the processing of the connected devices.

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.

The method/apparatus according to the embodiments means a method/device for transmitting point cloud data and/or a method/device for receiving point cloud data according to the embodiments.

The point cloud according to the embodiments may be variously referred to as point cloud data, point cloud content, one or more points, etc. according to the embodiments.

Geometry information according to embodiments refers to geometric information according to embodiments.

The attribute information according to the embodiments means attribute information according to the embodiments.

The transmission device according to the embodiments may be referred to as a point cloud encoder, a point cloud encoder, or the like. The receiving device according to embodiments may be referred to as a point cloud decoder, a point cloud decoder, or the like.

The method/apparatus according to the embodiments provides a method of spatial division, quantization adjustment, and/or application of geometry-based point cloud compression (G-PCC) for compressing 3D point cloud data. Can provide.

The method/apparatus according to the embodiments proposes a method for applying/adjusting adaptive quantization for each area according to characteristics of point cloud content and service characteristics.

Hereinafter, in the process of G-PCC geometry/attribute encoding/decoding, the region is divided into tiles, slices, or blocks that are smaller than slices according to the degree of dispersion of contents (point cloud contents), and divided according to the type of service. We propose an adaptive quantization control/application method for each region by allocating geometry/attribute QP (Quantization Point) to control the quality of compression of the image and setting whether to merge overlapping points. In this regard, a method for analyzing point cloud point variance and segmentation, a method for adjusting geometry/attribute QP of divided blocks and setting whether to merge overlapping points, and a signaling method for supporting the above method will be described in detail with reference to each drawing. .

For this reason, the method/apparatus according to the embodiments provides a geometry and attribute compression quality of Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. ), and in particular, can provide an efficient effect that can be flexibly adjusted according to the distribution of the point cloud.

A point cloud is composed of a set of points, and each point may have geometry information and attribute information (attributes). The geometry information is three-dimensional position (XYZ) information, and the attribute information is color (RGB, YUV, etc.) or/and/and/or a 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 as described above)

In the process of compressing geometric information, an octree technique or a trisoup technique can be used. When using a geometry information voxelization, an octree technique for managing occupied voxels, and a tri-forest, a process of converting information into information of vertex segments for forming a triangle may be included. (Details are as described above)

In the attribute information compression process, a prediction transform scheme, a lift transform scheme, and a RAHT technique can be used. (Details are as described above).

Point clouds can be processed by geometry coding and attribute coding. Accordingly, the quality of compression may be applied differently from geometry coding and attribute coding, and since a point cloud reconstructed as a result of geometry compression is used as an input for attribute coding, the quality of geometry compression may affect attribute coding.

The quality control of geometry and attribute compression can be controlled by quantization values. Also, geometry query control can be adjusted according to overlapping point processing.

When controlling the quality of geometry and attribute compression, it may be necessary to consider the following.

① It may be necessary to adjust/apply adaptive quantization for each area depending on the characteristics of the point cloud content and the service. Since the area close to the captured equipment is important for the data captured during autonomous driving on the road, it is important to preserve the important areas as much as possible. In the case of the terrain of the flight simulation, the problem of quickly loading the point cloud of a large area may be a bigger problem than preserving the close area of the terrain. This may be possible because the plane and terrain are far apart. In this case, it may be necessary to quantize a larger value in a region with a high distribution. Therefore, it may be necessary to adjust the quality by adjusting the quantization value for each area according to the point distribution of the content and the characteristics of the service.

② To divide the area, tiles (tile: a tile is a divided area of space. For example, one room can be a tile) and slice (slice: a slice can exist in a tile, and a slice is divided by the number of points in the tile. Parallel fast processing is the purpose of slicing). However, an ideal slice can be divided based on about 1,100,000 points, and tiles can be made up of more than one slice, which can be a larger criterion. Since slices are performed in parallel, related information cannot be exchanged between slices during compression. Therefore, a method of dividing a region may require a method of dividing a point cloud into a unit (block) smaller than a slice and in addition to a tile and a slice, and performing compression while exchanging information about different regions during block division and compression. For this reason, in the type or situation of data that divides a slice into a more detailed region, a unit called a block is required.

In consideration of the above, the method/apparatus according to the embodiments provides a method for applying/adjusting adaptive quantization for each area according to the characteristics of the point cloud content and the service. Specifically, we propose a method for 1) analysis of point cloud point variance and segmentation, 2) a method for adjusting geometry/attributes QP of the divided block and setting whether to merge redundant points, and/or 3) a signaling method to support the above method. .

In addition, changes and combinations between embodiments are possible in this document. Terms used in this document may be understood based on the intended meaning of the terms within the range widely used in the relevant field. The point cloud distribution-based region segmentation method is performed in PCC attribute encoding of the PCC encoder, and may be reconstructed through the PCC attribute decoding process of the PCC decoder.

Hereinafter, a method for dividing a point cloud distributed mood region according to embodiments will be described. The region division method according to the embodiments includes a method/apparatus according to the embodiments, specifically, the transmission device 10000 of FIG. 1, the point cloud video encoder 10002, the reception device 10004, and the point cloud video decoder 10006. ), encoding (20001), decoding (20003) of FIG. 2, and before the geometry/attribute encoder of FIG. 11, the data input unit 12000 of Fig. 12, the reception processing unit 13001 of Fig. 13, the point cloud encoder of Fig. 14, It can be processed by the point cloud decoder, the point cloud encoder of Fig. 15, the point cloud decoder of Fig. 16, the XR device 2230 of Fig. 17, and the like.

Specifically, the region segmentation method according to the embodiments includes: 1) adjusting position values for points, 2) analyzing point cloud distributions, 3) segmenting regions, 4) geometry of the segmented regions, and It may include an attribute QP adjustment step, and/or 5) a step of setting whether to merge duplicated points of the divided regions. In the following, each step will be described.

1) Position value adjustment step for points

The method/apparatus according to embodiments may find the minimum x, y, and z position values of all points and subtract the found position value from the position values of each point. Through this process, the position at the bottom left and front of the point cloud content can be adjusted as the origin (0, 0, 0). Furthermore, according to embodiments, by receiving movement and/or rotation information corresponding to user information, and applying a movement value and/or a rotation value received from points of the point cloud, all points may be represented. Also, by receiving the full scale value, the scale value may be applied to the position values of the points. The scale value may be considered as an overall geometry quantization parameter. Therefore, in lossless geometry coding, the scale value may be fixed to 1, and in lossy geometry, the scale value may be designated as a number less than 1. A first-order geometry quantization step can be applied by applying a full scale value. Here, the scale value may vary according to embodiments.

The method/device according to the embodiments may analyze the degree of variance of the point cloud data with respect to the X-axis, Y-axis, and Z-axis through the variance analysis step.

2) Point cloud distribution analysis stage

When an automatic region-specific QP (quantization value, quantization parameter) adjustment flag is set during point cloud encoding, a positional variance analysis process for content may be performed. Through the content point cloud variance analysis process, the variance of points for each axis can be known, and the geometric QP value for each region can be automatically set in the subsequent encoding process using the information. Alternatively, you can automatically set the attribute QP value. The set QP information may be signaled to the decoder.

As shown in the figure, distributions in the x-axis, y-axis, and z-axis may be expressed. Depending on the type of point cloud data (category, autonomous driving, etc.), the distribution of points may be dense or small. For example, in the case of the X-axis, there may be many points at a close distance, and as the distance increases, there may be fewer points. Relatively, the Y-axis and Z-axis may have fewer points than the X-axis.

For example, when there are various distributions or densities for each axis, a quantization value may be set to be larger in a dense place for each rectification of point cloud data, and a quantization value may be set to be larger in a small area. This is because, as described above, when driving on a road, a nearby area is important, and it is more important to set a quantization value so that a point cloud can be loaded quickly when flying.

Referring to the drawings, for example, in the case of the X-axis, the vertical axis refers to the number of points and the horizontal axis refers to the distance. The data on the X-axis has a large volume of points, but it can be seen that most of the points are distributed near the distance. For such data, when the density is complex, the effect of dividing the area based on the division unit (tile/slice/block) according to the embodiments, and applying the QP to the divided area according to the type or quality of the service Want to provide.

3) Area division step

The method/apparatus according to the embodiments may divide a region of point cloud data based on a degree of dispersion for each axis.

For example, if the automatic region-specific QP adjustment flag is set, the region can be automatically divided according to the point distribution. According to embodiments, a division criterion may be generated for each axis by receiving input from a user according to the point distribution of each axis. The degree of division can be input and applied.

Here, the unit of the divided area may be variously set according to embodiments. Specifically, a) tile division, b) slice division, and/or c) block division may be provided.

a) Split tiles

A tile splitting technique can be applied. The method/apparatus according to the embodiments may divide one content into tiles for each area according to the analyzed point cloud dispersion degree.

b) Apply slice division

A slice division technique can be applied. The method/apparatus according to the embodiments may divide one tile into slices based on the analyzed dispersion degree.

c) Apply block division

One slice can be divided into smaller regions according to the degree of dispersion. According to embodiments, such a smaller area may be referred to as a block as a new area concept. Although it is encoded/decoded in one slice, there may be an advantage in that different parameters can be applied for each block to compress.

The above-described information on a unit of a divided region for a tile/slice/block, etc. may be signaled to the decoder.

The area division method according to the embodiments relates to obtaining a division area range in each axis, and specifically, a) setting a division area range for uniform division, b) setting a division area range according to the distribution ratio, and/or c) It may include an octree node area + a division area range setting according to a distribution rate.

Through the point cloud variance analysis operation according to the embodiments, the variance of points for each axis can be known, and the corresponding point cloud axis and the density of each area can be known through the analyzed variance information. By using the density information through variance analysis, the region can be divided in the subsequent encoding process and the geometric QP value for each region can be automatically set.

19 shows an example of a division method according to a range of each axis according to embodiments.

Referring to FIG. 19, a method for dividing a region according to embodiments will be described.

a) Setting the division area range for uniform division

If the area where the point exists is divided based on the number of divisions for each axis, the range of one area can come out. According to embodiments, the range of the region may be uniformly obtained for each axis. In the segmentation method, the area with the highest distribution of all points is divided into area ranges for each axis. Repeat for each axis. Here, the number of divisions may be applied by receiving input from the user and/or the PCC system.

For example, if the number of divisions for each axis is all 2, the area can be divided in units of 50% variance. A range corresponding to 50% based on the X-axis, 50% based on the y-axis, and 50% based on the z-axis can be obtained, and regions can be divided by a combination of the x, y, and z-axis regions. Divided as shown in the drawing may be possible. The range of the line 1910 may be selected as one area.

Referring to the drawing, the distribution of the x-axis is analyzed, and the area may be divided based on the number of divisions received based on the area having the largest distribution. The same is true for division of regions on the y-axis and z-axis.

b) Setting the range of division area according to the distribution ratio

The method/apparatus according to the embodiments may perform region division by receiving division reference information (distribution information of point density) on the distribution rate of points for each axis from a user or a PCC system. For example, if block 1 (which can be referred to as the first block, etc.) divided about the x-axis receives (or is allocated) an area according to the distribution of the top 50%, it is based on the area with the highest distribution of all points. As a result, the range of the divided area of the block belonging to block 1 can be calculated according to the percentage of the total number of points to the number of points belonging to the area. When the divided area for each axis is obtained, the point cloud area may be divided according to the area division criterion for each axis to form blocks. The construction method is the same as the example described in the previous uniform division.

20 illustrates an example of a variance percentage for each octree node according to embodiments.

c) Set octree node area + partition area range according to distribution ratio

A method of segmenting a region based on a variance percentage for each octree node will be described with reference to the drawings.

When the point cloud data is expressed as an octree node, for example, a variance percentage for each node such as a first node 20010, a second node 20020, and a third node may be checked. In the area corresponding to the first node 20010, the variance percentage of points is 70%, the variance percentage of the points in the area corresponding to the second node 20020 is 23%, and the points in the area corresponding to the third node 20030 The percent variance of may be 17 percent.

As described above, the method/apparatus according to the embodiments receives reference information on the point distribution rate for each axis, checks the distribution rate around the octree nodes, and determines the range of the distribution rate of the octree node based on the received reference information. By checking, the area can be divided based on octree nodes.

Specifically, the point cloud content may be divided according to a division method, and the divided area may be set as one tile, slice, or block according to the selected area unit. The segmentation method may be transmitted by including an encoder (PCC encoder) and a transmission device in a bitstream as signaling information for a reconstruction operation of a decoder (PCC decoder).

When configuring a divided region, the order of the axes can be changed (y,z,x, etc.), not based on the order of the x, y, z axes, and the changed order is included as signaling information by an encoder (PCC encoder) and a transmission device in the bitstream. Can be transmitted.

Additional information for each tile, slice, and/or block may be included in the bitstream and transmitted by an encoder (PCC encoder) and a transmission device.

Here, a point density value may be generated. The density degree according to embodiments may be generated by the number of points / area. In addition, each tile, slice, and block may have information (bounding-box, position, size) about the bounding box of each block (or tile, slice), and this information is included in the bitstream and is included in the encoder (PCC encoder). , Can be transmitted by the transmission device.

In the case of division by octree node + distribution ratio, region division can be performed using an order value such as breadth-first/depth-first set of the octree node instead of the bounding box.

For example, the octree node may be generated by an octree generator, etc., and when checking the distribution/distribution of points for each axis as described above, the distribution of points for each node is determined according to the embodiments as shown in the figure. /The device can figure it out.

The method/apparatus according to the embodiments may arrange the above-described divided area(s) in the order according to the embodiments.

For example, regions may be arranged in the order of regions with high density, and bitstreams encoded in the ordered order may be transmitted. In this case, since the density is high, an important area can be decoded first. It provides the effect of processing important areas faster.

4) Step of adjusting geometry and attribute QP of segmented area

After the region is divided (or generated) through the region division step to generate tiles, slices, and/or blocks, the geometry QP and the attribute QP may be adjusted (or applied) for each unit. The QP set for each block may be automatically set according to the point density of the block, or may be set by receiving a QP value according to each point density.

For example, QP according to embodiments means a geometry QP and/or an attribute QP, and in the case of a geometry QP, it means a QP value that can be applied as an adjustment value to X, Y, Z, etc., which are geometry information. Similarly, in the case of attribute QP, it means a QP value to which an adjustment value can be applied to the attribute information such as RGB.

As described above, in accordance with service requirements, geometry QP and/or attribute QP can be applied high for areas with high density, and conversely, geometry QP and/or attribute QP can be applied low for areas with high density. have. Subsequently, quantization/voxelization is performed.

Specifically, in the case of automatic setting, a different policy may be provided depending on the service. If the point density is high, the QP value can be increased or decreased. These policies can be determined depending on whether the important part of the service is real-time or visual quality. Or, if you want to set it yourself rather than an automatic policy, you can receive input and set it. It can be applied to geometry QP, and it can also be applied to attribute QP. The QP value can be set as an absolute value or a delta value according to the point density.

In addition, in the case of the geometry QP, the QP value may have one QP value, or may be applied differently to each of x, y, and z. The quantization parameter value of the block with the highest detail may be 1. As the detail decreases, the quantization parameter value is a number less than 1.

5) Steps to set whether to merge duplicated points in the divided area

After the region is divided through the region division step to generate tiles, slices, and/or blocks, quantization/voxelization, etc. may be performed. Here, a case of overlapping points may occur, and a method for processing overlapping points is It can be set differently according to the environment of the service. That is, it is possible to set whether or not to merge each overlapping point differently. Whether to merge may be automatically set according to the point density, or whether to merge duplicate points according to each point density may be input from the user and/or the PCC system and set. If it is set automatically, it can be set to have different policies depending on the service, similar to QP settings. When real-time is important, the number of points can be reduced by performing merging on blocks with high density, and when the geometry of the core area is important, points can be preserved without performing merging points.

Geometry QP and attribute QP values set according to tiles, slices, and/or blocks may be applied during compression (point cloud compression session) (details of point cloud compression are as described above).

An example of a configuration diagram of a method/device according to embodiments for processing the area division method according to the above-described embodiments will be described below.

6) Area axis order adjustment

In the region division operation of the method/device according to the embodiments, after the region is divided and tiles, slices, or blocks are generated, the axis order of each tile/slice/block may be changed. For example, the axis ordering axis may be expressed as xyz, xzy, yxz, yzx, zxy zyx. Attribute compression rates may vary according to the axis order of each divided area. Therefore, the order of each axis can be adjusted according to the density. Therefore, there is an effect of optimizing and adjusting the order of the axes according to the quality and encoding/decoding performance of the point cloud content service.

21 shows an example of a spatial division unit of a tile-based division method according to embodiments.

In the case of tile-based region division according to embodiments, a PCC encoder may be configured as shown in the figure.

The encoders in the figure include the point cloud video encoder 10002 of FIG. 1, the encoding 20001 of FIG. 2, the encoding of FIG. 4, the transmission device of FIG. 12, the point cloud encoder of FIG. 17, and the XR device of FIG. 2230), it is possible to perform all/some of the above-described operations. Each component according to the embodiments will be described.

The data input unit 21000 receives parameters, which are metadata (signaling information) related to geometry information, attribute information, geometry information, and/or attribute information.

The coordinate system conversion unit 21010 may generate/transform a coordinate system for the geometry based on the coordinate system XYZ of the 3D space.

The geometric information transform quantization processor 21020 may quantize and voxel geometric information.

The space dividing unit 21040 divides an area of point cloud data. Specifically, the space division unit 21040 checks whether to perform analysis division (21040-1). When performing analysis division, the point distribution division unit 21040-2 analyzes the point distribution for each axis as described above. If the analysis segmentation is not performed, the operation of the tile segmentation unit goes directly to the operation of the tile segmentation unit without the point variance analysis unit. The tile dividing unit 21040-3 divides the area of the point cloud based on the tile according to the result of analyzing the variance of the points or without analyzing the variance of the points (eg, automatically set reference information can be used). . Here, the information value for each tile may be generated as signaling information, included in the bitstream, and transmitted. The tile signaling information according to the embodiments includes the position of the bounding box for the tile, the size of the bounding box, the density (number of points/area area), and the order value of the octree node (this value may be optional depending on the embodiments. For example, the octree node order may follow breadth-first order, etc.). For such signaling information/parameters, refer to FIGS. 26-35.

The setting unit 21040-4 may set a geometry QP and/or an attribute QP for each tile, and may set whether to merge overlapping points. Here, the information value for each tile may be generated as signaling information, included in the bitstream, and transmitted. For example, the information value for tiles according to embodiments may include geometry QP, attribute QP, information indicating whether overlapping points are merged, or the like for each tile. For such signaling information/parameters, refer to FIGS. 26-35.

The slice dividing unit 21040-5 may divide the tile into one or more slices.

The point cloud data is divided into a region based on a tile/slice and then input to the geometric information encoder 21050.

The geometric information encoding unit 21050 encodes geometry information. Specifically, the voxelization processing unit 21050-1 voxels the point cloud data. The detailed operation of voxelization is as described above.

The octree generator 21050-2 represents the point cloud data as node(s) having an octree structure.

The geometric information prediction unit 21050-3 performs prediction on geometry information. Geometry information may be generated as a predicted value.

The geometric information entropy encoding unit 21050-4 encodes the predicted geometry information and/or the geometry information based on an entropy technique, and generates a geometry bitstream.

The geometric information inverse quantization processing unit 21050-5 inversely quantizes the geometric information based on the predicted geometric information. This is to generate reconstructed geometry information by restoring geometry information whose location information has been changed through an encoding process.

The attribute information encoding unit 21060 encodes attribute information based on reconstructed geometry information. In this case, when residual attribute information is generated, the residual attribute information may be quantized to encode the attribute information. The attribute information encoder 21060 may generate an attribute bitstream including encoded attribute information.

22 illustrates an example of a spatial division unit in a slice-based division method according to embodiments.

For a description of the configuration overlapping with the above-described drawings, refer to the above description.

When a divided area is generated based on a slice, the space dividing unit 22000 may divide the area of the point cloud based on a slice unit.

Specifically, the tile dividing unit 22000-1 divides the point cloud data into one or more tiles. The detailed operation of the tile dividing unit is as described above.

Then, it is checked whether or not to perform the analysis segmentation (22000-2). In the case of performing the analysis division, the point variance analysis unit 22000-3 is performed, and when the analysis division is not performed, the slice division unit 22000-4 is directly performed without the point variance analysis unit 22000-3. do.

The point variance analysis unit 22000-3 may analyze the number/distribution of points for each axis of the point cloud data.

The slice dividing unit 22000-4 divides the area of the point cloud based on the slice unit according to the variance analysis result of the point or without the variance analysis result of the point (for example, automatically set reference information can be used). can do. The slice dividing unit 22000-4 may generate an information value for each slice as signaling information, include it in a bitstream, and transmit it. For example, the signaling information on the slice is the location of the bounding box for the slice, the size of the bounding box, the density (number of points/area area), and the order value of the octree node (this value may be optional according to embodiments. For example, the octree node order may follow breadth-first order, etc.). For such signaling information, refer to FIGS. 26-35.

The setting unit 22000-5 may set geometric QP and attribute QP values for each slice. Furthermore, when overlapping points occur during division of regions, whether to merge overlapping points may be set. Here, according to embodiments, an information value for each slice may be added to the bitstream as signaling information. For example, the signaling information for the slice may include a geometry QP value, an attribute QP value, information indicating whether to merge duplicate points, and the like. For such signaling information, refer to FIGS. 26-35.

When performing region division based on a block, the space dividing unit 23000 may divide the region of the point cloud based on a block.

Specifically, the tile dividing unit 23000-1 may divide the point cloud data based on one or more tiles.

The slice dividing unit 23000-2 may divide one or more tiles based on a slice.

The method/device according to the embodiments may or may not divide the analysis.

When performing analysis segmentation, the point variance analysis unit 23000-4 may analyze the point cloud data for each axis to calculate a distribution map.

When the analysis division is not performed, the block division unit 23000-5 is performed without the point variance analysis unit 23000-4.

As described above, the point variance analysis unit 23000-4 may analyze the variance of the point cloud for each axis.

The block dividing unit 23000-5 divides the point cloud data based on the block based on the analyzed variance or based on the set variance reference information or the variance reference information input from the PCC system without the analyzed distribution information. I can.

Here, the information value for each block may be included in the bitstream and transmitted as signaling information. For example, the position of the bounding box for each block, the size of the bounding box, the density (number of points/area area), the order value of the octree node (this value may be optional depending on the embodiments. For example, the octree) Node order may follow breadth-first order, etc.). For such signaling information, refer to FIGS. 26-35.

The setting unit 23000-6 may set a geometry QP value and/or an attribute QP value for each block, and when points overlap, set whether to merge the overlapping points. Here, according to embodiments, the information value for each block may be added to the bitstream as signaling information. For example, the signaling information for the block may include a geometry QP value, an attribute QP value, information indicating whether to merge duplicate points, and the like. For such signaling information, refer to FIGS. 26-35.

The unit of division according to embodiments may be a tile/slice/block. The

space dividing units

21000, 22000, and 23000 based on the tile/slice/block have different division units, and can process the following operations in common. Hereinafter, it is briefly referred to as a space division unit.

The spatial division unit may receive an input from a system or a user whether to divide the analysis. The spatial division unit may receive a divided area unit (tile, slice, block) from a system or a user. The block of the spatial division unit may be an area obtained by virtually dividing one slice. In other words, a tile may be a space division unit, a slice may be a division unit of a certain number of points in a tile, and a block may mean a division unit of a slice.

When the spatial division unit divides the tiles, the distribution of points of the content may be analyzed for each axis.

When the spatial division unit performs slice division, point distribution for each tile may be analyzed for each axis.

When the spatial division unit performs block division, point distribution for each slice can be analyzed for each axis.

When the spatial division unit performs block division, an input for a criterion for setting a block may be received. The input may include a method of setting a divided area range based on a uniform division, a method of setting a divided area range according to a distribution rate, and a method of setting a divided area range according to an octree node area + distribution rate.

When the spatial division unit performs block division, information on the degree of division of the region (eg, division number, division region information according to distribution percentage, etc.) may be input.

The area is divided based on the method in which the spatial division unit is set, the analyzed distribution type, and the degree of division, and at this time, one area may be called a block.

When the spatial division unit performs block division, after division is performed, each block may have signaling information such as a bounding box position, size, density, or octree node order value for each block.

Information on whether or not the spatial division unit automatically sets whether to merge geometry QP / attribute QP and overlapping points for each region may be input.

When the divided area or the area division method is automatically set, the space division unit may receive a policy regarding area division from a user or a PCC system.

When the segmentation area or the segmentation method is automatically set, the policy may lower the QP value to improve the quality of the high-density area, or increase the QP value to match the quality of the high-density area to the low area. Geometry and attribute QP can be set automatically according to policy.

When the divided area or the area division method is automatically set, whether to merge duplicate points may be set according to a policy type. If the policy makes high-density areas of high quality, duplicate points can be eliminated, and overlapping points can be merged to match the high-density areas to low-quality areas. You can set whether to automatically merge duplicate points according to the policy.

When the division area or the division method is not automatically set, geometry QP and attribute QP for each tile, slice, or block can be input. The QP value can be an absolute value or a relative value (delta, difference value).

When the division area or the division method is not automatically set, whether to merge overlapping points for each tile, slice, or block may be input.

An axis order (area_distribution_based_partition_axes_order) may be adjusted for each divided area, and related information may be signaled.

The method/apparatus for transmitting point cloud data according to the embodiments may perform spatial division for dividing a region of point cloud data based on a division unit upon encoding.

In addition, when the dividing unit is a tile, the distribution of point cloud data is analyzed when performing spatial division, and the operation of spatially dividing the data of the point cloud based on the tile based on the distribution is performed, and the bitstream is the point cloud data. A parameter of may further be included, and the parameter may include information on a spatial division operation based on the tile (FIGS. 26 to 35 ).

In addition, when the division unit is a slice, the distribution of point cloud data is analyzed when performing spatial division, and the operation of spatially dividing the point cloud data based on the slice is performed based on the distribution diagram, and the bitstream is applied to the point cloud data. A related parameter may be further included, and the parameter may include information on a slice-based spatial division operation (FIGS. 26 to 35).

In addition, when the division unit is a block, the distribution of point cloud data is analyzed when performing spatial division, and the operation of spatially dividing the data of the point cloud based on the block based on the distribution is performed, and the bitstream is applied to the point cloud data. A related parameter may be further included, and the parameter may include information about the spatial division operation based on the block (FIGS. 26-35).

The method/apparatus for receiving point cloud data according to the embodiments may spatially reconstruct a region of the point cloud data based on the division unit upon decoding.

In addition, when the dividing unit is a tile, the operation of spatially reconstructing the data of the point cloud based on the tile during spatial reconstruction, a quantization value (QP) for the tile is applied to the tile, and the bitstream is applied to the point cloud data. A related parameter may be further included, and the parameter may include information on a spatial division operation based on the tile (FIGS. 26-35).

In addition, when the partitioning unit is a slice, the operation of spatially reconstructing the data of the point cloud based on the slice is performed during spatial reconstruction, the quantization value for the slice is applied to the slice, and the bitstream contains parameters related to the point cloud data. Further, the parameter may include information on a slice-based spatial division operation (FIGS. 26-35).

In addition, when the partitioning unit is a block, the operation of spatially reconstructing the data of the point cloud based on the block during spatial reconstruction is performed, the quantization value for the block is applied to the block, and the bitstream contains parameters related to the point cloud data. Further, the parameter may include information on a block-based spatial division operation (FIGS. 26-35).

For this reason, the method/apparatus according to the embodiments can efficiently process PCC encoding/decoding by dividing the point cloud content into areas of appropriate division units. For example, there is an effect of providing optimal content to users by spatially dividing and quantizing point cloud content according to the type of point cloud data such as road driving data and/or flight data, and user-provided quality.

In addition, in the case of tiles, it is possible to achieve the optimization of spatial division of point cloud data according to the spatial perspective, and in the case of slices included in the tile, space for optimizing encoding/decoding of point cloud data according to the number of points Split can be provided, and in the case of a block included in a slice, there is an effect of additionally providing a detail setting capable of encoding/decoding the slice in more detail according to the block.

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

All/part of the operation of the point cloud decoder (PCC decoder) of FIG. 24 may perform a reverse process of all/part of the operation of the point cloud encoder (PCC decoder) of FIGS. 21 to 23 described above.

The spatial reconstruction unit 24000 receives a geometry bitstream including geometry information. The space reconfiguration unit 24000 performs the reverse process of the

space division units

21040, 22000, and 23000 described above. The spatial reconstruction unit 24000 restores the space of point cloud data based on information values for each tile/slice/block.

Specifically, the spatial reconstruction unit 24000 may reconstruct a space based on information for each tile/slice/block. Thereafter, the restored points may be combined into one to restore one point cloud. According to the transmitted geometry QP for each tile/slice/block, a geometry quantization value can be applied inversely to each block. Attribute quantization values can be reversely applied to each block according to the transmitted attribute QP for each tile/slice/block. Here, a spatial reconfiguration operation may be performed based on signaling information related to a location of a bounding box for each tile/slice/block, a size of a bounding box, an octree node order value, and whether or not overlapping points are merged. For such signaling information, refer to FIGS. 26-35.

The geometric information entropy decoder 24010 decodes the geometric information based on an entropy technique.

The geometric information decoding unit 24020 decodes the geometric information in the following manner. Specifically, the octree reconstruction unit 24030 reconstructs the geometry information into an octree. The geometric information prediction unit 24040 generates predicted geometry information by predicting the geometry information. The geometric information transformation inverse quantization processing unit 24050 inversely quantizes the geometric information. The coordinate system inverse transform unit 24060 calculates geometry information by inversely transforming the coordinate system of the geometry information.

The geometric information transformation inverse quantization processor 24050 may perform a restoration operation based on signaling information indicating a geometric QP value for each tile/slice/block. For such signaling information, refer to FIGS. 26-35.

The reconstructed geometry information generated by the geometric information prediction unit 24040 is input to the attribute information decoding unit 24070.

The attribute information decoding unit 24070 decodes attribute information. Specifically, the attribute bitstream including the attribute information is decoded based on the reconstructed geometry information. In addition, when the residual attribute information is used, a residual attribute information inverse quantization processing unit for inversely quantizing the residual attribute information may be further included. Here, the attribute information may be decoded using signaling information including the attribute QP value for each block. Accordingly, the attribute information decoding unit 24070 may calculate attribute information. For such signaling information, refer to FIGS. 26-35.

In addition, in the above-described embodiments, terms such as density and distribution may be expanded and interpreted to complement each other with the same or similar function/meaning.

According to embodiments, the data size of the point cloud content may be a map during autonomous driving or may be used for indoor navigation. In this case, it could be a huge amount of data that is locally linked. Since such content cannot be encoded/decoded at once, partitioning can be performed before compression of point cloud content. In partitioning, a tile (or 3D tile) may be composed of a rectangular cube divided into spaces. By applying parallelization, the partitioned tiles can be repartitioned into slices for fast encoding/decoding. Each slice is composed of a rectangular cube and may be a unit of a bitstream that can be independently decoded. Points in the decoded slice may overlap with other slices. A process of compressing geometric information and a process of compressing attribute information may be performed in units of each slice. Accordingly, the method/apparatus according to the embodiments may provide spatial division (partitioning) based on a tile/slice/block.

25 shows an example of a point cloud bitstream according to embodiments.

The method/apparatus according to the embodiments may generate and obtain a point cloud bitstream as shown in the figure. For example, the transmission device 10000 of FIG. 1, the point cloud video encoder 10002, the receiving device 10004, the point cloud video decoder 10006, the encoding 20001, the decoding 20003 of FIG. The encoding process, the decoding process of Fig. 11, the transmission device of Fig. 12, the receiving unit of Fig. 13, the PCC system of Figs. 14 to 16, the XR device 2230 of Fig. 17, the PCC encoder of Figs. 21 to 23, the PCC of Fig. 24 A point cloud bitstream including parameters including geometry information, attribute information, and/or metadata for the same may be generated (encoded) and received (decoded) by a decoder or the like.

Information for embodiments may be signaled.

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.

In addition, with respect to signaling information related to the operation of the spatial division unit and/or the spatial reconstruction unit, the region division related option information may be added to the SPS, TPS, or a geometry header for each slice for signaling. Quantization-related information can be added to each tile, slice, and block for signaling. Blocks can be signaled by creating a Block Parameter Set (BPS). Hereinafter, syntax of specific signaling information will be described with reference to the drawings. For reference, the name of signaling information according to embodiments may be understood by being modified/expanded within the meaning/function range intended by the signaling information. A field of signaling information may be classified into first signaling information, second signaling information, and the like and called.

As described above, the point cloud data transmission apparatus (for example, the point cloud data transmission apparatus described in FIGS. 1, 11, 14, and 1) may transmit encoded point cloud data in the form of a bitstream. A bitstream according to embodiments may include one or more sub-bitstreams.

The point cloud data transmission device (for example, the point cloud data transmission device described in FIGS. 1, 11, 14, and 1) divides the image of the point cloud data into one or more packets in consideration of the error of the transmission channel. Can be transmitted over the network. A bitstream according to embodiments may include one or more packets (eg, Network Abstraction Layer (NAL) units). Therefore, even if some packets are lost in a poor network environment, the device for receiving point cloud data may restore a corresponding image using the remaining packets. The point cloud data may be processed by dividing it into one or more slices or one or more tiles. Tiles and slices according to embodiments are areas for processing point cloud compression coding by partitioning a picture of point cloud data. The point cloud data transmission apparatus may provide high-quality point cloud content by processing data corresponding to each region according to the importance of each divided region of the point cloud data. That is, the point cloud data transmission apparatus according to the embodiments may perform point cloud compression coding processing of data corresponding to an area important to a user having better compression efficiency and appropriate latency.

An image (or picture) of point cloud content according to embodiments is divided into units of basic processing units for point cloud compression coding. A basic processing unit for point cloud compression coding according to embodiments is a coding tree (CTU). unit), brick, etc., and are not limited to this example.

A slice according to the embodiments does not have a rectangular shape as an area including basic processing units for one or more integer number of point cloud compression coding. A slice according to embodiments includes data transmitted through a packet. A tile according to embodiments includes one or more basic processing units for coding a point cloud compression as an area divided into a rectangular shape in an image. One slice according to embodiments may be included in one or more tiles. Also, one tile according to embodiments may be included in one or more slices.

The bitstream 3000 according to the embodiments includes a Sequence Parameter Set (SPS) for signaling of a sequence level, a Geometry Parameter Set (GPS) for signaling of geometry information coding, and an Attribute Parameter Set (APS) for signaling of attribute information coding. ), signaling information including a Tile Parameter Set (TPS) for signaling of a tile level, and one or more slices.

The SPS according to the embodiments is encoding information on the entire sequence such as a profile and a level, and may include comprehensive information on the entire file, such as a picture resolution and a video format.

One slice (for example, slice 0 of FIG. 25) according to embodiments includes a slice header and slice data. Slice data may include one geometry bitstream (Geom0 ⁰ ) and one or more attribute bitstreams (Attr0 ⁰ and Attr1 ⁰ ). The geometry bitstream may include a header (eg, geometry slice header) and a payload (eg, geometry slice data). The header of the geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_geom_parameter_set_id), a tile identifier (geom_tile id), a slice identifier (geom_slice_id), and information on data included in the payload. I can. The attribute bitstream may include a header (eg, attribute slice header or attribute brick header) and a payload (eg, attribute slice data or attribute brick data).

26 shows an example of region division signaling information (SPS) according to embodiments.

The SPS of the figure is parameter information included in the bitstream described above in FIG. 25, and may be generated, transmitted, and received according to the embodiments described above in FIG. 25.

Region division status flag (area_distribution_based_partition_flag): Indicates whether region division based on variance analysis is applied.

Area division unit (area_distribution_based_partition_unit): Indicates a unit to which area division is applied. Specifically, it may be signaled such as 1 = tile, 2 = slice, 3 = block, and the like. The integer value representing the tile/slice/block can be variously modified.

Partition area policy (area_distribution_based_partition_policy): Represents a policy applied to a divided area. Specifically, 1 = high density, high QP application, 2 = high density, low QP application, 3 = manual, etc. may be signaled. The integer value representing each policy can be variously modified.

Division method (area_distribution_based_partition_method): Represents a division method. Specifically, the signal may be signaled such as 1= a split-region range-based setting method for uniform division, 2= a split-region range setting method according to a distribution ratio, and 3= an octree node area + a split-region range setting method according to a distribution ratio. The integer value representing each method can be variously modified.

Partition order (area_distribution_based_partition_axes_order: When configuring a partition, it can indicate an axis order. For example, 1 = xyz, 2 = xzy, 3 = yxz, 4 = yzx, 5 = zxy, 6 = zyx, etc. may be signaled. The integer value representing each sequence can be variously modified.

QP value characteristic (area_distribution_based_partition_absolute_QP_flag): This indicates whether the QP value of the block is an absolute value or a relative value (offset, delta).

The profile (profile_idc) represents a profile for a bitstream as described in the standard. The bitstream does not include profile IDC values of other standards. Other values of Profile IDC may be reserved for future use of ISO/IEC (indicates a profile to which the bitstream conforms as specified in Annex A. Bitstreams shall not contain values of profile_idc other than those specified in Annex A. Other values) of profile_idc are reserved for future use by ISO/IEC).

When the profile compatibility flag (profile_compatibility_flags) is 1, it indicates that the bitstream follows a profile identified by the profile IDC having a standard J value. The value of profile_compatibility_flag[ j] is 0 for J that is not described as the value of the standard profile IDC (equal to 1, indicates that the bitstream conforms to the profile indicated by profile_idc equal to j as specified in Annex A. The value of profile_compatibility_flag[ j] shall be equal to 0 for any value of j that is not specified as an allowed value of profile_idc in Annex A).

The level (level_idc) represents the level followed by the bitstream as described in the standard. The bitstream may not contain a level value different from that described in the standard. Other values of the level value may be reserved for future use of ISO/IEC (indicates a level to which the bitstream conforms as specified in Annex A. Bitstreams shall not contain values of level_idc other than those specified in Annex A. Other values) of level_idc are reserved for future use by ISO/IEC).

If the SPS bounding box presence flag (sps_bounding_box_present_flag) is 1, it indicates that the source bounding box offset and size information is signaled in the SPS. If sps_bounding_box_present_flag is 0, it indicates that the source bounding box information is not signaled (equal to 1 indicates the source bounding box offset and the size information is signaled in the SPS. sps_bounding_box_present_flag equal to 0 indicates the source bounding box information is not signaled).

Due to the above-described embodiments, the method/apparatus according to the embodiments signals whether to divide a region, a region division unit, a division policy, a division method, a division region order, and a QP value, and PCC encoding/device based on these values. There is an effect that can perform decoding. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

27 shows an example of region division signaling information (SPS) according to embodiments.

According to embodiments, instead of all the partitioning-related information present in the SPS, as shown in FIG. 27, only the following basic partitioning information may be added to the SPS for signaling. For example, whether to apply area division according to variance analysis (area_distribution_based_partition_flag) and a unit to which area division is applied (area_distribution_based_partition_unit, for example, tile/slice/block) may be signaled as basic division information in the SPS.

The remaining information may be added to the TPS. This will be described with reference to FIG. 28.

Due to the above-described embodiments, the method/apparatus according to the embodiments may signal whether or not region division is applied and a unit to which region division is to be applied, and through this, it is possible to signal a spatial division operation of PCC encoding/decoding. have. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

The TPS in the figure is parameter information included in the bitstream described above in FIG. 25, and may be generated, transmitted, and received according to the embodiments described above in FIG. 25.

As described above, the region division option may be added to the TPS for signaling. Due to this, the region division option can be signaled efficiently. The name of the signaling information according to the embodiments of the present specification may be understood within the scope of the meaning and function of the signaling information.

Referring to the drawing, a tile quantization and related information may be added to the TPS and signaled.

Octree traverse index (area_distribution_based_partition_octree_traverse_index): indicates an order value according to the octree traverse method used during encoding.

Area density (area_distribution_based_partition_density): indicates the density of an area.

Duplicate point information (area_distribution_based_partition_duplicated_point_merge_flag): Indicates whether duplicate points are allowed.

Geometry QP information (area_distribution_based_partition_geometryQP): indicates the geometry QP value. Specifically, the QP value is a geometry second-order QP value, and this value is applied when quantizing the second-order geometry.

Attribute QP information (area_distribution_based_partition_attributeQP: indicates attribute QP.

Number of tile BPS (num_bps_set): This indicates the number of block parameter sets (BPS) belonging to the tile.

BPS identifier (bps_block_ids): represents the BPS ID (identifier) belonging to the tile.

Number of tiles (num_tiles): Indicates the number of tiles signaled for a bitstream. If not present, the number of tiles may be inferred as 0 (specifies the number of tiles signaled for the bitstream.When not present, num_tiles is inferred to be 0).

Tile bounding box offset X (tile_bounding_box_offset_x[i]): represents the X offset of the I-th tile in the coordinate system (eg, Cartesian coordinates). If not present, the value of tile_bounding_box_offset_x[ 0] can be inferred as sps_bounding_box_offset_x (indicates the x offset of the i-th tile in the cartesian coordinates. When not present, the value of tile_bounding_box_offset_x[ 0] is inferred to be sps_bounding_box_box_ ).

Tile bounding box offset Y (tile_bounding_box_offset_y[i]): indicates the Y offset of the I-th tile in the coordinate system (eg, Cartesian coordinates). If not present, tile_bounding_box_offset_y[ 0] can be inferred as sps_bounding_box_offset_y (indicates indicates the y offset of the i-th tile in the cartesian coordinates. When notoff present, the value of tile_bounding_box_offset_y[ 0] is inferred to be sps_bounding_box_) .

Tile bounding box offset Z (tile_bounding_box_offset_z[i]): represents the Z offset of the I-th tile in the coordinate system (eg, Cartesian coordinates). If not present, the value of tile_bounding_box_offset_z[ 0] can be inferred as sps_bounding_box_offset_z (indicates indicates the z offset of the i-th tile in the Cartesian coordinates. When not present, the value of tile_bounding_box_offset_z[ 0] is inferred to be sps_bounding_box_offset_z).

Tile bounding box size width (tile_bounding_box_size_width[ i ]): This indicates the width of the I-th tile in the coordinate system (eg Cartesian coordinates). When not present, the value of tile_bounding_box_size_width[ 0] is inferred to be sps_bounding_box_size_width (indicates the width of the i-th tile in the Cartesian coordinates. .

Tile bounding box size height (tile_bounding_box_size_height[ i ]): indicates the height of the I-th tile in the coordinate system (eg Cartesian coordinates). If not present, the value of tile_bounding_box_size_height[ 0] can be inferred as sps_bounding_box_size_height (indicates the height of the i-th tile in the Cartesian coordinates. When not present, the value of tile_bounding_box_size_height[ 0] is inferred to be sps_bounding_box_size) .

Tile bounding box size depth (tile_bounding_box_size_depth[ i ]): indicates the depth of the I-th tile in the coordinate system (eg, Cartesian coordinates). If not present, the value of tile_bounding_box_size_depth[ 0] can be inferred as sps_bounding_box_size_depth (indicates the depth of the i-th tile in the Cartesian coordinates. When not present, the value of tile_bounding_box_size_depth[ 0] is inferred to be sps_depth_box_size) .

Due to the above-described embodiments, the method/apparatus according to the embodiments includes an octree traverse index, area density, redundant point information, geometry QP value, attribute QP value, number of tile BPS, BPS identification, number of tiles, tile It is possible to signal the bounding box offset (XYZ), the tile bounding box size (width, height, depth), etc., and there is an effect of efficiently signaling the spatial division process of the PCC encoder/decoder. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

As shown in the drawing, according to embodiments, the geometry QP may be separately specified for each axis. Due to this, the geometry Q can be efficiently expressed.

Block geometry QP can be signaled per axis. Specifically, block_geometryQP_x, block_geometryQP_y, and/or block_geometryQP_z represent geometry secondary QP for each axis. These values can be applied when quantizing the second-order geometry.

Due to the above-described embodiments, since the geometric QP value can be signaled for each axis, it is possible to efficiently support the operation of spatial division/reconstruction for each axis. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

30 shows an example of quantization information (GSH) according to embodiments.

The GSH (Geometry Slice Header) of the figure is parameter information included in the bitstream described above in FIG. 25, and may be generated, transmitted, and received according to the embodiments described above in FIG. 25.

The area_distribution_based_partition_flag and area_distribution_based_partition_unit of the drawing may follow values set in the SPS.

In addition, area_distribution_based_partition_policy, area_distribution_based_partition_method, area_distribution_based_partition_axes_order, area_distribution_based_partition_absolute_QP_flag in the drawing may follow a value set in SPS or TPS.

Quantization-related information can be signaled by being added to a geometry slice header. Accordingly, quantization-related information can be efficiently signaled.

The following signaling information in the drawing is as described above: Whether to apply region region division according to variance analysis (area_distribution_based_partition_flag), segmentation method (area_distribution_based_partition_method, for example, 1= segmentation range based setting method for uniform segmentation, 2= A method of setting the range of the partition area according to the distribution ratio. For example, 1= tile, 2= slice, 3= block), area density (area_distribution_based_partition_density[i]), whether overlapping points are allowed (area_distribution_based_partition_duplicated_point_merge_flag), geometry 2nd QP (area_distribution_based_partition, tile belonging to BPS (area_distribution_based_partition, tile) Number (num_bps_set[i]), BPS ID belonging to the tile (bps_block_ids[i][j]),

Due to the above-described embodiments, it is possible to support the operation of the PCC decoder by efficiently transmitting the division method and the QP value to the receiver. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

31 illustrates an example of quantization information GSH according to embodiments.

As shown in the drawing, the geometry QP and area_distribution_based_partition_geometryQP can be separately specified for each axis (area_distribution_based_partition_geometryQP_x/_y/_z).

Information included in the geometry slice header of FIGS. 30 to 31 is as follows.

The geometry parameter set identifier (gsh_geometry_parameter_set_id) indicates the value of the gps_geom_parameter_set_id of the active GPS (specifies the value of the gps_geom_parameter_set_id of the active GPS).

The tile identifier (gsh_tile_id) represents the value of the tile ID referenced by GSH. The value of gsh_tile_id may have a range of 0 to XX (inclusive) (specifies the value of the tile id that is referred to by the GSH. The value of gsh_tile_id shall be in the range of 0 to XX, inclusive).

The slice ID (gsh_slice_id) may identify a slice header for reference by other syntax elements. The value of gsh_slice_id may have a range of 0 to XX (inclusive) (identifies the slice header for reference by other syntax elements.The value of gsh_slice_id shall be in the range of 0 to XX, inclusive).

GSH box log scale (gsh_box_log2_scale) specifies the scaling factor of bounding box origin for the slice.

GSH box origin X (gsh_box_origin_x) specifies the x value of bounding box origin that scaled by gsh_box_log2_scale value.

GSH box origin Y (gsh_box_origin_y) specifies the y value of bounding box origin that scaled by gsh_box_log2_scale value

GSH box origin Z (gsh_box_origin_z) specifies the z value of bounding box origin that scaled by gsh_box_log2_scale value.

GSH log2 max nodesize (gsh_log2_max_nodesize) specifies the value of the variable MaxNodeSize that is used in the decoding process

The GSH point number (gsh_points_number) specifies the number of coded points in the slice.

For this reason, the geometric QP value can be signaled based on the geometry slice header, and the receiving method/device according to the embodiments has an effect of performing spatial reconstruction using quantization information. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

32 shows an example of attribute QP information (ASH) according to embodiments.

The ASH (Attribute Slice Header) of the figure is parameter information included in the bitstream described above in FIG. 25, and may be generated, transmitted, and received according to the embodiments described above in FIG. 25.

Attribute QP can be signaled by being added to an attribute slice header. For this reason, attribute QP can be signaled efficiently.

Area_distribution_based_partition_flag and area_distribution_based_partition_unit of the drawing may follow values set in the SPS.

The attribute parameter set ID (ash_attr_parameter_set_id) represents the identifier of the attribute parameter set in ASH.

The attribute index (ash_attr_sps_attr_idx) represents an attribute set in the active SPS.

The slice ID (ash_attr_geom_slice_id) represents the value of the geometry slice ID.

The slice QP flag (aps_slice_qp_delta_present_flag) indicates that there are ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements in ASH. If aps_slice_qp_present_flag is 0, ash_attr_qp_delta_luma and ash_attr_qp_delta_luma syntax elements do not exist in ASH.

The QP delta luma (ash_qp_delta_luma) represents the luma delta qp from the initial slice QP in the active attribute parameter set.

QP delta chroma (ash_qp_delta_chroma) represents the chroma delta qp from the initial slice QP in the active attribute parameter set.

Whether to apply area division according to variance analysis of the drawing (area_distribution_based_partition_flag), unit to which area division is applied (area_distribution_based_partition_unit, 1= tile, 2= slice, 3= block), attribute QP (area_distribution_based_partition_attributeQP), num_bps_set[i], bps_block_ids[i] ][j] and the like are as described above.

Accordingly, the method/apparatus according to the embodiments may efficiently support the spatial reconfiguration operation of the reception method/device according to the embodiments by signaling a region division and an attribute QP value through the attribute slice header. In addition, since the parameters are signaled by being located on the bitstream as shown in the figure, there is an effect that the receiving device can efficiently decode.

33 illustrates an example of attribute QP information according to embodiments.

According to embodiments, as shown in the drawing, the attribute slice header may use the existing ash_dq_depth_luma and chroma instead of area_distribution_based_partition_attributeQP. In that case, attribute_slice_header is not changed. As for the semantic, an absolute value and a relative value may be interpreted according to an area_distribution_based_partition_absolute_QP_flag value.

The parameter set ID in the attribute slice header of the drawing (ash_attr_parameter_set_id), the attribute identifier in the attribute slice header (ash_attr_sps_attr_idx), the slice ID in the slice header (ash_attr_geom_slice_id), the first flag (aps_slice_qpition_delta_present_flagition_partition_distribution_present_flag_distribution_flag) ash_qp_delta_luma) and chroma (ash_qp_delta_chroma) are as described above.

As shown in the figure, since the above-described parameters are located on the bitstream and signaled, there is an effect that the reception device can efficiently decode.

The block parameter set (BPS) of the figure is parameter information included in the bitstream described above in FIG. 25, and may be generated, transmitted, and received according to the embodiments described above in FIG. 25.

The BPS may deliver information about blocks according to embodiments. As information on the block, the following information may be included.

Reference tile ID (gsh_reference_tile_id): indicates the tile ID connected to the block.

Reference slice ID (sh_reference_slice_id: indicates the slice ID connected to the block.

BPS block ID (bps_blocks_id): represents the unique ID of the BPS.

Block number (num_blocks): indicates the number of blocks.

Block ID (block_id): Represents the block ID.

Block bounding box X, Y, Z (block_bounding_box_x, block_bounding_box_y, block_bounding_box_z): indicates the left/bottom/front reference position (X, Y, Z) of the block bounding box.

Block bounding box size width, block bounding box size height, block bounding box size depth (block_bounding_box_size_width, block_bounding_box_size_height, block_bounding_box_size_depth): Represents the block bounding box size (width, height, depth, etc.).

Block virtual bounding box offset X, Y, Z (block_virtual_bounding_box_offset_x, block_virtual_bounding_box_offset_y, block_virtual_bounding_box_offset_z): Indicates the location of a virtual bounding box for adjusting the position of each block when coding occupied bits using one octree.

Block Virtual Bounding Box Size Width, Block Virtual Bounding Box Size Height, Block Virtual Bounding Box Size Depth (block_virtual_bounding_box_size_width, block_virtual_bounding_box_size_height, block_virtual_bounding_box_size_depth): When coding the occupied bit size of each virtual block using one octree Represents (width, height, depth, etc.).

Area_distribution_based_partition_method, area_distribution_based_partition_octree_traverse_index[i], area_distribution_based_partition_density[i], area_distribution_based_partition_duplicated_point_merge_flag[i], area_distribution_based_partition_partition[i], area_distribution_based_partition_partition_geometry[i] of the drawing are the same.

As shown in the drawing, the geometry QP and area_distribution_based_partition_geometryQP may be separately signaled for each axis (area_distribution_based_partition_geometryQP_x, _y, _z).

In the method/apparatus according to the embodiments, for example, data captured during autonomous driving on a road may extract points having more areas close to the captured device. In addition, during autonomous driving, the equipment to be captured is installed in the vehicle, and since the surroundings of the vehicle are important, it is important to preserve the high density and important areas as much as possible. Accordingly, it is possible to use an adaptive quantization control/apply method for each region in which regions with high density are separated, geometry/attribute QP is allocated accordingly, and overlapping point merging is set. Alternatively, when capturing a person with a point cloud, the points may be more concentrated on the face. In this case, it is possible to adjust the QP for preserving the highly dense area.

In addition, in the case of the terrain for flight simulation, the equipment to be captured is installed on the plane, but when capturing the terrain visible from the ground to the bottom, the problem of quickly loading the point cloud of a large area is greater than preserving the close area of the terrain. It could be a problem. This may be possible because the plane and terrain are far apart. In this case, it may be necessary to quantize a larger value in a region with a high distribution.

Therefore, in order to solve this technical problem, the method/apparatus according to the embodiments adjusts the quantization value for each area according to the point distribution (density) of the content and the characteristics of the service to meet the need to adjust the quality by area. A method for controlling/applying type quantization can be provided.

S3600, the point cloud data transmission method according to the embodiments includes encoding the point cloud data. The detailed process of encoding is the point cloud video encoder 10002 of FIG. 1, the encoding of FIG. 2 (20001), the point cloud encoding of FIG. 4, the geometry/attribute encoding of FIG. 12 (12000 to 12012, etc.), and the Point cloud acquisition, point cloud encoding, file/segment encapsulation, the process of the XR device of Fig. 17, and the operation of the

spatial division units

21040, 22000, and 23000 of Figs. 21 to 23 are followed.

S3610, the point cloud data transmission method according to the embodiments further includes transmitting a bitstream including point cloud data. The transmission process is the transmission device 10000 of Fig. 1, the transmitter 10003, the transmission 20002 of Fig. 2, the transmission processing unit 12012 of Fig. 12, the delivery of Figs. 14 to 15, and the XR device 2230 of Fig. 17. The process of, the geometry/attribute bitstream transmission process of Figs. 21 to 23, the point cloud bitstream of Fig. 25, and the like are followed. In addition, the bitstream includes the parameters of Figs. 26 to 35.

S3700, a method for receiving point cloud data according to embodiments includes receiving a bitstream including point cloud data. The receiving process includes the receiving device 10004 of Fig. 1, the receiver 10005, the transmission-decoding of Fig. 2 (20002-20003), the geometry/attribute bitstream reception of Fig. 11, and the receiving unit-receiving processing unit 13000-13001 of Fig.13. ), the delivery of Figs. 14-15, the delivery of Fig. 16, the process of the XR device 2230 of Fig. 17, the reception of the geometry bitstream and the attribute bitstream of Fig. 24, the point cloud bitstream of Fig. 25, and the like are followed. In addition, the bitstream includes the parameters of Figs. 26 to 35.

S3710, the method for receiving point cloud data according to the embodiments further includes decoding the point cloud data. The decoding process includes a point cloud video decoder 10006 of Fig. 1, decoding 20003 of Fig. 2, decoding of a geometry/attribute bitstream of Fig. 11, decoding of a geometry/attribute bitstream of Fig. 13 (13002 to 13010, etc.), and Fig. 14 To the file/segment decapsulation of FIG. 16, point cloud decoding, the process of the XR device 2230 of FIG. 17, and the geometry/attribute bitstream decoding of FIG. 23 are followed. In addition, the decoding process decodes the bitstream and parameters of FIGS. 25 and 26 to 35.

S3720, the method for receiving point cloud data according to the embodiments further includes rendering the point cloud data. The rendering process follows the processes of the rendering 1007 of FIG. 1, the rendering 20004 of FIG. 2, the rendering/display of FIGS. 14 and 16, and the process of the XR device 2230 of FIG.

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.

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 embodiments are all or part of each of the embodiments selectively combined so that various modifications can be made. It can also be configured. 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.

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 embodiments may be stored in a non-transitory CRM or other computer program products configured to be executed 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 (eg, RAM, etc.) but also non-volatile 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. Further, the processor-readable recording medium is distributed over a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.

In this document, “/” and “,” are interpreted as “and/or”. For example, “A/B” is interpreted as “A and/or B”, and “A, B” is interpreted as “A and/or B”. Additionally, “A/B/C” means “at least one of A, B and/or C”. In addition, “A, B, C” also means “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”.

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

Encoding the point cloud data;

Transmitting a bitstream including the point cloud data; Containing,

Point cloud data transmission method.
The method of claim 1,

The encoding step,

Further comprising a space division step of dividing the area of the point cloud data based on a division unit,

Point cloud data transmission method.
The method of claim 2,

When the dividing unit is a tile, the spatial dividing step

Analyzing a distribution diagram of the point cloud data, performing an operation of spatially dividing the data of the point cloud based on the tile based on the distribution diagram,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the tile,

Point cloud data transmission method.
The method of claim 1,

When the division unit is a slice, the spatial division step

Analyzing a distribution diagram of the point cloud data, performing an operation of spatially dividing the data of the point cloud based on the slice based on the distribution diagram,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the slice,

Point cloud data transmission method.
The method of claim 1,

When the division unit is a block, the space division step

Analyzing a distribution diagram of the point cloud data, performing an operation of spatially dividing the data of the point cloud based on the block based on the distribution diagram,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the block,

Point cloud data transmission method.
An encoder for encoding point cloud data;

A transmitter for transmitting a bitstream including the point cloud data; Containing,

Point cloud data transmission device.
The method of claim 6,

The encoder,

Further comprising a space division unit for dividing the area of the point cloud data based on a division unit,

Point cloud data transmission device.
The method of claim 6,

When the division unit is a tile, the space division unit

Analyzing the distribution map of the point cloud data, spatially dividing the data of the point cloud based on the tile based on the distribution map,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the tile,

Point cloud data transmission device.
The method of claim 6,

When the division unit is a slice, the space division unit,

Analyzing a distribution map of the point cloud data, spatially partitioning the data of the point cloud based on the slice, based on the distribution map,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the slice,

Point cloud data transmission device.
The method of claim 6,

When the division unit is a block, the space division unit

Analyzing the distribution map of the point cloud data, spatially partitioning the data of the point cloud based on the block based on the distribution map,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the block,

Point cloud data transmission device.
Receiving a bitstream including point cloud data;

Decoding the point cloud data;

Rendering the point cloud data; Containing,

How to receive point cloud data.
The method of claim 11,

The decoding step,

Further comprising the step of spatially reconstructing the area of the point cloud data based on a division unit,

How to receive point cloud data.
The method of claim 11,

When the dividing unit is a tile, reconfiguring the space may include

Performs an operation of spatially reconstructing data of the point cloud based on the tile, and a quantization value for the tile is applied to the tile,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the tile,

How to receive point cloud data.
The method of claim 11,

When the division unit is a slice, the spatial reconfiguration step

Performs an operation of spatially reconstructing the data of the point cloud based on the slice, and a quantization value for the slice is applied to the slice,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the slice,

How to receive point cloud data.
The method of claim 11,

When the division unit is a block, the spatial reconfiguration step

Performs an operation of spatially reconstructing the data of the point cloud based on the block, and a quantization value for the block is applied to the block,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the block,

How to receive point cloud data.
A receiver for receiving a bitstream including point cloud data;

A decoder for decoding the point cloud data;

A renderer for rendering the point cloud data; Containing,

Point cloud data receiving device.
The method of claim 16,

The decoder,

Further comprising a space reconstruction unit for spatially reconstructing the area of the point cloud data based on a division unit,

Point cloud data receiving device.
The method of claim 16,

When the division unit is a tile, the spatial reconstruction unit

Performs an operation of spatially reconstructing data of the point cloud based on the tile, and a quantization value for the tile is applied to the tile,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the tile,

Point cloud data receiving device.
The method of claim 16,

When the division unit is a slice, the spatial reconstruction unit

Performs an operation of spatially reconstructing the data of the point cloud based on the slice, and a quantization value for the slice is applied to the slice,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the slice,

Point cloud data receiving device.
The method of claim 16,

When the division unit is a block, the spatial reconstruction unit

Performs an operation of spatially reconstructing the data of the point cloud based on the block, and a quantization value for the block is applied to the block,

The bitstream further includes a parameter related to the point cloud data,

The parameter includes information on a spatial division operation based on the block,

Point cloud data receiving device.