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

Abstract

A decoding method according to embodiments may include the step of receiving a bitstream containing point cloud data; and the step of decoding the point cloud data. An encoding method according to embodiments may include the step of encoding point cloud data; and the step of transmitting a bitstream containing the point cloud data.

Description

Technology Field

[0001] The embodiments relate to a method and apparatus for processing point cloud content. Background Technology

[0002] Point cloud content is content represented as a point cloud, which is a set of points belonging to a coordinate system that represents three-dimensional space. Point cloud content can represent three-dimensional media and is used to provide various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving services. However, representing point cloud content requires tens of thousands to hundreds of thousands of point data points. Therefore, a method is required to efficiently process a vast amount of point data. The problem to be solved

[0003] The embodiments provide an apparatus and a method for efficiently processing point cloud data. The embodiments provide a method and apparatus for processing point cloud data to address latency and encoding / decoding complexity.

[0004] However, the scope of rights of the embodiments is not limited to the technical problems described above, and may be extended to other technical problems that a person skilled in the art can infer based on the entire content described. means of solving the problem

[0005] A method for transmitting point cloud data according to embodiments may include the step of encoding point cloud data and the step of transmitting a bitstream containing point cloud data. A method for receiving point cloud data according to embodiments may include the step of receiving a bitstream containing point cloud data and the step of decoding point cloud data. Effects of the invention

[0006] The device and method according to the embodiments can process point cloud data with high efficiency.

[0007] The device and method according to the embodiments can provide a high-quality point cloud service.

[0008] The device and method according to the embodiments can provide point cloud content for providing general-purpose services such as VR services and autonomous driving services. Brief explanation of the drawing

[0009] Drawings are included to further understand the embodiments, and the drawings illustrate the embodiments along with descriptions related to the embodiments. For a better understanding of the various embodiments described below, one must refer to the description of the embodiments below in relation to the following drawings, which include parts corresponding to similar reference numerals throughout the drawings. FIG. 1 shows an example of a point cloud content provision system according to embodiments. FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments. FIG. 3 shows an example of a point cloud encoder according to embodiments. FIG. 4 shows examples of octree and occupancy codes according to embodiments. Figure 5 shows an example of a point configuration by LOD according to embodiments. Figure 6 shows an example of a point configuration by LOD according to embodiments. FIG. 7 shows an example of a point cloud decoder according to embodiments. FIG. 8 is an example of a transmission device according to embodiments. FIG. 9 is an example of a receiving device according to embodiments. FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments. FIG. 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments. FIG. 12 illustrates partial point cloud bitstream transmission and decoding according to embodiments. FIG. 13 illustrates scalable decoding of point cloud data according to embodiments. FIG. 14 shows scalable coding according to embodiments. FIG. 15 shows a group of layers for scalable transmission according to embodiments. FIG. 16 shows partial geometry and partial attributes according to embodiments. FIG. 17 shows partial geometry and partial attributes according to embodiments. FIG. 18 shows partial geometry and partial attributes according to embodiments. FIG. 19 shows partial geometry and partial attributes according to embodiments. FIG. 20 shows a bitstream including point cloud data and parameters according to embodiments. FIG. 21 shows scalable lifting LoD generation information according to embodiments. FIG. 22 shows a set of attribute parameters according to embodiments. FIG. 23 shows an encoder (transmitting device) according to embodiments. FIG. 24 shows a decoder (receiving device) according to embodiments. FIG. 25 shows a scalable lifting according to the embodiments. FIG. 26 shows subgroup LoD sampling considering the missing layer according to the embodiments. FIG. 27 shows FGS-related encoder and decoder use cases according to embodiments. FIG. 28 shows FGS-related encoder and decoder use cases according to embodiments. FIG. 29 shows a set of sequence parameters according to the embodiments. FIG. 30 shows a set of sequence parameters according to the embodiments. FIG. 31 shows a encoding method according to embodiments. FIG. 32 illustrates a decoding method according to embodiments. Specific details for implementing the invention

[0010] 발명의 실시를 위한 최선의 형태

[0011] Preferred embodiments of the embodiments are described in detail, and examples thereof are shown in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to describe preferred embodiments of the embodiments rather than merely embodiments that may be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it is obvious to those skilled in the art that the embodiments may be practiced without these details.

[0012] Most terms used in the embodiments are selected from those commonly used in the field, but some terms are chosen at the applicant's discretion, and their meanings are described in detail in the following description as necessary. Accordingly, the embodiments should be understood based on the intended meaning of the terms, rather than their mere names or meanings.

[0013] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0014] 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) can communicate via wired or wireless means to transmit and receive point cloud data.

[0015] A transmission device (10000) according to embodiments can acquire, process, and transmit point cloud video (or point cloud content). According to embodiments, the transmission device (10000) may include a fixed station, a base transceiver system (BTS), a network, an AI (Artificial Intelligence) device and / or system, a robot, an AR / VR / XR device and / or server, etc. Additionally, according to embodiments, the transmission device (10000) may include a device that communicates with a base station and / or other wireless devices using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a mobile device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0016] A transmission device (10000) according to embodiments includes a point cloud video acquisition unit (10001), a point cloud video encoder (10002), and / or a transmitter (or communication module), 10003.

[0017] A point cloud video acquisition unit (10001) according to the embodiments acquires a point cloud video through processing steps such as capture, synthesis, or generation. The point cloud video is a point cloud content represented 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, etc. The point cloud video according to the embodiments may include one or more frames. A 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.

[0018] A point cloud video encoder (10002) according to the embodiments encodes the obtained point cloud video data. The point cloud video encoder (10002) can encode the point cloud video data based on point cloud compression coding. The point cloud compression coding according to the 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. Furthermore, the point cloud compression coding according to the embodiments is not limited to the embodiments described above. The point cloud video encoder (10002) can output a bitstream containing the encoded point cloud video data. The bitstream may include not only the encoded point cloud video data but also signaling information related to the encoding of the point cloud video data.

[0019] A transmitter (10003) according to the embodiments transmits a bitstream containing encoded point cloud video data. The bitstream according to the embodiments is encapsulated into a file or segment (e.g., a streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not illustrated in the drawings, the transmission device (10000) may include an encapsulation unit (or encapsulation module) that performs an encapsulation operation. Additionally, according to the embodiments, the encapsulation unit may be included in the transmitter (10003). According to the embodiments, the file or segment may be transmitted to a receiving device (10004) via a network or stored on a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to the embodiments can communicate wired or wirelessly with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G. Additionally, the transmitter (10003) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). Additionally, the transmission device (10000) can transmit encapsulated data according to an on-demand method.

[0020] A receiving device (10004) according to embodiments includes a receiver (10005), a point cloud video decoder (10006), and / or a renderer (10007). According to embodiments, the receiving device (10004) may include a device, robot, vehicle, AR / VR / XR device, mobile device, home appliance, IoT (Internet of Thing) device, AI device / server, etc., that communicates with a base station and / or other wireless device using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0021] A receiver (10005) according to the embodiments receives a bitstream containing point cloud video data or a file / segment containing the bitstream from a network or a storage medium. The receiver (10005) can perform necessary data processing operations according to a network system (e.g., a communication network system such as 4G, 5G, 6G, etc.). The receiver (10005) according to the embodiments can output a bitstream by decapsulating the received file / segment. Additionally, according to the embodiments, the receiver (10005) may include a decapsulation unit (or decapsulation module) for performing a decapsulation operation. Additionally, the decapsulation unit may be implemented as an element (or component) separate from the receiver (10005).

[0022] A point cloud video decoder (10006) decodes a bitstream containing point cloud video data. The point cloud video decoder (10006) can decode the point cloud video data according to the way the point cloud video data is encoded (e.g., the reverse process of the operation of a point cloud video encoder (10002)). Accordingly, the point cloud video decoder (10006) can decode the point cloud video data by performing point cloud decompression coding, which is the reverse process of point cloud compression. Point cloud decompression coding includes G-PCC coding.

[0023] The renderer (10007) renders the decoded point cloud video data. The renderer (10007) can render not only the point cloud video data but also audio data to output point cloud content. According to embodiments, the renderer (10007) may include a display for displaying the point cloud content. According to embodiments, the display may not be included in the renderer (10007) but may be implemented as a separate device or component.

[0024] The arrows indicated by dotted lines in the drawing represent the transmission path of feedback information obtained from the receiving device (10004). The feedback information is information intended to reflect interaction with a user consuming point cloud content, and includes user information (e.g., head orientation information), viewport information, etc. In particular, if the point cloud content is content for a service requiring interaction with a user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitting side (e.g., the transmitting device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used in the receiving device (10004) as well as the transmitting device (10000), or it may not be provided.

[0025] Head orientation information according to the embodiments is information regarding the user's head position, direction, angle, movement, etc. The receiving device (10004) according to the embodiments can calculate viewport information based on the head orientation information. Viewport information is information about the area of ​​the point cloud video that the user is looking at. The viewpoint refers to the point where the user is looking at the point cloud video, and may mean the exact center point of the viewport area. That is, the viewport is an area centered on the viewpoint, and the size and shape of the area can be determined by the Field Of View (FOV). Therefore, the receiving device (10004) can extract viewport information based on the vertical or horizontal FOV supported by the device in addition to the head orientation information. In addition, the receiving device (10004) performs gaze analysis, etc., to check the user's point cloud consumption method, the point cloud video area the user is looking at, the gaze time, etc. According to embodiments, the receiving device (10004) may transmit feedback information including gaze analysis results to the transmitting device (10000). According to embodiments, the feedback information may be obtained during the rendering and / or display process. According to embodiments, the feedback information may be obtained by one or more sensors included in the receiving device (10004). Also, according to embodiments, the feedback information may be obtained by the renderer (10007) or a separate external element (or device, component, etc.). The dotted line in FIG. 1 indicates the process of transmitting the feedback information obtained from the renderer (10007). The point cloud content providing system may process (encode / decode) point cloud data based on the feedback information. Accordingly, the point cloud video data decoder (10006) may perform a decoding operation based on the feedback information.Additionally, the receiving device (10004) can transmit feedback information to the transmitting device (10000). The transmitting device (10000) (or the point cloud video data encoder (10002)) can perform an encoding operation based on the feedback information. Thus, the point cloud content providing system can efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information without processing (encoding / decoding) all point cloud data, and provide point cloud content to the user.

[0026] According to embodiments, the transmission device (10000) may be referred to as an encoder, transmission device, transmitter, etc., and the receiving device (10004) may be referred to as a decoder, receiving device, receiver, etc.

[0027] Point cloud data processed in the point cloud content providing system of FIG. 1 according to embodiments (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0028] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented using hardware, software, processors, and / or combinations thereof.

[0029] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0030] The block diagram of FIG. 2 illustrates the operation of the point cloud content provision system described in FIG. 1. As described above, the point cloud content provision system can process point cloud data based on point cloud compression coding (e.g., G-PCC).

[0031] A point cloud content providing system according to the embodiments (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can acquire a point cloud video (20000). The point cloud video is represented as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to the embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. If the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply file contains point cloud data such as the geometry and / or attributes of the points. The geometry includes the positions of the points. The position of each point may be represented by parameters (e.g., values ​​of the X-axis, Y-axis, and Z-axis, respectively) representing a three-dimensional coordinate system (e.g., a coordinate system consisting of XYZ axes). Attributes include attributes of points (e.g., texture information, color (YCbCr or RGB), reflectance (r), transparency, etc. of each point). A point has one or more attributes (or properties). For example, a point may have one attribute which is color, or two attributes which are color and reflectance. According to embodiments, geometry may be referred to as positions, geometry information, geometry data, etc., and attributes may be referred to as attributes, attribute information, attribute data, etc.In addition, a point cloud content provision system (e.g., a point cloud transmission device (10000) or a point cloud video acquisition unit (10001)) can obtain point cloud data from information related to the acquisition process of point cloud video (e.g., depth information, color information, etc.).

[0032] A point cloud content providing system (e.g., a transmission device (10000) or a point cloud video encoder (10002)) according to embodiments can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data may include geometry and attributes of points. Accordingly, the point cloud content providing system can output a geometry bitstream by performing geometry encoding to encode geometry. The point cloud content providing system can output an attribute bitstream by performing attribute encoding to encode attributes. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. The bitstream according to the embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0033] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a transmitter (10003)) can transmit encoded point cloud data (20002). As described in FIG. 1, the encoded point cloud data can be represented as a geometry bitstream and an attribute bitstream. Additionally, the encoded point cloud data can be transmitted in the form of a bitstream along with signaling information related to the encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). Additionally, the point cloud content providing system can encapsulate the bitstream transmitting the encoded point cloud data and transmit it in the form of a file or segment.

[0034] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream containing encoded point cloud data. Additionally, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0035] A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode encoded point cloud data (e.g., a geometry bitstream, an attribute bitstream) transmitted as a bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode point cloud video data based on signaling information related to the encoding of point cloud video data included in the bitstream. A point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the geometry bitstream to restore the positions (geometry) of the points. A point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore the attributes of the points. A point cloud content delivery system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can restore a point cloud video based on positions according to the restored geometry and decoded attributes.

[0036] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a renderer (10007)) can render decoded point cloud data (20004). The point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) can render geometry and attributes decoded through a decoding process according to various rendering methods. Points of the point cloud content may be rendered as vertices having a certain thickness, cubes having a specific minimum size with the vertex location as the center, or circles with the vertex location as the center, etc. All or part of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0037] A point cloud content providing system (e.g., a receiving device (10004)) according to the embodiments can obtain feedback information (20005). The point cloud content providing system can encode and / or decode point cloud data based on the feedback information. Since the feedback information and the operation of the point cloud content providing system according to the embodiments are the same as the feedback information and operation described in FIG. 1, a detailed description is omitted.

[0038] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0039] FIG. 3 shows an example of the point cloud video encoder (10002) of FIG. 1. The point cloud encoder reconstructs point cloud data (e.g., positions and / or attributes of points) and performs encoding operations to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) according to network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps in the case of 30 fps), the point cloud content delivery system may not be able to stream the content in real time. Therefore, the point cloud content delivery system may reconstruct the point cloud content based on a maximum target bitrate to provide it according to the network environment.

[0040] As described in FIGS. 1 and 2, the point cloud encoder can perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.

[0041] The point cloud encoder according to the embodiments comprises a coordinate system transformation unit (Transformation Coordinates, 30000), a quantization unit (Quantize and Remove Points (Voxelize), 30001), an octree analysis unit (Analyze Octree, 30002), a surface approximation analysis unit (Analyze Surface Approximation, 30003), an arithmetic encoder (Arithmetic Encode, 30004), a geometry reconstruction unit (Reconstruct Geometry, 30005), a color transformation unit (Transform Colors, 30006), an attribute transformation unit (Transfer Attributes, 30007), a RAHT transformation unit (30008), an LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011). Includes an and / or arithmetic encoder (Arithmetic Encode, 30012).

[0042] The coordinate system transformation unit (30000), quantization unit (30001), octree analysis unit (30002), surface approximation analysis unit (30003), arismetic encoder (30004), and geometry reconstruction unit (30005) can perform geometry encoding. Geometry encoding according to the embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied optionally or in combination. Additionally, geometry encoding is not limited to the above examples.

[0043] As illustrated in the drawings, the coordinate system conversion unit (30000) according to the embodiments receives positions and converts them into a coordinate system. For example, the positions can be converted into position information in a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system). The position information in the three-dimensional space according to the embodiments may be referred to as geometry information.

[0044] The quantization unit (30001) according to the embodiments quantizes the geometry. For example, the quantization unit (30001) can quantize points based on the minimum position values ​​of all points (e.g., minimum values ​​on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation to find the nearest integer value by multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value and then performing rounding down or rounding up. Thus, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to the embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. Just as the minimum unit containing 2D image / video information is a pixel, the points of the point cloud content (or 3D point cloud video) according to the embodiments may be contained in one or more voxels. A voxel is a combination of volume and pixel, and refers to a three-dimensional cubic space that is generated when a three-dimensional space is divided into units (unit=1.0) based on axes representing the three-dimensional space (e.g., X-axis, Y-axis, Z-axis). The quantization unit (40001) can match groups of points in the three-dimensional space to voxels. According to embodiments, a single voxel may contain only one point. According to embodiments, a single voxel may contain one or more points. In addition, to represent a single voxel as a single point, the position of the center of the voxel can be set based on the positions of one or more points included in the voxel. In this case, the attributes of all positions included in the voxel can be combined and assigned to the voxel.

[0045] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent the voxels in an octree structure. The octree structure represents points matched to the voxels based on an octree structure.

[0046] The surface approximation analysis unit (30003) according to the embodiments can analyze and approximate an octree. The octree analysis and approximation according to the embodiments is a process of analyzing to voxelize an area containing multiple points in order to efficiently provide octree and voxelization.

[0047] An arithmetic encoder (30004) according to the embodiments entropy-encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. As a result of the encoding, a geometry bitstream is generated.

[0048] The color conversion unit (30006), attribute conversion unit (30007), RAHT conversion unit (30008), LOD generation unit (30009), lifting conversion unit (30010), coefficient quantization unit (30011) and / or arismetic encoder (30012) perform attribute encoding. As described above, a point may have one or more attributes. The attribute encoding according to the embodiments is applied equally to the attributes of a point. However, if a single attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. The attribute encoding according to the embodiments may include color conversion coding, attribute conversion coding, Region Adaptive Hierarchial Transform (RAHT) coding, prediction transformation (Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) coding, and lifting transformation (interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transformation coding, and lifting transformation coding may be used optionally, or a combination of one or more of the codings may be used. Furthermore, the attribute encoding according to the embodiments is not limited to the examples described above.

[0049] The color conversion unit (30006) according to the embodiments performs color conversion coding that converts color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) can convert the format of color information (e.g., convert from RGB to YCbCr). The operation of the color conversion unit (30006) according to the embodiments may be applied optionally depending on the color values ​​included in attributes.

[0050] The geometry reconstruction unit (30005) according to the embodiments reconstructs (decompresses) an octree and / or an approximated octree. The geometry reconstruction unit (30005) reconstructs an octree / voxel based on the results of analyzing the distribution of points. The reconstructed octree / voxel may be referred to as the reconstructed geometry (or restored geometry).

[0051] The attribute transformation unit (30007) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. As described above, since attributes are dependent on geometry, the attribute transformation unit (30007) can transform attributes based on reconstructed geometry information. For example, the attribute transformation unit (30007) can transform the attributes of a point at a position based on the position value of a point included in a voxel. As described above, when the position of the center point of a voxel is set based on the positions of one or more points included in a voxel, the attribute transformation unit (30007) transforms the attributes of one or more points. When trisoop geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the trisoop geometry encoding.

[0052] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific location / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight based on the distance from the center point to each point when calculating the average value. Thus, each voxel has a position and a calculated attribute (or attribute value).

[0053] The attribute conversion unit (30007) can search for neighboring points within a specific location / radius from the position of the center point of each voxel based on a KD tree or a Molton code. A KD tree is a binary search tree that supports a data structure capable of managing points based on their positions to enable rapid Nearest Neighbor Search (NNS). A Molton code is generated by representing the coordinate values ​​(e.g., (x, y, z)) representing the 3D positions of all points as bit values ​​and mixing the bits. For example, if the coordinate values ​​representing the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). When the bit values ​​are mixed according to the bit indices in the order of z, y, and x, it becomes 010001000111. When this value is represented in decimal, it becomes 1095. That is, the Molton code value of the point with coordinates (5, 9, 1) is 1095. The attribute transformation unit (30007) sorts the points based on the Molton code value and can perform shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if shortest neighbor search (NNS) is required in other transformation processes for attribute coding, a KD tree or Molton code is utilized.

[0054] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0055] The RAHT transformation unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transformation unit (30008) can predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.

[0056] The LOD generation unit (30009) according to the embodiments generates a Level of Detail (LOD) to perform predictive transformation coding. The LOD according to the embodiments represents the degree of detail of the point cloud content, and indicates that the smaller the LOD value, the lower the detail of the point cloud content, and the larger the LOD value, the higher the detail of the point cloud content. Points can be classified according to the LOD.

[0057] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms the attributes of the point cloud based on weights. As described above, the lifting transformation coding may be applied optionally.

[0058] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0059] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0060] The elements of the point cloud encoder of FIG. 3 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud encoder of FIG. 3. One or more memories according to the embodiments may include high-speed random access memory and may include non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0061] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0062] As described in FIGS. 1 to 3, a point cloud content providing system (point cloud video encoder (10002)) or a point cloud encoder (e.g., an octree analysis unit (30002)) performs octree geometry coding based on an octree structure (or octree coding) to efficiently manage the area and / or position of a voxel.

[0063] The top of FIG. 4 shows an octree structure. The three-dimensional space of the point cloud content according to the embodiments is represented by the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). The octree structure has two poles (0,0,0) and (2 d , 2 d , 2 d It is generated by recursively subdividing the bounding box (cubical axis-aligned bounding box) defined by ). 2d can be set to the value that constitutes the smallest bounding box enclosing all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined according to the following equation. In the equation below, (x int n , y int n , z int n ) represents the positions (or position values) of quantized points.

[0064] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0065] As illustrated in the middle of the top of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is represented as a cube having six faces. As illustrated in the right of the top of Fig. 4, each of the eight spaces is further divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Thus, each space is again divided into eight smaller spaces. The divided smaller spaces are also represented as cubes having six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0066] The bottom of Fig. 4 shows the occupancy code of an octree. The occupancy code of an octree is generated to indicate whether each of the eight partitioned spaces resulting from the partitioning of a single space contains at least one point. Therefore, one occupancy code is represented by eight child nodes. Each child node represents the occupancy of the partitioned space, and the child node has a value of 1 bit. Thus, the occupancy code is represented as an 8-bit code. That is, if the space corresponding to the child node contains at least one point, the node has a value of 1. If the space corresponding to the child node does not contain a point (empty), the node has a value of 0. Since the occupancy code shown in Fig. 4 is 00100001, it indicates that the spaces corresponding to the 3rd and 8th child nodes among the eight child nodes each contain at least one point. As illustrated in the drawing, the 3rd child node and the 8th child node each have 8 child nodes, and each child node is represented by an 8-bit Occupancy code. The drawing indicates that the Occupancy code of the 3rd child node is 10000111 and the Occupancy code of the 8th child node is 01001111. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30004)) can entropy-encode the Occupancy code. Additionally, to increase compression efficiency, the point cloud encoder can intra- / inter-encode the Occupancy code. A receiving device according to the embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs the octree based on the Occupancy code.

[0067] A point cloud encoder according to the embodiments (e.g., the point cloud encoder of FIG. 3, or the octree analysis unit (30002)) can perform voxelization and octree coding to store the positions of the points. However, since points in a three-dimensional space are not always evenly distributed, there may be specific areas where few points exist. Therefore, performing voxelization on the entire three-dimensional space is inefficient. For example, if there are almost no points in a specific area, there is no need to perform voxelization up to that area.

[0068] Accordingly, the point cloud encoder according to the embodiments can perform direct coding, which directly codes the positions of points included in a specific region (or nodes excluding leaf nodes of an octree) without performing voxelization on the aforementioned specific region. The coordinates of the points directly coded according to the embodiments are referred to as the Direct Coding Mode (DCM). Additionally, the point cloud encoder according to the embodiments can perform trisoup geometry encoding, which reconstructs the positions of points within a specific region (or node) based on voxels using a surface model. Trisoup geometry encoding is a geometry encoding that represents an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments may be performed optionally. In addition, direct coding and trisoop geometry encoding according to the embodiments can be performed in combination with octree geometry coding (or octree coding).

[0069] To perform direct coding, the option to use direct mode for applying direct coding must be enabled, the node to which direct coding is to be applied must not be a leaf node, and there must be points within a specific node that are below a threshold. In addition, the total number of points subject to direct coding must not exceed a preset threshold. If the above conditions are satisfied, the point cloud encoder (or arismetic encoder (30004)) according to the embodiments can entropy-code the positions (or position values) of the points.

[0070] A point cloud encoder according to the embodiments (e.g., a surface approximation analysis unit (30003)) can determine a specific level of an octree (where the level is smaller than the depth d of the octree) and, starting from that level, perform trisoop geometry encoding to reconstruct the position of points within a node region based on voxels using a surface model (trisoop mode). The point cloud encoder according to the embodiments can specify the level to which trisoop 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 trisoop mode. That is, the point cloud encoder according to the embodiments can operate in trisoop mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic region of nodes at a specified level according to the embodiments is referred to as a block. A block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented as a surface. A surface according to the embodiments may intersect each edge of the block at most once.

[0071] Since one block has 12 edges, there are at least 12 intersection points within one block. Each intersection point is referred to as a vertex. A vertex along an edge is detected if there is at least one occupied voxel adjacent to that edge among all blocks sharing that edge. An occupied voxel according to the embodiments means a voxel containing a point. The position of a vertex detected along an edge is the average position along the edge of all voxels adjacent to that edge among all blocks sharing that edge.

[0072] When a vertex is detected, the point cloud encoder according to the embodiments can entropy-code the edge start point (x, y, z), edge direction vector (Δx, Δy, Δz), and vertex position value (relative position value within the edge). When trisoop geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., geometry reconstruction unit (30005)) can generate restored geometry (reconstructed geometry) by performing triangle reconstruction, up-sampling, and voxelization processes.

[0073] The vertices located on the edges of the block determine the surface passing through the block. The surface according to the embodiments is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by triangles based on the edge start point, the edge direction vector, and the vertex position value. The triangle reconstruction process is as follows: ① calculate the centroid value of each vertex, ② subtract the centroid value from each vertex value, ③ square the result, and add all the result together.

[0074]

[0075] The minimum sum is calculated, and a projection process is performed along the axis where the minimum value is located. For example, if the x-element is at its minimum, each vertex is projected along the x-axis relative to the center of the block and onto the (y, z) plane. If the value obtained from projecting onto the (y, z) plane is (ai, bi), the θ value is calculated using atan2(bi, ai), and the vertices are aligned based on the θ value. The table below shows the combinations of vertices to generate triangles depending on the number of vertices. The vertices are aligned in order from 1 to n. The table below indicates that for four vertices, two triangles can be formed depending on the combination of vertices. The first triangle can be formed from the 1st, 2nd, and 3rd vertices among the aligned vertices, and the second triangle can be formed from the 3rd, 4th, and 1st vertices among the aligned vertices.

[0076] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0077] n triangles

[0078] 3 (1,2,3)

[0079] 4 (1,2,3), (3,4,1)

[0080] 5 (1,2,3), (3,4,5), (5,1,3)

[0081] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0082] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0083] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0084] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0085] 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)

[0086] 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)

[0087] 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)

[0088] The upsampling process is performed to voxelize by adding intermediate points along the edges of the triangle. Additional points are generated based on the upsampling factor value and the width of the block. The additional points are referred to as refined vertices. A point cloud encoder according to the embodiments can voxelize the refined vertices. Additionally, the point cloud encoder can perform attribute encoding based on the voxelized positions (or position values).

[0089] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0090] As described in FIGS. 1 to 4, the encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the arrangement of the direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trisoop geometry encoding is applied, the geometry reconstruction process involves triangle reconstruction, upsampling, and voxelization. Since attributes depend on geometry, attribute encoding is performed based on the reconstructed geometry.

[0091] A point cloud encoder (e.g., an LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to the LOD. The left side of the drawing shows the original point cloud content. The second figure from the left of the drawing shows the distribution of points of the lowest LOD, and the rightmost figure of the drawing shows the distribution of points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, while the points of the highest LOD are densely distributed. In other words, according to the direction of the arrow indicated at the bottom of the drawing, as the LOD increases, the spacing (or distance) between points becomes shorter.

[0092] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0093] As described in FIGS. 1 to 5, a point cloud content providing system or a point cloud encoder (e.g., a point cloud video encoder (10002), the point cloud encoder of FIG. 3, or an LOD generation unit (30009)) can generate an LOD. The LOD is generated by reorganizing points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distances). The LOD generation process is performed in a point cloud decoder as well as a point cloud encoder.

[0094] The top of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in three-dimensional space. The Original Order in Fig. 6 represents the order of points P0 to P9 prior to LOD generation. The LOD-based Order in Fig. 6 represents the order of points following LOD generation. Points are rearranged by LOD. Additionally, higher LODs include points belonging to lower LODs. As illustrated in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes the points of LOD0 and P1, P6, and P3. LOD2 includes the points of LOD0, the points of LOD1, and P9, P8, and P7.

[0095] As described in FIG. 3, the point cloud encoder according to the embodiments can perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

[0096] The point cloud encoder according to the embodiments can generate predictors for points and perform predictive transformation coding to set the predicted attribute (or predicted attribute value) of each point. That is, N predictors can be generated for N points. The predictor according to the embodiments can calculate a weight (=1 / distance) value based on the LOD value of each point, indexing information for neighboring points within a set distance per LOD, and the distance value to the neighboring points.

[0097] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the values ​​obtained by multiplying the attributes (or attribute values, e.g., color, reflectance, etc.) of neighboring points set in the predictor of each point by a weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) can quantize and inverse quantize the residual values ​​(which may be referred to as residual attributes, residual attribute values, attribute prediction residual values, etc.) obtained by subtracting the predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is as shown in the following table.

[0098] graph. Attribute prediction residuals quantization pseudo code

[0099] int PCCQuantization(int value, int quantStep) {

[0100] if( value >=0) {

[0101] return floor(value / quantStep + 1.0 / 3.0);

[0102] } else {

[0103] return -floor(-value / quantStep + 1.0 / 3.0);

[0104] }

[0105] }

[0106] graph. Attribute prediction residuals inverse quantization pseudo code

[0107] int PCCInverseQuantization(int value, int quantStep) {

[0108] if( quantStep ==0) {

[0109] return value;

[0110] } else {

[0111] return value * quantStep;

[0112] }

[0113] }

[0114] A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the quantized and inversely quantized residual values ​​as described above when there are neighboring points in the predictor of each point. A point cloud encoder according to the embodiments (e.g., an arismetic encoder (30012)) can entropy-code the attributes of the corresponding point without performing the process described above when there are no neighboring points in the predictor of each point.

[0115] A point cloud encoder according to the embodiments (e.g., a lifting transformation unit (30010)) can perform lifting transformation coding by generating a predictor for each point, setting the LOD calculated in the predictor, registering neighboring points, and setting weights based on the distance to neighboring points. The lifting transformation coding according to the embodiments is similar to the prediction transformation coding described above, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to the embodiments is as follows.

[0116] 1) Create an array QW (QuantizationWight) to store the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the current point's predictor weight by the QW value of the predictor index of the neighboring node registered in the predictor.

[0117] 2) Lift prediction process: To calculate the predicted attribute value, the value obtained by multiplying the point's attribute value by a weight is subtracted from the existing attribute value.

[0118] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0119] 4) For all predictors, the calculated weight is additionally multiplied by the weight stored in the QW corresponding to the predictor index, and the resulting weight is accumulated in the update weight array with the neighbor node index. In the update array, the value obtained by multiplying the attribute value of the neighbor node index by the calculated weight is accumulated.

[0120] 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 at the predictor index, and the original attribute value is added back to the divided value.

[0121] 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 (stored in QW) updated through the lift prediction process. A point cloud encoder according to the embodiments (e.g., coefficient quantizer (30011)) quantizes the predicted attribute value. Additionally, a point cloud encoder (e.g., arismetic encoder (30012)) entropies the quantized attribute value.

[0122] A point cloud encoder according to the embodiments (e.g., a RAHT transform unit (30008)) can perform RAHT transform coding to predict attributes of upper-level nodes using attributes associated with nodes at lower levels of the octree. RAHT transform coding is an example of attribute intra-coding through octree backward scanning. A point cloud encoder according to the embodiments scans from voxels to the entire region and repeats the merging process up to the root node, merging voxels into larger blocks at each step. The merging process according to the embodiments is performed only on occupied nodes. The merging process is not performed on empty nodes, and the merging process is performed on the node immediately above the empty node.

[0123] The following equation represents the RAHT transformation matrix. g l x, y, z represents the average attribute value of the voxels at level l. g l x, y, z is g l+1 2x, y, z wa g l+1 2x+1, y, z It can be calculated from g l 2x, y, z wa g l 2x+1, y, z The weights of w1=w l 2x, y, z and w2=w l 2x+1, y, z am.

[0124]

[0125] *g l-1 x, y, z is a low-pass value used in the merging process at the next higher level. hl-1 x, y, z ε₀ are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (400012)). The weights are w l-1 x, y, z =w l 2x, y, z +w l 2x+1, y, z It is calculated as. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0126]

[0127] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0128] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0129] The point cloud decoder illustrated in FIG. 7 is an example of a point cloud decoder and can perform a decoding operation, which is the reverse process of the encoding operation of the point cloud encoder described in FIG. 1 to 6.

[0130] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0131] A point cloud decoder according to the embodiments comprises an arithmetic decoder (7000), a synthesize octree (7001), a synthesize surface approximation (7002), a reconstruct geometry (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), an LOD generater (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0132] An arismetic decoder (7000), an octree composite unit (7001), a surface offset composite unit (7002), a geometry reconstruction unit (7003), and a coordinate system inverse transformation unit (7004) can perform geometry decoding. Geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied optionally. Additionally, geometry decoding is not limited to the above examples and is performed as the reverse process of geometry encoding described in FIGS. 1 through 6.

[0133] The arismetic decoder (7000) according to the embodiments decodes the received geometry bitstream based on arismetic coding. The operation of the arismetic decoder (7000) corresponds to the reverse process of the arismetic encoder (30004).

[0134] The octree synthesis unit (7001) according to the embodiments can generate an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding the geometry obtained as a result of decoding). A specific description of the Occupancy code is as described in FIGS. 1 to 6.

[0135] The surface off-relation synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when trisoop geometry encoding is applied.

[0136] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on a surface and / or decoded geometry. As described in FIGS. 1 through 6, direct coding and trisoop geometry encoding are applied optionally. Accordingly, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding has been applied. In addition, when trisoop geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing reconstruction operations of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. Specific details are omitted as they are the same as those described in FIG. 4. The restored geometry may include a point cloud picture or frame that does not contain attributes.

[0137] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of the points by transforming the coordinate system based on the restored geometry.

[0138] The arismetic decoder (7005), inverse quantization unit (7006), RAHT transformation unit (7007), LOD generation unit (7008), inverse lifting unit (7009), and / or color inverse transformation unit (7010) can perform attribute decoding as described in FIG. 10. Attribute decoding according to the embodiments may include Region Adaptive Hierarchial Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbour prediction-Prediction Transform) decoding, and interpolation-based hierarchical nearest-neighbour prediction with an update / lifting step (Lifting Transform) decoding. The three decodings described above may be used optionally, or a combination of one or more decodings may be used. Furthermore, attribute decoding according to the embodiments is not limited to the examples described above.

[0139] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0140] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attribute obtained as a result of decoding and outputs the inverse quantized attributes (or attribute values). Inverse quantization may be optionally applied based on the attribute encoding of the point cloud encoder.

[0141] According to embodiments, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can process the reconstructed geometry and inverse quantized attributes. As described above, the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009) can optionally perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0142] The color inverse conversion unit (7010) according to the embodiments performs inverse conversion coding to inversely convert the color value (or texture) included in the decoded attributes. The operation of the color inverse conversion unit (7010) may be selectively performed based on the operation of the color conversion unit (30006) of the point cloud encoder.

[0143] The elements of the point cloud decoder of FIG. 7 may be implemented in hardware, software, firmware, or a combination thereof, comprising one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not illustrated in the drawing. One or more processors may perform at least one of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Additionally, one or more processors may operate or execute a set of software programs and / or instructions for performing the operations and / or functions of the elements of the point cloud decoder of FIG. 7.

[0144] FIG. 8 is an example of a transmission device according to embodiments.

[0145] The transmission device illustrated in FIG. 8 is an example of the transmission device (10000) of FIG. 1 (or the point cloud encoder of FIG. 3). The transmission device illustrated in FIG. 8 can perform at least one of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in FIG. 1 to 6. A transmission device according to embodiments may include a data input unit (8000), a quantization processing unit (8001), a voxelization processing unit (8002), an octree occupancy code generation unit (8003), a surface model processing unit (8004), an intra / inter coding processing unit (8005), an arithmetic coder (8006), a metadata processing unit (8007), a color conversion processing unit (8008), an attribute conversion processing unit (or attribute conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011) and / or a transmission processing unit (8012).

[0146] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform an operation and / or acquisition method identical or similar to the operation and / or acquisition method of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0147] The data input unit (8000), quantization processing unit (8001), voxelization processing unit (8002), octree occupancy code generation unit (8003), surface model processing unit (8004), intra / inter coding processing unit (8005), and arithmetic coder (8006) perform geometry encoding. Since the geometry encoding according to the embodiments is identical or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0148] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., location values ​​of points, or position values). The operation and / or quantization of the quantization processing unit (8001) is the same or similar to the operation and / or quantization of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0149] The voxelization processing unit (8002) according to the embodiments voxelizes the position values ​​of the quantized points. The voxelization processing unit (80002) may perform the same or similar operation and / or process as the operation and / or voxelization process of the quantization unit (30001) described in FIG. 3. The specific description is the same as that described in FIG. 1 to 6.

[0150] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on an octree structure. The octree occupancy code generation unit (8003) can generate an occupancy code. The octree occupancy code generation unit (8003) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (or octree analysis unit (30002)) described in FIGS. 3 and 4. The specific description is the same as that described in FIGS. 1 through 6.

[0151] The surface model processing unit (8004) according to the embodiments can perform trisup geometry encoding that reconstructs the positions of points within a specific region (or node) based on a voxel based on a surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to the operations and / or methods of the point cloud encoder (e.g., surface approximation analysis unit (30003)) described in FIG. 3. The specific description is the same as that described in FIG. 1 through 6.

[0152] According to the embodiments, the intra / inter coding processing unit (8005) can intra / inter code point cloud data. The intra / inter coding processing unit (8005) can perform coding identical or similar to the intra / inter coding described in FIG. 7. The specific description is the same as that described in FIG. 7. According to the embodiments, the intra / inter coding processing unit (8005) may be included in an arismetic coder (8006).

[0153] An arismetic coder (8006) according to the embodiments entropy-encodes an octree and / or approximated octree of point cloud data. For example, the encoding method includes an arismetic encoding method. The arismetic coder (8006) performs the same or similar operation and / or method as the arismetic encoder (30004).

[0154] A metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides it to necessary processing processes such as geometry encoding and / or attribute encoding. Additionally, a metadata processing unit (8007) according to the embodiments may generate and / or process signaling information related to geometry encoding and / or attribute encoding. The signaling information according to the embodiments may be encoded separately from geometry encoding and / or attribute encoding. Additionally, the signaling information according to the embodiments may be interleaved.

[0155] The color conversion processing unit (8008), attribute conversion processing unit (8009), prediction / lifting / RAHT conversion processing unit (8010), and arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is identical or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description is omitted.

[0156] The color conversion processing unit (8008) according to the embodiments performs color conversion coding that converts color values ​​included in attributes. The color conversion processing unit (8008) may perform color conversion coding based on reconstructed geometry. The description of the reconstructed geometry is the same as that described in FIGS. 1 through 6. In addition, it performs the same or similar operation and / or method as the operation and / or method of the color conversion unit (30006) described in FIG. 3. A detailed description is omitted.

[0157] The attribute transformation processing unit (8009) according to the embodiments performs attribute transformation that transforms attributes based on positions where geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods identical or similar to the operations and / or methods of the attribute transformation unit (30007) described in FIG. 3. A detailed description is omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to the embodiments may code the transformed attributes by RAHT coding, prediction transformation coding, and lifting transformation coding, or a combination thereof. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one of operations identical or similar to the operations of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the descriptions of predictive transformation coding, lifting transformation coding, and RAHT transformation coding are the same as those described in Figures 1 to 6, so a detailed description is omitted.

[0158] The arismetic coder (8011) according to the embodiments can encode coded attributes based on arismetic coding. The arismetic coder (8011) performs the same or similar operation and / or method as the operation and / or method of the arismetic encoder (300012).

[0159] A transmission processing unit (8012) according to embodiments may transmit each bitstream containing encoded geometry and / or encoded attributes and metadata information, or may transmit the encoded geometry and / or encoded attributes and metadata information by configuring them into a single bitstream. When the encoded geometry and / or encoded attributes and metadata information according to embodiments is configured into a single bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information and slice data, including SPS (Sequence Parameter Set) for sequence-level signaling, GPS (Geometry Parameter Set) for signaling of geometry information coding, APS (Attribute Parameter Set) for signaling of attribute information coding, and TPS (Tile Parameter Set) for tile-level signaling. The slice data may include information regarding one or more slices. One slice according to embodiments is one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 It may include ).

[0160] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0161] According to the embodiments, the TPS may include information regarding each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. The geometry bitstream may include a header and a payload. The header of the geometry bitstream according to the embodiments may include identification information of a parameter set included in the GPS (geom_parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information regarding data included in the payload, etc. As described above, the metadata processing unit (8007) according to the embodiments may generate and / or process signaling information and transmit it to the transmission processing unit (8012). According to the embodiments, the elements performing geometry encoding and the elements performing attribute encoding may share data / information with each other as indicated by the dotted lines. The transmission processing unit (8012) according to the embodiments may perform an operation and / or transmission method identical or similar to the operation and / or transmission method of the transmitter (10003). A detailed explanation is omitted as it is the same as that described in FIGS. 1 and 2.

[0162] FIG. 9 is an example of a receiving device according to embodiments.

[0163] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1 (or the point cloud decoder of FIG. 10 and FIG. 11). The receiving device illustrated in FIG. 9 can perform at least one of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIG. 1 to FIG. 11.

[0164] A receiving device according to the embodiments may include a receiving unit (9000), a receiving processing unit (9001), an arithmetic decoder (9002), an occupancy code-based octree reconstruction processing unit (9003), a surface model processing unit (triangle reconstruction, up-sampling, voxelization) (9004), an inverse quantization processing unit (9005), a metadata parser (9006), an arithmetic decoder (9007), an inverse quantization processing unit (9008), a prediction / lifting / RAHT inverse transformation processing unit (9009), a color inverse transformation processing unit (9010), and / or a renderer (9011). Each component of the decoding according to the embodiments may perform the inverse process of the components of the encoding according to the embodiments.

[0165] A receiver (9000) according to the embodiments receives point cloud data. The receiver (9000) may perform an operation and / or a receiving method identical or similar to the operation and / or receiving method of the receiver (10005) of FIG. 1. A detailed description is omitted.

[0166] A receiving processing unit (9001) according to the embodiments can obtain a geometry bitstream and / or an attribute bitstream from the received data. The receiving processing unit (9001) may be included in the receiving unit (9000).

[0167] The arismetic decoder (9002), the Occupancy code-based octree reconstruction processing unit (9003), the surface model processing unit (9004), and the inverse quantization processing unit (9005) can perform geometry decoding. Since the geometry decoding according to the embodiments is identical or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0168] The arismetic decoder (9002) according to the embodiments can decode a geometry bitstream based on arismetic coding. The arismetic decoder (9002) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7000).

[0169] According to the embodiments, the Occupancy code-based octree reconstruction processing unit (9003) can reconstruct an octree by obtaining an Occupancy code from a decoded geometry bitstream (or information regarding geometry obtained as a result of decoding). The Occupancy code-based octree reconstruction processing unit (9003) performs the same or similar operations and / or methods as the octree synthesis unit (7001) and / or octree generation method. According to the embodiments, the surface model processing unit (9004) can perform trisup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when trisup geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as the surface offset synthesis unit (7002) and / or geometry reconstruction unit (7003).

[0170] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0171] A metadata parser (9006) according to the embodiments can parse metadata included in the received point cloud data, such as setting values, etc. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A specific description of the metadata is omitted as it is the same as described in FIG. 8.

[0172] The arismetic decoder (9007), inverse quantization processing unit (9008), prediction / lifting / RAHT inverse transformation processing unit (9009), and color inverse transformation processing unit (9010) perform attribute decoding. Since attribute decoding is identical or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description is omitted.

[0173] The arismetic decoder (9007) according to the embodiments can decode an attribute bitstream into arismetic coding. The arismetic decoder (9007) can perform decoding of the attribute bitstream based on reconstructed geometry. The arismetic decoder (9007) performs the same or similar operation and / or coding as the operation and / or coding of the arismetic decoder (7005).

[0174] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs the same or similar operation and / or method as the operation and / or inverse quantization method of the inverse quantization unit (7006).

[0175] According to the embodiments, the prediction / lifting / RAHT inverse transformation processing unit (9009) can process the reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transformation processing unit (9009) performs at least one of the same or similar operations and / or decodings as the operations and / or decodings of the RAHT transformation unit (7007), LOD generation unit (7008), and / or inverse lifting unit (7009). According to the embodiments, the color inverse transformation processing unit (9010) performs inverse transformation coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The color inverse transformation processing unit (9010) performs the same or similar operations and / or inverse transformation coding as the operations and / or inverse transformation coding of the color inverse transformation unit (7010). A renderer (9011) according to the embodiments can render point cloud data.

[0176] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0177] The structure of FIG. 10 represents a configuration in which at least one of a server (1060), a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) is connected to a cloud network (1010). The robot (1010), the autonomous vehicle (1020), the XR device (1030), the smartphone (1040), or the home appliance (1050) are referred to as devices. Additionally, the XR device (1030) may correspond to a point cloud data (PCC) device according to the embodiments or may be linked with a PCC device.

[0178] The cloud network (1000) may refer to a network that constitutes part of the cloud computing infrastructure or exists within the cloud computing infrastructure. Here, the cloud network (1000) may be configured using a 3G network, a 4G or LTE (Long Term Evolution) network, or a 5G network, etc.

[0179] The server (1060) is connected to at least one of a robot (1010), an autonomous vehicle (1020), an XR device (1030), a smartphone (1040), a home appliance (1050) and / or an HMD (1070) via a cloud network (1000) and can assist in at least some of the processing of the connected devices (1010 to 1070).

[0180] The HMD (Head-Mount Display) (1070) represents one of the types in which an XR device and / or PCC device according to the embodiments may be implemented. A device of the HMD type 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, etc.

[0181] Hereinafter, various embodiments of the device (1010 to 1050) to which the above-described technology is applied are described. Here, the device (1010 to 1050) illustrated in FIG. 10 may be linked / coupled with a point cloud data transmission / reception device according to the above-described embodiments.

[0182] <PCC+XR>

[0183] The XR / PCC device (1030) may be implemented as a Head-Mount Display (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a stationary robot, or a mobile robot by applying PCC and / or XR (AR+VR) technology.

[0184] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data obtained through various sensors or from an external device to generate position data and attribute data for 3D points, and can render and output an XR object to be output. For example, the XR / PCC device (1030) can output an XR object containing additional information about a recognized object by associating it with the recognized object.

[0185] <PCC+XR+모바일폰>

[0186] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0187] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0188] <PCC+자율주행+XR>

[0189] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0190] An autonomous vehicle (1020) equipped with XR / PCC technology may refer to an autonomous vehicle equipped with means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within the XR images. In particular, the autonomous vehicle (1020) that is the subject of control / interaction within the XR images is distinguished from the XR device (1030) and can be interconnected with it.

[0191] An autonomous vehicle (1020) equipped with means for providing XR / PCC images can acquire sensor information from sensors including cameras and output XR / PCC images generated based on the acquired sensor information. For example, the autonomous vehicle (1020) can provide an XR / PCC object corresponding to a real object or an object in the screen to the occupant by providing an XR / PCC object by outputting an XR / PCC image with a HUD.

[0192] At this time, when the XR / PCC object is displayed on the HUD, at least a portion of the XR / PCC object may be displayed so as to overlap with the actual object to which the occupant's gaze is directed. On the other hand, when the XR / PCC object is displayed on a display provided inside the autonomous vehicle, at least a portion of the XR / PCC object may be displayed so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may display XR / PCC objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, motorcycles, pedestrians, buildings, etc.

[0193] 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.

[0194] In other words, VR technology is a display technology that provides real-world objects or backgrounds solely as CG images. On the other hand, AR technology refers to a technology that displays virtual CG images alongside images of real objects. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects with the real world. However, it is distinguished from AR technology in that while AR technology maintains a clear distinction between real-world objects and virtual objects created from CG images, using virtual objects to complement real-world objects, MR technology regards virtual objects as having the same nature as real-world objects. To give a more specific example, the aforementioned MR technology is applied in hologram services.

[0195] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are also referred to as XR (extended Reality) technology. Therefore, embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. These technologies may utilize encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0196] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0197] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0198] When a point cloud data (PCC) transceiver according to the embodiments is connected to a vehicle for wired or wireless communication, it can receive and process content data related to AR / VR / PCC services that can be provided along with an autonomous driving service, and transmit it to the vehicle. Additionally, when the point cloud data transceiver is mounted on a vehicle, the point cloud transceiver can receive and process content data related to AR / VR / PCC services according to a user input signal received through a user interface device and provide it to the user. A vehicle or a user interface device according to the embodiments can receive a user input signal. The user input signal according to the embodiments may include a signal indicating an autonomous driving service.

[0199] The encoding method / device according to the embodiments includes a transmitting device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), an acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, encoders of FIG. 11 to 13, partial encoding of FIG. 14 to 19, bitstream and parameter generation of FIG. 20 to 22 and FIG. 29 to 30, encoding of FIG. 23 to 28, encoding method of FIG. 31, etc.

[0200] The decoding method / device according to the embodiments includes the receiving device (10004) of FIG. 1, the receiver (10005), the point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoders of FIG. 11 to 13, partial decoding of FIG. 14 to 19, bitstream and parameter parsing of FIG. 20 to 22 and FIG. 29 to 30, decoding of FIG. 23 to 28, the decoding method of FIG. 32, etc.

[0201] Additionally, the encoding / decoding method / device according to the embodiments may be referred to as the method / device according to the embodiments.

[0202] According to the embodiments, geometry data, geometry information, location information, etc. constituting the point cloud data are interpreted as having the same meaning. Attribute data, attribute information, attribute information, etc. constituting the point cloud data are interpreted as having the same meaning.

[0203] The encoding / decoding method according to the embodiments includes a subgroup LoD sampling direction signaling method for multiple decoders.

[0204] The embodiments include a method for efficiently supporting selective decoding of a portion of data when it is necessary due to receiver performance or transmission speed when transmitting and receiving point cloud data. In this case, a scalable lifting method for scalable coding may be used when performing attribute coding, and additional consideration is required for use in a partial geometry coding environment.

[0205] The embodiments relate to a technique for constructing a data structure composed of a point cloud. The embodiments include a method for informing a receiver of a sampling location by considering a partial geometry coding environment when generating a scalable LoD for scalable attribute coding.

[0206] Referring to FIGS. 3 and 7, point cloud data consists of the location (geometry: e.g., XYZ coordinates) and attributes (attributes: e.g., color, reflectance, intensity, grayscale, opacity, etc.) of each data point. Point Cloud Compression (PCC) performs octree-based compression to efficiently compress distribution characteristics that are non-uniformly distributed in three-dimensional space, and compresses attribute information based on this.

[0207] The encoding / decoding method according to the embodiments assumes full octree depth geometry when generating the LoD used in scalable lifting and alternately applies sampling positions according to the octree depth, thereby increasing compression efficiency during compression by using attributes located near the node center. When supporting scalable transmission, partial geometry is supported; in this case, a mismatch may occur between the encoder and the decoder regarding the sampling positions for generating the scalable lifting LoD, which may result in the problem of inaccurately representing the partial point cloud. By changing the sampling reference position or clearly signaling it according to the method of the embodiments, the problem of mismatch between geometry and attributes during partial decoding can be resolved.

[0208] The embodiments include a subgroup LoD sampling direction signaling method considering single bitstream multiple decoders.

[0209] FIG. 11 illustrates the encoding, transmission, and decoding processes of point cloud data according to embodiments.

[0210] Point cloud data divides location information of data points and feature information such as color, brightness, and reflectivity into geometry and attribute information, compressing and transmitting each separately. At this time, PC data can be configured according to the level of detail, either through an octree structure with layers or according to the Level of Detail (LoD), which enables scalable point cloud data coding and representation. However, depending on the receiver's performance or transmission speed, it is possible to decode or represent only a portion of the point cloud data; currently, there is no method to remove unnecessary data in advance. In other words, when only a portion of the scalable PCC bitstream needs to be transmitted (when decoding only a portion of the layers during scalable decoding), it is not possible to select and send only the necessary parts. Therefore, either 1) the necessary parts are re-encoded after decoding, or 2) the entire data is transmitted and then selectively applied at the receiver. However, in case 1), delay may occur due to the time required for decoding and re-encoding, and in case 2), bandwidth efficiency is reduced because unnecessary data is transmitted, and there is a disadvantage that data quality must be lowered when using a fixed bandwidth.

[0211] FIG. 12 illustrates partial point cloud bitstream transmission and decoding according to embodiments.

[0212] If compressed data is divided and transmitted according to layers, it has the advantage of allowing only the necessary parts of the pre-compressed data to be selectively delivered at the bitstream stage without a separate conversion process. This is efficient in terms of storage space, as only one storage space is required per stream, and enables efficient transmission in terms of bandwidth because only the necessary layers are selectively transmitted before sending (bitstream selector).

[0213] FIG. 13 illustrates scalable decoding of point cloud data according to embodiments.

[0214] From the perspective of the receiver (decoder), if information capable of reconstructing the entire PCC data is transmitted regardless of the receiver's performance, the receiver needs to perform a process of selecting only the data corresponding to the required layer (data selection or sub-sampling) after reconstructing the point cloud data through decoding. In this case, since the transmitted bitstream is already decoded, it may cause delays in receivers aiming for low latency, or it may fail to decode depending on the receiver's performance. However, if the bitstream is divided into slices and transmitted, the receiver can selectively send the bitstream to the decoder based on the density of the point cloud data to be represented, depending on the decoder's performance or the application field. In this case, since selection is made before decoding, decoder efficiency is increased, and there is the advantage of being able to support decoders of various performance levels.

[0215] FIG. 14 shows scalable coding according to embodiments.

[0216] The encoding / decoding method according to the embodiments can scalably encode and decode point cloud data.

[0217] Scalable coding:

[0218] The encoding method according to the embodiments can compress and transmit point cloud data to enable scalable transmission. First, a tool capable of supporting scalability can be used when using position and attribute compression. Additionally, when transmitting the compressed data, slices can be subdivided to enable partial decoding, and a basis for selecting each slice can be signaled prior to decoding. Information for selecting the slice required by the receiver can be transmitted. Scalable transmission may mean supporting the transmission or decoding of only a portion of the bitstream rather than decoding the entire bitstream, and the result may be low-resolution point cloud data.

[0219] When applying scalable transmission to an octree (equivalent to an accupancy tree)-based geometry bitstream, point cloud data can be constructed using only information up to a specific octree layer for the bitstream of each octree layer from the root node to the leaf node. To achieve this, there is no dependency on lower octree layer information for the target octree layer (the layer of the accupancy tree). Dependency conditions can be constraints that apply commonly to geometry and attribute coding.

[0220] In the case of attribute coding, when using scalable lifting, partial decoding based on octree depth may be possible, similar to octree-based geometry coding. Each octree depth can belong to a Level of Depth (LoD), and one or more octree depths can be matched to a Level of Depth. Fig. 14 describes a method for generating a Level of Depth when using scalable lifting, illustrating a method of selecting an attribute of a parent node as one of the attributes of a child node based on a geometry octree structure. As shown in Fig. 14, when the octree depth is 4, an attribute of a node belonging to octree depth 3 can select one of the child nodes belonging to octree depth 4. Similarly, an attribute of a node belonging to octree depth 2 can select one of the child nodes belonging to octree depth 3. In scalable lifting, attributes can be assigned to all octree-level nodes by recursively using a method that selects a representative attribute based on the attributes of child nodes in the direction from leaf nodes to the root node.

[0221] FIG. 14 illustrates a case where the position of the selected node among the child nodes varies depending on the octree level. At the leaf node level (octree depth 4), the first node is used as the attribute of the parent node when sampling, and at octree depth 3, the last node can be used as the attribute of the parent node when sampling. The shaded node (1400) indicates the position selected as the attribute of the parent node.

[0222] The encoding method according to the embodiments can encode the entire attribute data based on the level of detail associated with the octree (accumancy tree). Among the nodes belonging to each depth (or layer) of the octree (accumancy tree), if the index of the depth (or layer) is even, the attribute of the first node in sorted order among the child nodes belonging to a single parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in sorted order among the child nodes belonging to a single parent node can be set (sampled) as the attribute of the parent node.

[0223] The decoding method according to the embodiments can decode the entire attribute data based on the level of detail related to the octree (accumancy tree). Among the nodes belonging to each depth (or layer) of the octree (accumancy tree), if the index of the depth (or layer) is even, the attribute of the first node in sorted order among the child nodes belonging to one parent node can be set (sampled) as the attribute of the parent node. If the index of the depth (or layer) is odd, the attribute of the last node in sorted order among the child nodes belonging to one parent node can be set (sampled) as the attribute of the parent node.

[0224] FIG. 15 shows a group of layers for scalable transmission according to embodiments.

[0225] The encoding method according to the embodiments can generate a layer group for scalable transmission.

[0226] Geometry and attribute coding schemes support layer-based scalability, and by configuring slices according to the layer structure, scalability can be supported prior to decoding, such as in scalable transmission applications. FIG. 15 is a method for configuring layer groups according to an octree structure.

[0227] Layer group: Represents a grouping of layer structural units that occur in G-PCC coding, such as octree layers and LoD layers.

[0228] Sub-group: Represents a set of adjacent nodes based on location information for a single layer group. Alternatively, groups can be formed based on the lowest layer within the layer group (which may refer to the layer closest to the root direction; for example, Layer 6 in the case of Group 3). Groups of adjacent nodes can be formed based on Molton code order, distance-based adjacency, or coding order. Additionally, nodes in a parent-child relationship can be defined to exist within a single sub-group.

[0229] When defining a subgroup, a boundary occurs in the middle of a layer, and regarding whether continuity is maintained at the boundary, continuity with the previous slice can be maintained by indicating whether entropy is used continuously using flags such as the entropy continuity enable flag (sps_entropy_continuation_enabled_flag) and the entropy continuity flag (gsh_entropy_continuation_flag), and by providing the reference slice ID (ref_slice_id).

[0230] When using layer group and subgroup structures, it is necessary to provide a scalable structure to select scalable layers at the transmitter / receiver (encoder / decoder). When considering an octree structure, all octree layers may support scalable transmission, but scalable transmission may be possible only for specific octree layers or lower. If some octree layers are included, the necessity or non-necessity of the slice can be determined at the bitstream stage by indicating which scalable layer the slice belongs to. For example, starting from the root node, scalable transmission may not be supported up to a certain depth, and a single scalable layer may be configured, and the octree layers below may be configured to have a one-to-one match with the scalable layer. Generally, scalability can be supported for the parts corresponding to the leaf nodes, and as shown in FIG. 15, if multiple octree layers are included within a slice, a single scalable layer may be configured for those layers.

[0231] The embodiments may distinguish between scalable transmission and scalable decoding depending on the purpose. In the case of scalable transmission, the embodiments may be used for the purpose of selecting information up to a specific layer without passing through a decoder at the transmitting and receiving ends. In the case of scalable decoding, a specific layer can be selected during the coding process. Scalable transmission supports the selection of necessary information without passing through a decoder in a compressed state (at the bitstream stage), enabling identification at the transmitting or receiving end. Scalable decoding supports encoding / decoding only up to the necessary parts during the encoding / decoding process, thereby enabling its use in cases such as scalable representation.

[0232] The layer configuration for scalable transmission and the layer configuration for scalable decoding may differ. For example, the lower three octree layers including the leaf node may be configured as a single layer from the perspective of scalable transmission, but from the perspective of scalable decoding, scalable decoding may be possible for each of the leaf node layer, leaf node layer-1, and leaf node layer-2, if all layer information is included.

[0233] The encoding / decoding method according to the embodiments can encode and decode point cloud data based on slice-level scalability and octree-level scalability.

[0234] A layer-group may represent a unit for representing a set of one or more octree layers. A layer-group may represent a bundle of octree layers for organizing into slice units. Spatial scalability by actual octree layers (or scalable attribute layers) may be provided for each octree layer, but when configuring scalability at the slice unit prior to bitstream parsing, it may be selected at the layer-group unit level. In FIG. 15, when utilizing scalability at the slice unit, such as in scalable transmission, the provided scalable steps may include three stages: Group 1, Group 2, and Group 3. The scalable steps that may be provided at the decoding stage by the octree structure may include eight stages from the root to the leaf.

[0235] According to the embodiment, when groups 1 to 3 are each composed of slices, the receiving unit or the transmitting unit may 1) select only group 1, 2) select group 1 and group 2, or 3) select group 1, group 2, and group 3.

[0236] 1) When only Group 1 is selected, the maximum octree level becomes 4, and one scalable layer among octree layers 0 to 4 can be selected during the decoding process. The receiver (decoder) can consider the node size obtainable through the maximum octree depth as a leaf node. The node size can then be transmitted to the decoder as signaling information (parameter information).

[0237] 2) When Group 1 and Group 2 are selected, Layer 5 is added, so the maximum octree level becomes 5, and one scalable layer among the octree layers from 0 to 5 can be selected during the decoding process. In this case, the receiver can consider the node size obtainable through the maximum octree depth as a leaf node. The node size can then be transmitted to the decoder as signaling information (parameter information).

[0238] 3) When Group 1, Group 2, and Group 3 are selected, Layers 6 and 7 are added, making the maximum octree level 7, and one scalable layer among the octree layers 0 to 7 can be selected during the decoding process. In this case, the receiver can consider the node size obtainable through the maximum octree depth as a leaf node. The node size can then be transmitted to the decoder as signaling information (parameter information).

[0239] The decoding layers of geometry and attributes may be selected identically or asymmetrically.

[0240] FIG. 16 shows partial geometry and partial attributes according to embodiments.

[0241] The encoding method according to the embodiments can encode attributes based on partial geometry. The decoding method according to the embodiments can decode attributes based on partial geometry.

[0242] When scalable transfer is used, partial decoding of geometry and attributes may be performed, and a difference may occur between the encoded octree depth and the decoded octree depth. In the case of scalable lifting that performs LoD generation based on the total octree depth, a mismatch may occur due to the difference between the encoding and decoding octree depths.

[0243] When performing Scalable Lifting LoD generation for an octree depth of 4, the first child node is selected for the leaf node level, the last child node for the leaf node level+1, and the first child node for the leaf node level+2, and so on, alternating between the first and last as the octree depth decreases. The method of selecting a node close to the center of the parent node has the advantage of increasing coding efficiency. However, assuming the entire octree depth, the following problem may occur in the case of partial geometry. Figure 16 illustrates the case where Scalable Lifting LoD generation is performed in a situation where partial decoding is performed up to an octree depth of 3 in a situation encoded for an octree depth of 4. Scalable Lifting LoD generation alternately performs the operation of selecting the first child node and the last child node according to the octree level, starting from the leaf node level and reaching the root node level. Considering the octree depth of the decoded geometry, the receiver (decoder) selects the first child node at octree level 3 and the last child node at octree level 2 during the scalable lifting LoD generation. However, this results in selecting attributes different from the scalable lifting LoD generation performed based on the octree depth of 4 at the time of encoding. Consequently, a partial point cloud different from the encoder's intent is decoded.

[0244] As shown in FIG. 16, the encoder can sample the attribute of the first child node among the child nodes belonging to a single parent node at depth 4 as the representative attribute of the parent node, and sample the attribute of the last child node among the child nodes belonging to a single parent node at depth 3 as the representative attribute of the parent node, out of a total of four octree depths (octree layers or levels of detail). The decoder can decode point cloud data related to octree depths 1 through 3 for partial decoding. Assuming that depth 3 is the last layer, it can sample the attribute of the first child node among the child nodes belonging to a single parent node at depth 2 as the representative attribute of the parent node. Since the encoder and decoder perform sampling for depth (layer) 3 in different orders, an error occurs in the reconstructed point cloud.

[0245] FIG. 17 shows partial geometry and partial attributes according to embodiments.

[0246] As a method to resolve the problem of sampling order inconsistency between the encoder and decoder when generating scalable lifting LoDs due to partial geometry, the encoder can signal the sampling position to the decoder. The sampling position for each level can be signaled separately. By transmitting information about the entire octree depth together, accurate sampling positions can be used in partial geometry situations. This has the advantage of supporting partial geometry and attributes through signaling without modifying the existing encoder / decoder.

[0247] The decoder can partially decode geometry related to octree depths (levels of layers or levels of detail) 1 through 3 and partially decode attributes related to octree depths (levels of layers or levels of detail) 1 through 3. When the encoder encodes attributes for depths 1 through 4, the point cloud data can be compressed by sampling the attribute of the first node among the child nodes belonging to a single parent node at depth 4 as the representative attribute of the parent node. When restoring the compressed point cloud data, the decoder can partially decode the point cloud data up to depths (levels) 1 through 3, and when decoding starts at depth 3, the attribute of the last child node among the child nodes, rather than the first child node among the child nodes belonging to a single parent node at depth 3, based on the sampling order signaled by the encoder, can sample the attribute of the last child node among the child nodes as the representative attribute of the parent node. The encoder can transmit the sampling positions for all depths 2 to 1 to the decoder as signaling information (parameter information).

[0248] FIG. 18 shows partial geometry and partial attributes according to embodiments.

[0249] The encoding method according to the embodiments can signal the sampling position for Octree Depth 1. Since partial geometry decoding is a method of discarding consecutive Octree levels from the leaf node level, if sampling information for Octree Depth 1, which is always decoded, is transmitted, subsequent sampling positions can be applied alternately according to Octree Depth. For example, if the sampling position for Octree Depth 1 is the last child node, the first child node and the last child node can be inferred for the subsequent Octree Depth. There is an advantage in that partial geometry and attributes can be supported through signaling without modifying the existing encoder / decoder. If the sampling position for Depth 1 is the last node, the encoder can transmit information indicating that the sampling position for Depth 1 is the last node to the decoder. The decoder can sequentially sample the first node at depth 2 and the last node at depth 3 as attributes of the parent node for depths 2 through 3.

[0250] FIG. 19 shows partial geometry and partial attributes according to embodiments.

[0251] The encoding method according to the embodiments can fix the sampling position for octree depth 1. Unlike the method of fixing the sampling position for leaf node levels in existing methods, fixing it for octree depth 1 has the advantage that the scalable LoD generation sampling position does not change even when partial geometry coding occurs.

[0252] As shown in FIG. 19, among the child nodes belonging to a single parent node at depth (layer or level) 1, the sampling position can be fixed to the first node. The decoder can sample the first node at depth 1 according to a fixed order, sequentially infer the sampling position at depth 2 to sample the last node, and infer the sampling position at depth 2 to sample the first node.

[0253] Furthermore, the encoding method according to the embodiments may use a fixed sampling position for all depths. The encoding method according to the embodiments may signal to use points in a specific order.

[0254] octree depth encoder (full octree depth 4) decoder (partial octree depth 3) 1 First (fixed) First (fixed) 2 First (fixed) First (fixed) 3 First (fixed) First (fixed) 4 First (fixed) -

[0255] octree depth encoder (full octree depth 4) decoder (partial octree depth 3) 1 Last (fixed) Last (fixed) 2 Last (fixed) Last (fixed) 3 Last (fixed) Last (fixed) 4 Last (fixed) -

[0256] As described above, the sampling position can be fixed, such as sampling the first child node at each depth, or the sampling position can be fixed, such as sampling the last mode at each depth. The encoder can transmit signaling information indicating the use of a fixed position (order) to the decoder.

[0257] In applications such as layer-group slicing or scalable coding, where skip layer-groups or skip layers are used, the sampling direction used in the encoder can be inferred to maintain coding efficiency. Lost layer information can be inferred through the layer information, bounding box information, or root node information of the actual decoded / encoded sub-data, along with the total layer information, bounding box information, or root node information of the encoder's input point cloud data. Layers can be estimated from the encoder. The sampling direction used can be inferred as (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded).

[0258] if (layer_group_enabled_flag) {

[0259] octreeNodeSizeLog2 = lodIndex;

[0260] direction = (octreeNodeSizeLog2 + rootNodeSizeLog2 - rootNodeSizeLog2_coded) & 1;

[0261] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0262] }

[0263] LoD Index (lodIndex): This is an index representing the current lod. The index value can sequentially increase or decrease from the upper lod to the lower lod.

[0264] Octree Node Size (octreeNodeSizeLog2): A log2 scale value for the node size of the current lod.

[0265] Root Node Size (rootNodeSizeLog2): A log2 scale value for the size of the node or bounding box covering the entire input point cloud data of the encoder.

[0266] Coded Root Node Size (rootNodeSizeLog2_coded): This is the log2 scale value for the size of the nodes or bounding boxes covering the actual coded point cloud data.

[0267] FIG. 20 shows a bitstream including point cloud data and parameters according to embodiments.

[0268] The encoding method according to the embodiments may include information related to the separated slices according to the embodiments in the bitstream and transmit it. A parameter set and / or SEI message within the bitstream may include information related to the separated slices. A sequence parameter set (SPS), geometry parameter set (GPS), attribute parameter set (APS) and / or geometry slice header (GSH) and / or attribute slice header (ASH) within the bitstream may include information related to the separated slices. Depending on the application or system, information related to the separated slices may be defined at a corresponding location within the bitstream or at a separate location to use different scopes of application, application methods, etc. Depending on the location where the signal is transmitted within the bitstream, it may have different meanings; if defined in the SPS, it may be applied equally to the entire sequence; if defined in the GPS, it may indicate that it is used for location restoration; if defined in the APS, it may indicate that it is applied for attribute restoration; if defined in the TPS, it may indicate that the corresponding signaling is applied only to points within the tile; and if transmitted at the slice unit, it may indicate that the signal is applied only to the corresponding slice. Furthermore, depending on the application or system, it can be defined in a corresponding location or a separate location to use different scopes and methods of application. Additionally, if the syntax element defined below can be applied not only to the current point cloud data stream but also to multiple point cloud data streams, information related to the separated slices can be transmitted through a higher-level parameter set.

[0269] Each abbreviation means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning: 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.

[0270] A slice contains point cloud data and represents a unit of encoding and decoding. A slice may be referred to as a data unit. A slice represents geometry and attributes for part or all of a coded point cloud frame. A slice may be referred to as a brick. A data unit represents a sequence of bytes that conveys a syntax structure of a fixed length.

[0271] The embodiments define the information to be described below independently of the coding technique, and furthermore, the information below may be defined in conjunction with the coding method and may be defined in a tile parameter set to support regionally different scalability. Additionally, if the syntax elements defined below can be applied to multiple point cloud data streams as well as the current point cloud data stream, they may be transmitted through a higher-level parameter set, etc.

[0272] The encoding method according to the embodiments can define a Network Abstract Layer (NAL) unit, define relevant information for selecting a layer such as a layer ID (layer_id) within the bitstream, and transmit it to a decoder. The decoding method according to the embodiments can select and decode the bitstream at the system level.

[0273] Parameters according to the embodiments (which may be referred to in various ways, such as metadata, signaling information, etc.) may be generated during the process of the transmitter according to the embodiments described below, and may be transmitted to the receiver according to the embodiments and used in the reconstruction process.

[0274] For example, parameters according to the embodiments may be generated in the metadata processing unit (or metadata generator) of the transmitting device according to the embodiments described below, and may be obtained in the metadata parser of the receiving device according to the embodiments.

[0275] FIG. 21 shows scalable lifting LoD generation information according to embodiments.

[0276] FIG. 22 shows a set of attribute parameters according to embodiments.

[0277] FIGS. 21 and FIGS. 22 illustrate the syntax of parameter information contained within the bitstream of FIG. 20. The information in FIGS. 21 and FIGS. 22 can be generated by an encoding device and decoded by a decoding device.

[0278] Top_down_LoD_generation_flag: If this value is 1, it indicates that when sampling attributes from child nodes for scalable lifting LoD generation, sampling is performed by alternating the sampling location in a top-down direction relative to the initial sampling depth (e.g., Octree depth (depth of the accusation tree) 1). If this value is 0, it indicates that when sampling attributes from child nodes for scalable lifting LoD generation, the sampling location is specified by alternating the sampling location in a bottom-up direction relative to the leaf node level. The criterion for determining the sampling position can be changed from bottom to top. Scalable lifting LoD generation can be performed bottom-up.

[0279] first_depth_sampling_direction_present_flag: If this value is 1, it indicates that the sampling location is explicitly provided when sampling attributes from child nodes for the reference octree depth (e.g., octree depth 1). If this value is 0, it indicates that the sampling location is implicitly provided when sampling attributes from child nodes. For example, either the first child node or the last child node may be the sampling location.

[0280] First_depth_sampling_direction_forward_flag: If this value is 1, it indicates that sampling is performed in the forward direction when sampling attributes from child nodes. That is, it indicates that the first child node is sampled for the first depth of sampling. If this value is 0, it indicates that sampling is performed in the reverse direction when sampling attributes from child nodes. That is, it indicates that the last child node is sampled for the first depth of sampling.

[0281] Total number of octree depths (num_full_octree_depth): Can represent the octree depths considered in the encoding. When top_down_LoD_generation_flag is 0, the sampling direction of scalable lifting LoD generation is determined in the bottom-up direction, so the decoder can use num_full_octree_depth, which represents the octree depth considered in the encoding rather than the decoded geometry depth, as a criterion for determining the direction at each octree depth.

[0282] Child Node Sampling Forward Direction Flag (child_node_sampling_forward_direction_flag): If this value is 1, it indicates that the sampling location is explicitly passed when sampling attributes from a child node for each octree depth. If it is 0, it indicates that the sampling location is implicitly passed when sampling attributes from a child node. For example, the sampling location can be either the first child node or the last child node.

[0283] Child_node_sampling_position: Can directly signal the sampling position at each octree level. If 0, the sampling position is the first node, and if 1, it indicates the last node. If the value is 2 through 7, it indicates using the 1st through 6th nodes (or the nearest nodes). For example, if the value is 2 through 7, it indicates a node located between the first node and the last node.

[0284] Lifting scalability enable flag (lifting_scalability_enabled_flag): If this value is 1, it indicates that the attribute decoding process allows pruned octree decoding results for the input geometry points. If lifting_scalability_enabled_flag is 0, it indicates that the attribute decoding process requires complete octree decoding results for the input geometry points. If this flag does not exist, its value is inferred to be 0. If trisoup_enabled_flag is 0, the value of lifting_scalability_enabled_flag is 0. If geom_tree_coded_axis_list_present_flag is 1, lifting_scalability_enabled_flag is 0.

[0285] If the geometry scaling enable flag (geom_scaling_enabled_flag) is 1, lifting_scalability_enabled_flag is 0 to ensure partial decoding of the geometry point cloud.

[0286] Lifting nearest neighbor range maximum (lifting_max_nn_range_minus1): If this value is increased by 1, it represents the maximum nearest neighbor range value used to limit the distance of points registered as neighbors. The value of lifting_max_nn_range is the number of octree nodes surrounding the point.

[0287] Fixed sampling forward flag (fixed_sampling_direction_forward_flag): If this value is 1, it indicates that a sampling method is used to select points in a fixed order for the points included in the sampling unit.

[0288] Sampling_direction_type: Can indicate the method or order of points used for sampling. 0 indicates that the first point of Morton code-ordered points is used, 1 indicates that the last point of Morton code-ordered points is used, 2 indicates that the second point is used, and 3 indicates that the third point is used.

[0289] Inferred sampling direction enable flag (inferred_sampling_direction_enabled_flag): If this value is 1, it indicates that a method for inferring the sampling direction is used.

[0290] Root Node Size (rootNodeSizeLog2): Represents a log2 scale value for the size of the node or bounding box that covers the entire input point cloud data of the encoder.

[0291] Coded root node size (rootNodeSizeLog2_coded): A log2 scaled value of the size of the node or bounding box covering the actual coded point cloud data. The encoder can signal the coded root node size. Instead of signaling it directly, the root node size can be inferred internally within the encoder or decoder based on the decoded geometry point cloud data.

[0292] The encoder and / or decoder can infer the number of missing layers through rootNodeSizeLog2 - rootNodeSizeLog2_coded and, if necessary, directly signal the number of missing layers as parameter information.

[0293] FIG. 23 shows an encoder (transmitting device) according to embodiments.

[0294] FIG. 23 corresponds to the transmitting device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoders of FIG. 11 to FIG. 13, the partial encoding of FIG. 14 to FIG. 19, the generation of bitstreams and parameters of FIG. 20 to FIG. 22 and FIG. 29 to FIG. 30, the encoding of FIG. 23 to FIG. 28, the encoding method of FIG. 31, etc.

[0295] The components of the transmitting and receiving unit of the present invention are described below. Each component may correspond to a processor, software, or hardware, etc. Additionally, the following components may be combined with the PCC transmitting and receiving unit structure and / or signaling information. Each of the encoder (encoding device) and decoder (decoding device) may be composed of memory and a processor.

[0296] 1) Encoder (Encoding device):

[0297] The encoder takes point cloud data as input. The encoder can encode location information (geometry data (e.g., XYZ coordinates, phi-theta coordinates, etc.)) and attribute information (attribute data (e.g., color, reflectance, intensity, grayscale, opacity, medium, material, glossiness, etc.)) respectively. The compressed data can be divided into units for transmission. Through a sub-bitstream generator, the necessary information at the bitstream level can be divided into units suitable for selection and packed according to layering structure information.

[0298] FIG. 24 shows a decoder (receiving device) according to embodiments.

[0299] FIG. 24 corresponds to the receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the decoders of FIG. 11 to 13, partial decoding of FIG. 14 to 19, bitstream and parameter parsing of FIG. 20 to 22 and FIG. 29 to 30, decoding of FIG. 23 to 28, the decoding method of FIG. 32, etc.

[0300] The decoder receives a bitstream as input from the encoder. The decoder can separate and process the bitstream for position information and the bitstream for attribute information. In this process, a sub-bitstream classifier can forward the data to the appropriate decoder based on the information in the bitstream header. Alternatively, the layer required by the receiver can be selected during this process. Depending on the characteristics of the data, the classified bitstream can be converted into geometry data and attribute data by the geometry decoder and attribute decoder, respectively, and then converted into a format for final output by the renderer.

[0301] FIG. 25 shows a scalable lifting according to the embodiments.

[0302] A decoding device according to the embodiments can decode point cloud data based on scalable lifting as shown in FIG. 25. When slices are configured at the layer level for scalable transmission, scalable lifting according to metadata can be performed as shown in FIG. 25. When scalable lifting is used, signaling information of scalable lifting LoD generation information (scalable_lifting_lod_generation_info()) transmitted via an SEI message or APS according to the embodiments can be used. If the top-down LoD generation flag is 1, the top level sampling direction can be explicitly signaled via first_depth_sampling_direction_forward_flag (when first_depth_sampling_direction_present_flag = 1), or the top level sampling direction defined in the standard document can be used (when first_depth_sampling_direction_present_flag = 0). If top-down LoD generation is not used, the sampling location can be estimated in a bottom-up direction using the octree depth information used during encoding (num_full_octree_depth), or the location can be determined using the sampling location of each octree depth given through child_node_sampling_forward_dicrection_flag or child_node_sampling_position.

[0303] The decoding method according to the embodiments estimates the sampling position in the bottom-up direction when Scalable Lifting Enable is true and Top-Down LoD Generation is false. When Top-Down LoD Generation is true and Sampling Direction Existence is false, the sampling position is estimated in the top-down direction. When Sampling Direction Existence is true, the sampling position is estimated in the top-down direction.

[0304] FIG. 26 shows subgroup LoD sampling considering a missing layer according to embodiments.

[0305] The method according to the embodiments can change the LoD sampling direction for each layer in attribute hierarchy-group slicing. In each layer, child nodes are arranged in Morton code order, and depending on the octree hierarchy, the attribute of the first or last node can be selected (sampled) as the attribute of the parent node.

[0306] When the total number of octree depths is passed to the decoder, the decoder can sample points (nodes) in the octree in the same direction as the encoder's LoD sampling direction. However, when a subset of the coded bitstream is decoded, the sampling direction of the bottom layer is fixed forward, and since there is a possibility that there may be different sampling directions (first or last node) depending on the encoding layer, the decoder's sampling direction may not be the same as the encoder's.

[0307] To solve this problem, the method according to the embodiments can compensate for the number of missing layers in subgroup LoD generation as follows:

[0308] Number of missing layers = Maximum depth of encoded LoD - Maximum depth of the current subgroup LoD

[0309] The method according to the embodiments can calculate the number of missing layers through the difference between the maximum depth value of the LoD encoded in the encoder and the maximum depth value of the current subgroup LoD.

[0310] In addition, the number of missing layers can be derived by two parameters.

[0311] Root Node Size (rootNodeSizeLog2) is a parameter representing the maximum depth of the encoded LoD.

[0312] The encoded root node size (rootNodeSizeLog2_coded) is a derived value representing the maximum depth of the current subgroup LoD.

[0313] These parameters can be used to compensate for the octree node size (octreeNodeSizeLog2) in determining the LoD sampling direction.

[0314] if (layer_group_enabled_flag) {

[0315] int32_t octreeNodeSizeLog2 = lodIndex;

[0316] bool direction = (octreeNodeSizeLog2 + (rootNodeSizeLog2 - rootNodeSizeLog2_coded)) & 1;

[0317] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0318] }

[0319] The sampling direction can be calculated based on the octree node size (octreeNodeSizeLog2), root node size (rootNodeSizeLog2), and coded node size (rootNodeSizeLog2_coded).

[0320] OctreeNodeSize(octreeNodeSizeLog2) can be the maximum depth of the LoD.

[0321] Based on this, the octreeNodeSizeLog2 parameter can be used as follows:

[0322] if (layer_group_enabled_flag) {

[0323] int32_t octreeNodeSizeLog2 = lodIndex;

[0324] bool direction = octreeNodeSizeLog2 & 1; / octreeNodeSizeLog2 = maxDepth - endDepth[layerId]

[0325] / maxDepth += sps.num_layers_minus1[layerGroupId]+1;

[0326] subsampleByOctree(pointCloud, packedVoxel, input, octreeNodeSizeLog2, retained, indexes, direction);

[0327] }

[0328] The maximum number of layers (max.layer) can be calculated using a method such as maxDepth += sps.num_layers_minus1[layerGroupId]+1.

[0329] In the two aforementioned methods of generating subgroup LoDs, the method for deriving the LoD sampling direction is the same. However, the method for calculating the maximum depth value of the encoded LoD may differ. One is implemented to derive the value using a defined parameter for the number of layers in each layer group, while the other signals this value as explicit parameter information.

[0330] FIG. 27 shows FGS-related encoder and decoder use cases according to embodiments.

[0331] FIG. 28 shows FGS-related encoder and decoder use cases according to embodiments.

[0332] In progressive decoding and partial decoding, the target application can be considered as a single decoder. Fine granularity slicing (FGS) according to the embodiments can be used to support multiple decoders with different functions due to memory, clock speed, display device, etc.

[0333] For example, if we describe decoders A, B, and C with different capabilities in terms of resolution, one way to support them is to generate different sets of bitstreams according to the target device requirements. As shown in FIG. 27(b), an efficient method is to select a fine-grained slice to generate bitstreams with fine-grained slicing that can support three different decoders.

[0334] For example, the encoder can select a group of sub-layers of the bitstream at the request of the decoder. If fine-grained slicing is not used, other bitstreams must be generated by different encoders and optionally stored or transmitted. As shown in FIG. 28(b), a single bitstream with fine-grained slicing can support multiple decoders without additional encoders and storage.

[0335] To support these use cases, granular slicing encoders can generate different sets of parameters for each target device. This is used to verify decoder capabilities and set decoder parameters. In this case, the encoder or transcoder can set different parameter values ​​depending on the target device of a bitstream subset when syntax definition is not clear.

[0336] If necessary, multiple parameter sets such as SPS, GPS, and APS can be generated for different target devices, and each parameter set can provide criteria for selecting different levels of bitstream subsets that are supported in the bitstream.

[0337] For example, the number of layer groups (num_layer_group_minus1) may represent the maximum depth value that the target device must support. Since the number of layer groups (num_layer_group_minus1) has different values ​​for decoders A, B, and C, the bitstreams that can be processed by each decoder can be selectively used.

[0338] For example, it is possible to determine whether it is a final displayable subset based on the number of points within a subgroup or layer group.

[0339] Referring to FIG. 27(a), in the case of a multi-encoder and multi-decoder without FGS, the multi-encoder can downsample and encode the entire point cloud data or encode the entire point cloud data and transmit bitstreams of different sizes, and the multi-decoder can decode the point cloud data of low resolution to high resolution, respectively.

[0340] Referring to FIG. 27(b), there is an FGS-based single encoder and a multi-decoder. When the single encoder encodes and transmits point cloud data into multiple FGSs through layer group slicing, the multi-decoder can decode low-resolution to high-resolution point cloud data based on FGS.

[0341] Referring to FIG. 28(a), there are multi-encoders and multi-decoders without FGS, and the multi-encoder can downsample the entire point cloud data to encode partial point cloud data or encode the entire point cloud data. A slice selector can select a slice of low resolution to high resolution, transcode the selected slice, and transmit it to a multi-decoder. The multi-decoder can decode the point cloud data within the received slice of low resolution to high resolution.

[0342] Referring to FIG. 28(b), there is a single encoder and a multi-decoder with an FGS. The single encoder encodes the entire point cloud data based on layer group slices, transcodes low-resolution to high-resolution FSGs through a slice selector, and transmits them to the decoder. The multi-decoder can decode the point cloud data within the low-resolution to high-resolution FSGs.

[0343] Two possible examples of the maximum depth of the encoded LoD are described below, taking into account the use cases of FIGS. 27 and 28.

[0344] FIG. 29 shows a set of sequence parameters according to the embodiments.

[0345] The encoding method according to the embodiments can generate a sequence parameter set of FIG. 29 and include it in a bitstream. The decoding method according to the embodiments can decode point cloud data based on information within the sequence parameter set of FIG. 29.

[0346] As shown in FIG. 29, the method according to the embodiments can generate and transmit FGS-related parameter information through explicit signaling.

[0347] In an explicit manner, the root subgroup bounding box size (root_subgroup_bbox_size), which represents the maximum depth of the LoD in the encoder, can be passed to the SPS.

[0348] The root node size (root_node_size_log2) represents the log 2 value of the cubic root node size of the encoded octree.

[0349] Root node size (root_node_size_log2 can refer to the number of encoded tree layers from the root to the leaf layer).

[0350] In granular slicing, the number of missing layers in a subgroup can be calculated based on the difference between root_node_size_log2 and the current maximum depth of the subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in subgroup LoD generation. The number of missing layers in a subgroup is used to compensate for the approximate location of nodes in intermediate layers.

[0351] FIG. 30 shows a set of sequence parameters according to the embodiments.

[0352] The method according to the embodiments may further add descriptive information within the bitstream, for example, within a sequence parameter set.

[0353] For example, constraints can be added to the maximum depth of the encoded LoD and related signaling information. Considering that EE SW uses num_layers_minus1 to derive the maximum depth of the encoded LoD, the following semantics can be added.

[0354] Number of layer groups (num_layer_groups_minus1): If this value is increased by 1, it represents the number of layer groups representing consecutive tree layer groups that are part of the geometry coding tree structure. num_layer_groups_minus1 is in the range of 0 to the number of coding tree layers. num_layer_groups_minus1 is the number of layer groups of the encoder.

[0355] Number of layers (num_layers_minus1): Adding 1 to this value represents the number of coding layers included in the i-th layer group. The maximum number of coded layers in the current frame is calculated by accumulating num_layers_minus1[i] + 1 for all i in the range from 0 to num_layer_groups_minus1.

[0356] The maximum number of layers derived from num_layers_minus1 is the number of encoded tree layers from the root to the leaf layer.

[0357] In fine granularity slicing, the number of missing layers in a subgroup can be the difference between the maximum number of layers and the maximum depth of the current subgroup. The number of missing layers in a subgroup is used to derive the sampling direction in subgroup LoD generation. The number of missing layers in a subgroup is used to compensate for the approximate location of nodes in intermediate layers.

[0358] The number of layer groups (num_layer_groups_minus1) may change depending on the number of receivers when the transcoder supports various receivers.

[0359] FIG. 31 shows a encoding method according to embodiments.

[0360] The encoding method according to the embodiments may include the step of encoding point cloud data (S3100), and / or the step of transmitting a bitstream containing point cloud data (S3110).

[0361] Referring to FIG. 23 and FIG. 15 together, the step of encoding point cloud data (S3100) includes the step of encoding geometry data of point cloud data and the step of encoding attribute data of point cloud data, wherein geometry data is encoded based on a tree including a root layer to a leaf layer, attribute data is encoded based on a tree including a root layer to a leaf layer, the tree for geometry data includes a layer group including at least one layer, said layer group includes at least one subgroup, the tree for attribute data includes a layer group including at least one layer, said layer group includes at least one subgroup, and the encoding step may further include generating scalable lifting level of detail (LoD) information.

[0362] Referring together with FIG. 17, regarding the signaling of the sampling location of a node within a tree, the step of encoding point cloud data includes generating information indicating the sampling location of nodes within a specific depth of the tree of the attribute data, and the sampling location may be either the first node or the last node.

[0363] Referring together with FIG. 18, regarding signaling only the first position of node sampling within the tree, the step of encoding point cloud data includes generating information indicating the sampling positions of nodes within the first depth of the tree of the attribute data, and the sampling position may be either the first node or the last node.

[0364] Referring to FIG. 19, regarding the fixing of the initial position of sampling within the tree, the step of encoding point cloud data includes sampling a node based on the sampling position within the first depth of the tree of the attribute data, and the sampling position may be either the first node or the last node.

[0365] The device according to the embodiments may further include a computer-readable storage medium that stores a bitstream generated by the encoding method according to the embodiments.

[0366] A method according to embodiments may include the step of obtaining a bitstream for point cloud data, said bitstream is generated based on the step of encoding geometry data of said point cloud data and the step of encoding attribute data of said point cloud data; and the step of transmitting data including said bitstream.

[0367] FIG. 32 illustrates a decoding method according to embodiments.

[0368] The decoding method according to the embodiments may include the step of receiving a bitstream containing point cloud data (S3200), and / or the step of decoding the point cloud data (S3210).

[0369] Referring together with FIG. 24, the step of decoding point cloud data (S3210) may include the step of decoding geometry data of point cloud data and the step of decoding attribute data of point cloud data.

[0370] Referring to FIG. 22 and FIG. 21 together, with respect to lifting_scalability_enabled_flag, scalable_lifting_lod_generation_info(), the bitstream includes a flag for scalable lifting, and based on the value of the flag for scalable lifting, the bitstream further includes scalable lifting level of detail (LoD) information, and the scalable lifting level of detail information may include at least one of a generation flag for the level of detail (LoD) for attribute data of point cloud data, a sampling direction presence flag, a sampling definition flag, or octree total depth count information.

[0371] Referring together with FIG. 21, with respect to top_down_LoD_generation_flag, first_depth_sampling_direction_present_flag, first_depth_sampling_direction_forward_flag, and num_full_octree_depth, the first value of the generation flag for level of detail for attribute data indicates that the sampling location for the node of the tree is changed in a top-down direction from a specific depth of the tree regarding the point cloud data, the second value of the generation flag for level of detail for attribute data indicates that the sampling location for the node of the tree is changed in a bottom-up direction from a leaf node of the tree regarding the point cloud data, the sampling direction present flag indicates whether the sampling location at a specific depth of the tree regarding the point cloud data is explicitly signaled, the sampling determination flag indicates whether the first node is sampled at a specific depth regarding the point cloud data, and the octree total depth information may indicate the total depth of the octree regarding the geometry data of the point cloud data.

[0372] Referring together with FIG. 21, regarding fixed_sampling_direction_forward_flag, sampling_direction_type, inferred_sampling_direction_enabled_flag, rootNodeSizeLog2, and rootNodeSizeLog2_coded, the scalable lifting level of detail information includes at least one of a fixed sampling direction flag, a sampling direction type, an inferred sampling direction flag, the root node size of the input point cloud data, or the root node size of the encoded point cloud data. The fixed sampling forward flag indicates whether the location for sampling a node in the tree regarding the point cloud data is fixed, the sampling direction type indicates whether the location for sampling a node in the tree regarding the point cloud data is the first node or the last node, and the inferred sampling direction flag indicates whether the location for sampling a node in the tree regarding the point cloud data is inferred.

[0373] Referring to FIG. 29, with respect to root_node_size_log2, the bitstream includes information regarding the size of the root node of the encoded octree with respect to the point cloud data, and the information regarding the size of the root node of the encoded octree with respect to the point cloud data may represent the number of layers from the root layer to the leaf layer of the encoded octree.

[0374] Referring to FIG. 30 together, with respect to num_layer_groups_minus1 and num_layers_minus1, the bitstream includes at least one of information indicating the number of layer groups or information indicating the number of layers, and the information indicating the number of layer groups represents a value related to the number of layer groups including a continuous sublayer of a tree regarding the geometry data of the point cloud data, and the information indicating the number of layers may represent a value related to the number of layers included in the layer groups.

[0375] The decoding method is performed by a decoding device, and the decoding device, referring to FIG. 1, includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: receive a bitstream containing point cloud data; and decode the point cloud data.

[0376] The method and apparatus according to the embodiments have the following technical effects.

[0377] The LoD generation used in scalable lifting assumes full octree depth geometry and applies sampling locations alternately according to octree depth, such as the first or last child node, thereby using attributes located near the node center to achieve compression efficiency. When scalable transmission is supported, partial geometry is supported; in this case, a mismatch may occur between the encoder and decoder regarding the sampling locations of scalable lifting LoD generation (due to at least one missing layer), which may result in an inaccurate representation of the partial point cloud. In the case of partial decoding, the mismatch between geometry and attributes can be resolved by changing or clearly signaling the sampling reference location according to the method of the embodiments.

[0378] The method / device for transmitting and receiving point cloud data according to the embodiments can scalably encode and / or decode point cloud data by considering partial geometry, and has the effect of efficiently compressing and restoring point cloud data based on operation and / or related signaling information according to the embodiments.

[0379] Various elements of the embodiments may be performed by hardware, software, firmware, or a combination thereof. Various elements of the embodiments may be performed on a single chip, such as a hardware circuit. Depending on the embodiments, the embodiments may optionally be performed on individual chips. Depending on the embodiments, at least one of the elements of the embodiments may be performed within one or more processors that include instructions for performing operations according to the embodiments.

[0380] Operations according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting and receiving device may include a transmitting and receiving unit for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart and / or data) for a process according to the embodiments, and a processor for controlling the operations of the transmitting and receiving devices.

[0381] The processor may be referred to as a controller, etc., and may correspond, for example, to hardware, software, and / or a combination thereof. The operation according to the embodiments described above may be performed by the processor. Additionally, the processor may be implemented as an encoder / decoder, etc., for the operation of the embodiments described above.

[0382] The embodiments have been described in terms of methods and / or devices, and the description of the methods and the description of the devices may be applied complementarily.

[0383] Although the drawings have been described separately for the convenience of explanation, it is also possible to design a new embodiment by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by a person skilled in the art, falls within the scope of the claims of the embodiments. The apparatus and method according to the embodiments are not limited to the configuration and method of the embodiments described above; rather, the embodiments may be configured by selectively combining all or part of each embodiment to allow for various modifications. Although preferred embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above. It is not only possible for a person skilled in the art to make various modifications without departing from the essence of the embodiments claimed in the claims, but such modifications should not be understood individually from the technical concept or perspective of the embodiments.

[0384] Various components of the device of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various components of the embodiments may be implemented as a single chip, for example, a single hardware circuit. Depending on the embodiments, the components according to the embodiments may each be implemented as separate chips. Depending on the embodiments, at least one 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 include instructions for performing or executing any one or more of the operations / methods according to the embodiments. Executable instructions for performing the methods / operations of the device according to the embodiments may be stored in non-transient CRMs or other computer program products configured to be executed by one or more processors, or may be stored in transient CRMs or other computer program products configured to be executed by one or more processors. Additionally, memory according to the embodiments may be used as a concept that includes not only volatile memory (e.g., RAM, etc.) but also non-volatile memory, flash memory, PROM, etc. In addition, it may also include implementation in the form of carrier waves, such as transmission over the Internet. Furthermore, processor-readable recording media are distributed across networked computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0385] In this document, “ / ” and “,” are interpreted as “and / or.” For example, “A / B” is interpreted as “A and / or B,” and “A, B” is interpreted as “A and / or B.” Additionally, “A / B / C” means “at least one of A, B and / or C.” Also, “A, B, C” means “at least one of A, B and / or C.” Additionally, in this document, “or” is interpreted as “and / or.” For example, “A or B” may mean 1) “A” alone, 2) “B” alone, or 3) “A and B.” In other words, “or” in this document may mean “additionally or alternatively.”

[0386] Terms such as "first," "second," etc., may be used to describe various components of the embodiments. However, the interpretation of the various components according to the embodiments should not be limited by these terms. These terms are merely used to distinguish one component from another. For example, the first user input signal may be referred to as the second user input signal. Similarly, the second user input signal may be referred to as the first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although the first user input signal and the second user input signal are both user input signals, they do not imply the same user input signals unless clearly indicated in the context.

[0387] The terms used to describe the embodiments are intended 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 explicitly indicated in the context. Expressions of and / or are used to mean including all possible combinations between the terms. Expressions of include describe the presence of features, numbers, steps, elements, and / or components and do not imply the exclusion of additional features, numbers, steps, elements, and / or components. Conditional expressions such as "if" or "when" used to describe the embodiments are not limited to being optional. It is intended to be interpreted as "when a specific condition is satisfied," "when a related action is performed in response to a specific condition," or "when a related definition is interpreted."

[0388] Additionally, operations according to the embodiments described herein may be performed by a transmitting and receiving device including memory and / or a processor, depending on the embodiments. The memory may store programs for processing / controlling operations according to the embodiments, and the processor may control various operations described in this document. The processor may be referred to as a controller, etc. Operations in the embodiments may be performed by firmware, software, and / or a combination thereof, and the firmware, software, and / or a combination thereof may be stored in the processor or in memory.

[0389] Meanwhile, the operation according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting and receiving device may include a transmitting and receiving unit for transmitting and receiving media data, a memory for storing instructions (program code, algorithm, flowchart and / or data) for a process according to the embodiments, and a processor for controlling the operations of the transmitting and receiving devices.

[0390] The processor may be referred to as a controller, etc., and may correspond, for example, to hardware, software, and / or a combination thereof. The operation according to the embodiments described above may be performed by the processor. Additionally, the processor may be implemented as an encoder / decoder, etc., for the operation of the embodiments described above.

[0391] 발명의 실시를 위한 형태

[0392] As described above, the relevant details have been explained in the best mode for carrying out the embodiments. Industrial applicability

[0393] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0394] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0395] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

Claim 1 A decoding method comprising: receiving a bitstream containing point cloud data; and decoding the point cloud data. Claim 2 A decoding method according to claim 1, wherein the step of decoding the point cloud data includes the step of decoding the geometry data of the point cloud data and the step of decoding the attribute data of the point cloud data. Claim 3 A decoding method according to claim 1, wherein the bitstream includes a flag for scalable lifting, and the bitstream further includes scalable lifting level of detail (LoD) information based on the value of the flag for scalable lifting, and the scalable lifting level of detail information includes at least one of a generation flag for the level of detail (LoD) for attribute data of the point cloud data, a sampling direction existence flag, a sampling determination flag, or octree total depth count information. Claim 4 In paragraph 3, the first value of the generation flag for the level of detail of the attribute data indicates that the sampling position for a node of the tree is changed in a top-down direction from a specific depth of the tree regarding the point cloud data, the second value of the generation flag for the level of detail of the attribute data indicates that the sampling position for a node of the tree is changed in a bottom-up direction from a leaf node of the tree regarding the point cloud data, the sampling direction presence flag indicates whether the sampling position at a specific depth of the tree regarding the point cloud data is explicitly signaled, the sampling determination flag indicates whether the first node is sampled at a specific depth regarding the point cloud data, and the octree total depth count information indicates the total depth count of the octree regarding the geometry data of the point cloud data, a decoding method. Claim 5 In paragraph 3, the scalable lifting level of detail information comprises at least one of a fixed sampling forward flag, a sampling direction type, a sampling direction inference flag, the root node size of the input point cloud data, or the root node size of the encoded point cloud data, wherein the fixed sampling forward flag indicates whether the position for sampling a node in the tree regarding the point cloud data is fixed, the sampling direction type indicates whether the position for sampling a node in the tree regarding the point cloud data is the first node or the last node, and the sampling direction inference flag indicates whether the position for sampling a node in the tree regarding the point cloud data is inferred, a decoding method. Claim 6 A decoding method according to claim 1, wherein the bitstream includes information related to the size of the root node of the encoded octree regarding the point cloud data, and the information related to the size of the root node of the encoded octree regarding the point cloud data indicates the number of layers from the root layer to the leaf layer of the encoded octree. Claim 7 A decoding method according to claim 1, wherein the bitstream includes at least one of information indicating the number of layer groups or information indicating the number of layers, wherein the information indicating the number of layer groups indicates a value related to the number of layer groups including a continuous sublayer of a tree regarding the geometry data of the point cloud data, and the information indicating the number of layers indicates a value related to the number of layers included in the layer groups. Claim 8 A decoding device comprising: a memory; and at least one processor connected to the memory; wherein the at least one processor is configured to: receive a bitstream containing point cloud data; and decode the point cloud data. Claim 9 A encoding method comprising: a step of encoding point cloud data; and a step of transmitting a bitstream containing the point cloud data. Claim 10 In claim 9, the step of encoding the point cloud data includes the step of encoding the geometry data of the point cloud data and the step of encoding the attribute data of the point cloud data, wherein the geometry data is encoded based on a tree including a root layer to a leaf layer, the attribute data is encoded based on a tree including a root layer to a leaf layer, the tree for the geometry data includes a layer group including at least one layer, the layer group includes at least one subgroup, the tree for the attribute data includes a layer group including at least one layer, the layer group includes at least one subgroup, and the encoding step further includes generating scalable lifting level of detail (LoD) information. Claim 11 In claim 10, the step of encoding the point cloud data comprises generating information indicating the sampling positions of nodes within a specific depth of the tree of the attribute data, wherein the sampling positions are either the first node or the last node, an encoding method. Claim 12 In claim 10, the step of encoding the point cloud data comprises generating information indicating the sampling positions of nodes within the first depth of the tree of the attribute data, wherein the sampling positions are either the first node or the last node, an encoding method. Claim 13 In claim 10, the step of encoding the point cloud data comprises sampling a node based on a sampling position within the first depth of the tree of the attribute data, wherein the sampling position is either the first node or the last node, encoding method. Claim 14 A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 9. Claim 15 A method comprising the steps of: acquiring a bitstream for point cloud data, wherein the bitstream is generated based on the steps of encoding geometry data of the point cloud data and encoding attribute data of the point cloud data; and transmitting data including the bitstream.

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