Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method

Abstract

A method according to embodiments may comprise the steps of: encoding geometry data of point cloud data; and encoding attribute data of the point cloud data. A method according to embodiments may comprise the steps of: decoding geometry data of point cloud data within a bitstream; and decoding attribute data of the point cloud data.

Description

Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device and point cloud data decoding method

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

[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.

[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.

[0005] To achieve the above-described purpose and other advantages, the method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream; and the step of decoding attribute data of the point cloud data.

[0006] A method according to the embodiments may include the step of encoding geometry data of point cloud data; and the step of encoding attribute data of the point cloud data.

[0007] The device and method according to the embodiments can process point cloud data with high efficiency.

[0008] The device and method according to the embodiments can provide a high-quality point cloud service.

[0009] 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.

[0010] 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.

[0011] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0012] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0013] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0014] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0015] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0016] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0017] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0018] FIG. 8 is an example of a transmission device according to embodiments.

[0019] FIG. 9 is an example of a receiving device according to embodiments.

[0020] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0021] FIGS. 11 and 12 illustrate a conventional partial encoding and partial decoding process.

[0022] FIG. 13 shows an example of a point cloud data configuration consisting of layers according to embodiments.

[0023] FIG. 14 shows a bitstream segment according to embodiments.

[0024] FIG. 15 shows a bitstream fragment according to the embodiments.

[0025] FIG. 16 illustrates a bitstream alignment method according to embodiments.

[0026] FIG. 17 illustrates a bitstream alignment method according to embodiments.

[0027] FIG. 18 shows an example of symmetrically selecting geometry attributes according to embodiments.

[0028] FIG. 19 shows an example of asymmetrically selecting geometry attributes according to embodiments.

[0029] FIG. 20 shows an example of a slice configuration according to embodiments.

[0030] FIG. 21 illustrates layer group-based geometry coding according to embodiments.

[0031] FIG. 22 shows examples of layer groups, subgroups, and corresponding bounding boxes according to embodiments.

[0032] FIG. 23 shows an example of incremental detail change for three levels of viewing distance according to embodiments.

[0033] FIG. 24 illustrates scalable transmission based on layer group slicing according to embodiments.

[0034] FIG. 25 shows a flowchart for missing node addition processing according to embodiments.

[0035] FIG. 26 shows an example of missing node addition processing according to embodiments.

[0036] FIG. 27 illustrates neighbor search according to embodiments.

[0037] FIG. 28 illustrates neighbor search according to embodiments.

[0038] FIG. 29 illustrates neighbor search according to embodiments.

[0039] FIG. 30 shows a flowchart of a layer group slicing encoder for LoD-based attribute coding according to embodiments.

[0040] FIG. 31 shows a flowchart of a decoder for LoD-based attribute decoding according to embodiments.

[0041] FIG. 32 shows a flowchart for the case of using Lod generation in subgroup units according to the embodiments.

[0042] FIG. 33 shows a subgroup structure according to embodiments.

[0043] FIG. 34 shows subsampling for a subgroup structure according to embodiments.

[0044] FIG. 35 shows the attribute inheritance relationship of the LoD structure according to the embodiments.

[0045] FIG. 36 shows a bitstream according to embodiments.

[0046] FIG. 37 shows the sequence parameter set syntax according to the embodiments.

[0047] FIG. 38 shows the dependent attribute data unit header syntax according to embodiments.

[0048] FIG. 39 shows the fgs_attribute_raw syntax according to the embodiments.

[0049] FIG. 40 illustrates partial encoding / decoding according to embodiments.

[0050] FIG. 41 illustrates an encoding method according to embodiments.

[0051] FIG. 42 illustrates a decoding method according to embodiments.

[0052] 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.

[0053] 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.

[0054] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0055] 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.

[0056] 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 Artificial Intelligence (AI) 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 Internet of Things (IoT) device, an AI device / server, etc.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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)).

[0062] 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).

[0063] 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.

[0064] 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.

[0065] 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 transmission side (e.g., transmission 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 transmission device (10000), or it may not be provided.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented in hardware, software, processors, and / or combinations thereof.

[0070] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0071] 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).

[0072] 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.).

[0073] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.).

[0078] 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.

[0079] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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 Hierarchical 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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).

[0094] 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.

[0095] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0100] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0101] 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).

[0102] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0103] 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.

[0104] 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.

[0105] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115]

[0116] 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.

[0117] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0118] n triangles

[0119] 3 (1,2,3)

[0120] 4 (1,2,3), (3,4,1)

[0121] 5 (1,2,3), (3,4,5), (5,1,3)

[0122] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0123] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0124] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0125] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0126] 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)

[0127] 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)

[0128] 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)

[0129] 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).

[0130] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0131] 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.

[0132] 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.

[0133] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] graph. Attribute prediction residuals quantization pseudo code

[0140] int PCCQuantization(int value, int quantStep) {

[0141] if( value >=0) {

[0142] return floor(value / quantStep + 1.0 / 3.0);

[0143] } else {

[0144] return -floor(-value / quantStep + 1.0 / 3.0);

[0145] }

[0146] }

[0147] graph. Attribute prediction residuals inverse quantization pseudo code

[0148] int PCCInverseQuantization(int value, int quantStep) {

[0149] if( quantStep ==0) {

[0150] return value;

[0151] } else {

[0152] return value * quantStep;

[0153] }

[0154] }

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 can be calculated from gl+1 2x, y, z and gl+1 2x+1, y, z. The weights of gl 2x, y, z and gl 2x+1, y, z are w1=wl 2x, y, z and w2=wl 2x+1, y, z.

[0165]

[0166] gl-1 x, y, z are low-pass values ​​used in the merging process at the next higher level. hl-1 x, y, z are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (300012)). The weights are calculated as wl-1 x, y, z = wl 2x, y, z + wl 2x + 1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0167]

[0168] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0169] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0170] 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.

[0171] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0172] 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).

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] FIG. 8 is an example of a transmission device according to embodiments.

[0186] 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).

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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 ).

[0201] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0202] 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.

[0203] FIG. 9 is an example of a receiving device according to embodiments.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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).

[0210] 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).

[0211] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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).

[0216] 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.

[0217] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] <PCC+XR>

[0224] 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.

[0225] 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.

[0226] <PCC+XR+모바일폰>

[0227] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0228] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0229] <PCC+자율주행+XR>

[0230] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0238] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0239] 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.

[0240] A point cloud transmission method / device (or encoding method and device) according to embodiments comprises a transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), an acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, encoding based on a layer structure of FIG. 13, bitstream segments of FIG. 14 and 15, bitstream alignment of FIG. 16 and 17, geometry and attribute selection of FIG. 18 and 19, encoding based on a slice configuration of FIG. 20, encoding based on a layer group of FIG. 21 and 22, an encoder of FIG. 24, encoding based on subsampling of FIG. 25 and 26, encoding based on nearest neighbor point search of FIG. 27 and 29, encoding of FIG. 30, and encoding based on a subgroup of FIG. 33 and 35. The bitstream and parameter information (syntax elements) of FIGS. 36 to 39, partial encoding of FIG. 40, encoding method of FIG. 41, etc., can be included and performed.

[0241] A point cloud receiving method / device (or decoding method and device) according to embodiments comprises a receiving device (10004) of FIG. 1, a receiver (10005), a point cloud video decoder (10006), a transmission-decoding-rendering (20002-20003-20004) of FIG. 2, a decoder of FIG. 7, a receiving device of FIG. 9, a device of FIG. 10, decoding based on a layer structure of FIG. 13, bitstream segments of FIG. 14 and 15, bitstream alignment of FIG. 16 and 17, geometry and attribute selection of FIG. 18 and 19, decoding based on slice configuration of FIG. 20, decoding based on a layer group of FIG. 21 and 22, a decoder of FIG. 24, decoding based on subsampling of FIG. 25 and 26, and nearest neighbors of FIG. 27 and 29. Decoding based on point search, decoding of FIGS. 31 to 32, decoding based on subgroups of FIGS. 33 to 35, acquisition of bitstream and parameter information (syntax elements) of FIGS. 36 to 39, partial decoding of FIG. 40, decoding method of FIG. 42, etc., can be included and performed.

[0242] Additionally, the point cloud data transmission / reception method / device (or encoding / decoding method / device) according to the embodiments may be referred to simply as the method / device according to the embodiments.

[0243] 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. In addition, occupancy trees, octrees, etc., used when decoding / encoding geometry data are interpreted as having the same meaning.

[0244] The embodiments include a method for encoding point cloud data based on a layer group slice, a method for transmitting a bitstream including encoded point cloud data and associated syntax, a method for receiving a bitstream, a method for decoding point cloud data based on a layer group slice, and the like.

[0245] 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. The embodiments include a method for selecting necessary information or removing unnecessary information at the bitstream level by dividing geometry and attribute data transmitted in existing data units into semantic units such as geometry octree and LoD (Level of Detail).

[0246] The embodiments include a method for constructing a data structure composed of point clouds. Specifically, it may include packing and signaling methods for effectively transmitting PCC data composed of layers, and may include a method for applying this to a scalable PCC-based service. In particular, when a direct compression mode is used for geometry compression, it may include a method for constructing and transmitting slice segments to be more suitable for a scalable PCC service. In particular, it may include a compression structure method for efficiently storing and transmitting large-capacity point cloud data with a wide distribution and high point density.

[0247] Referring to FIGS. 3 and FIGS. 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.

[0248] FIGS. 11 and 12 illustrate a conventional partial encoding and partial decoding process.

[0249] Point cloud data is compressed and transmitted by dividing location information of data points and feature information such as color, brightness, and reflectivity into geometry and attribute information. At this time, PCC data can be configured in an octree structure with layers depending on the level of detail or according to the Level of Detail (LoD), and based on this, scalable point cloud data coding and representation are possible. However, depending on the performance of the receiver or the transmission speed, it is possible to decode or represent only a part of the point cloud data, and conventionally, there is no method to remove unnecessary data in advance.

[0250] In other words, when only a portion of a scalable PCC bitstream needs to be transmitted (when only some layers of scalable decoding are decoded), it is not possible to select and send only the necessary parts; therefore, as shown in Fig. 11, the necessary parts must be re-encoded after decoding, or as shown in Fig. 12, the entire stream must be transmitted and then selectively applied at the receiver. However, in the case of Fig. 11, delay may occur due to the time required for decoding and re-encoding, and in the case of Fig. 12, 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.

[0251] In this case, for octree-based geometry compression, entropy-based compression methods and direct coding can be used together, and in this case, a slice configuration is required to efficiently utilize scalability.

[0252] In addition, for large-scale point clouds with a wide distribution and high point density, latency issues may occur due to the large number of bitstreams that must be processed to access the region of interest (ROI).

[0253] The embodiments implement a slice-independent decoder and include default attribute decoding, a method for specifying the range of partial LoDs generated from a partial occupancy tree, and a method for generating an output point cloud of partial depth decoding.

[0254] FIG. 13 shows an example of a point cloud data configuration consisting of layers according to embodiments.

[0255] The embodiments aim for efficient transmission and decoding by selectively transmitting and decoding at the bitstream level for layered point cloud data.

[0256] Referring to Fig. 13, the layering of point cloud data can have layer structures in various aspects such as SNR, sparial resolution, color, temporal frequency, and bit depth depending on the application field, and can form layers in a direction in which the density of data increases based on an octree structure or a LoD structure.

[0257] FIG. 14 shows a bitstream segment according to embodiments.

[0258] Referring to Fig. 14, the bitstream obtained through point cloud compression can be divided into a geometry data bitstream and an attribute data bitstream and transmitted according to the type of data. At this time, each bitstream can be transmitted by configuring it into slices, and the geometry data bitstream and the attribute data bitstream can each be configured into a single slice and transmitted regardless of layer information or LoD information. In this case, if only a part of the layer or LoD is to be used, the following steps must be taken: 1) decoding the bitstream, 2) selecting only the parts to be used and removing unnecessary parts, and 3) re-encoding based only on the necessary information.

[0259] Bitstream configuration

[0260] FIG. 15 shows a bitstream fragment according to the embodiments.

[0261] The embodiments may include a method of dividing the bitstream into layers (or LoDs) to avoid these unnecessary intermediate processes.

[0262] For example, considering the case of LoD-based PCC technology, it has a structure in which a low LoD (or LoD for a coarse level) is included in a high LoD (or LoD for a fine level). If we define R as information that is included in the current LoD but not in the previous LoD, that is, information newly included for each LoD, then as shown in Fig. 15, the initial LoD information and the information R newly included in each LoD can be divided and transmitted as independent units.

[0263] Bitstream alignment method

[0264] FIG. 16 illustrates a bitstream alignment method according to embodiments.

[0265] When transmitting a bitstream, geometry and attributes can be transmitted serially as shown in FIG. 16. In this case, depending on the type of data, the entire geometry information can be sent first, followed by the attribute information. In this case, there is an advantage that geometry information can be quickly restored based on the transmitted information.

[0266] FIG. 17 illustrates a bitstream alignment method according to embodiments.

[0267] In another method according to the embodiments, bitstreams constituting the same layer may be collected and transmitted as shown in FIG. 17. In this case, if a compression technique capable of parallel decoding of geometry and attributes is used, the decoding time can be reduced. Information that needs to be processed first (small LoD or LoD for coarse levels, geometry must precede attributes) can be placed first.

[0268] Bitstream selection

[0269] When transmitting a bitstream based on the method according to the embodiments, the desired layer (or LoD) in the application field can be selected at the bitstream level. When geometry information is collected and transmitted as shown in FIG. 16, there may be gaps in the middle after selecting the bitstream level, and in this case, the bitstream may need to be rearranged. When geometry and attributes are bundled and transmitted according to the layer as shown in FIG. 17, unnecessary information can be selectively removed as follows depending on the application field.

[0270] Selecting Symmetric Geometry-Attributes

[0271] FIG. 18 shows an example of symmetrically selecting geometry attributes according to embodiments.

[0272] Referring to Fig. 18, this represents a case where only up to LoD1 is selected for transmission or decoding, and information regarding R2 corresponding to the upper layer is removed before transmission / decoding.

[0273] Asymmetric Geometry-Attribute Selection

[0274] FIG. 19 shows an example of asymmetrically selecting geometry attributes according to embodiments.

[0275] Referring to Fig. 19, when geometry and attributes are transmitted asymmetrically, only the attributes of the upper layer can be removed and the entire geometry (gray area in the octree structure of triangles) can be selected for transmission / decoding.

[0276] Slice-level scalability vs. Octree-level scalability

[0277] The LoD defined in the embodiments of the present invention can be used as a unit to represent a set of one or more octree layers, and may also have the meaning of a bundle of octree layers to be configured in slice units. That is, while spatial scalability by actual octree layers (or scalable attribute layers) can be provided for each octree layer, when configuring scalability in slice units prior to bitstream parsing, it can be selected from the LoD unit defined in the embodiments of the present invention.

[0278] That is, as shown in Fig. 13, when utilizing scalability at the slice level, such as in scalable transmission, the provided scalable stages are three stages, LoD0, LoD1, and LoD2, and the scalable stages that can be provided in the decoding stage by the octree structure are eight stages from the root to the leaf.

[0279] Referring to FIG. 13, in the case where LoD0 to LoD2 are each composed of slices according to the embodiments, the transcoder of the receiver or transmitter may 1) select only LoD0, 2) select LoD0 and LoD1, or 3) select LoD0, LoD1, and LoD2.

[0280] 1) When only LoD0 is selected, the maximum octree level becomes 4, and one scalable layer among the octree layers from 0 to 4 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, and signal the node size at that time.

[0281] 2) When LoD0 and LoD1 are selected, Layer 5 is added, making the maximum octree level 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, and signal the node size at that time.

[0282] 3) When LoD0, LoD1, and LoD2 are selected, layers 6 and 7 are added, making the maximum octree level 7, and one scalable layer among the octree layers from 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 and signal the node size at that time.

[0283] Slice composition

[0284] FIG. 20 shows an example of a slice configuration according to embodiments.

[0285] As a technique for dividing a G-PCC bitstream into a slice structure, slices can be configured in finer units. For example, not only can one or more octree layers be matched in a single slice, but some nodes of an octree layer may also be included in a single slice, as shown in FIG. 20(a). Alternatively, as shown in FIG. 20(b) and FIG. 20(c), when multiple octree layers are matched in a single slice, only some nodes of each layer may be included. In such cases, when multiple slices constitute a geometry / attribute frame, information necessary to configure the layers can be transmitted to the receiver. This may include layer information included in each slice, node information included in each layer, etc.

[0286] scalable transmission

[0287] When a structure like that shown in Fig. 20 is used for scalable transmission, it can transmit information for selecting the slice required by the receiver. 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.

[0288] When applying scalable transmission to octree-based geometry bitstreams, it must be possible to construct point cloud data using only information up to a specific octree layer for the bitstreams of each octree layer from the root node to the leaf node. To achieve this, the target octree layer must not have any dependency on information from lower octree layers. This can be a constraint that applies commonly to geometry / attribute coding.

[0289] In addition, when transmitting scalable data, it is necessary to provide a scalable structure for selecting scalable layers at the transmitter and receiver. Considering the octree structure of FIG. 20, all octree layers may support scalable transmission, but scalable transmission may be enabled only for specific octree layers or lower. If some of the octree layers are included, by indicating which scalable layer the slice belongs to, it is possible to determine whether the slice is necessary or unnecessary at the bitstream stage.

[0290] For example, scalable transmission may not be supported for (1) starting from the root node in FIG. 20(a), and a single scalable layer may be configured, and the subsequent octree layers may be configured to have a one-to-one match with the scalable layer. Generally, scalability may be supported for parts corresponding to leaf nodes, and as in FIG. 20(c), when multiple octree layers are included within a slice, a single scalable layer may be defined for those layers.

[0291] In this case, scalable transmission and scalable decoding can be distinguished and used depending on the purpose. Scalable transmission can be used to select information up to a specific layer without passing through a decoder at the transmitting and receiving ends, while scalable decoding is intended to select a specific layer during the coding process. In other words, scalable transmission supports the selection of necessary information in a compressed state (at the bitstream stage) without passing through a decoder, enabling identification at the transmitting or receiving end. On the other hand, scalable decoding supports encoding or decoding only up to the necessary parts during the encoding / decoding process, allowing it to be used in cases such as scalable representation.

[0292] In this case, 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, if they include all layer information, scalable decoding may be possible for the leaf node layer, leaf node layer-1, and leaf node layer-2, respectively.

[0293] Fine granularity slicing

[0294] According to the embodiments, when fine granularity slicing is enabled, the G-PCC bitstream is divided into multiple sub-bitstreams. To effectively utilize the layering structure of the G-PCC, each slice may contain coded data of partial coding layers or coded data of partial regions.

[0295] When slice segments are combined with a coding layer structure, scalable transmission or spatial random access use cases can be supported in an efficient manner.

[0296] Layer-group based slice segment

[0297] FIG. 21 illustrates layer group-based geometry coding according to embodiments.

[0298] According to the embodiments, in fine granularity slicing, each slice segment contains coded data for a layer group defined as follows.

[0299] A layer group is defined as a group of consecutive tree layers where the start depth and end depth can be any number within the tree depth, and the start must be smaller than the end. The order of coded data within a slice partition must be the same as the order of coded data within a single slice.

[0300] For example, referring to FIG. 21(a), for a geometry coding layer structure having 8 layers, there are 3 layer groups, and each layer group can be matched with different slices as follows. Layer group 1 can be matched with coding layers 0 through 4, layer group 2 can be matched with coding layer 5, and layer group 3 can be matched with coding layers 6 through 7. When the first two slices are transmitted or selected, the decoded output will be partial layers 0 through 5 of FIG. 21(b). By using slices with a layer group structure, partial decoding of partial coding layers can be supported without accessing the entire bitstream.

[0301] According to the embodiments, in addition to the layer group structure, dividing the layer group into multiple subgroups can be used to consider a spatial random access use case.

[0302] A subgroup is a subset of a layer group, and the points in the subgroups are adjacent to each other. The subgroups of a layer group are mutually exclusive, and the set of points in the subgroups of a layer group is identical to the set of points in the layer group.

[0303] Since the points in each subgroup are bounded within a spatial region, the boundaries of the subgroups can be described by subgroup bounding box information. By using spatial information, the layer group and subgroup structures can support efficient access to the Region of Interest (ROI) by selecting slices that cover the ROI.

[0304] FIG. 22 shows examples of layer groups, subgroups, and corresponding bounding boxes according to embodiments.

[0305] Referring to FIG. 22, layer group 2 and layer group 3 are each divided into 2 and 4 subgroups, respectively, and included in different slices. According to embodiments, given the slices of layer groups and subgroups and their bounding box information, spatial access can be performed by 1) comparing the bounding box of each slice with the ROI, 2) selecting slices whose subgroup bounding boxes overlap with the ROI, and 3) decoding the selected slices.

[0306] For example, when the ROI is considered to be in region 3-3, slices 1, 3, and 6 are selected because the subgroup bounding boxes of layer group 1, subgroup 2-2, and subgroup 3-3 cover the ROI region. For effective spatial access, it is assumed that there are no dependencies between subgroups within the same layer group. For live streaming or low-latency use cases, selection and decoding can be performed at the time each slice segment is received, which can improve time efficiency.

[0307] According to the embodiments, based on scalability and spatial access capabilities, layer group slicing can provide efficient access to large-scale point cloud data or high-density point cloud data. Due to the large number of points and data size, rendering or displaying such content can take a significant amount of time. As an alternative approach, the level of detail (LoD) can be adjusted according to the viewer's interest. For example, if the viewer is far from a scene or object, structural or global area information is more important than local details. On the other hand, when the viewer approaches a specific area or object, detailed information about the area of ​​interest is required. The embodiments use an adaptive method so that the renderer can provide the viewer with data of sufficient quality in an efficient manner.

[0308] FIG. 23 shows an example of incremental detail change for three levels of viewing distance according to embodiments.

[0309] Referring to FIG. 23, it shows an incremental change in detail for three levels of viewing distance according to embodiments, and the viewing distance can be changed based on the region of interest (ROI).

[0310] Figure 23 shows the change in detail according to 1) a coarse detail, 2) a medium-level detail, and 3) a fine-grained detail.

[0311] FIG. 24 illustrates scalable transmission based on layer group slicing according to embodiments.

[0312] According to the embodiments, when layer group slicing is used for G-PCC bitstream generation, multi-resolution ROIs may be supported. Referring to FIG. 24, the encoder may generate bitstream slices of octree layer groups or spatial subgroups of each layer group. Upon request, a slice matching an ROI of each resolution may be selected and transmitted. Since the bitstream does not contain details other than the requested ROI, the total bitstream size is smaller than that of a tile-based approach.

[0313] According to embodiments, the decoder at the receiver can combine slices to generate three outputs: 1) an upper-level view output from layer group slice 1, 2) an intermediate-level view output from layer group slice 1 and selected subgroups of layer group 2, and 3) a lower-level view output of fine detail from layer group 1 and selected subgroups of layer groups 2 and 3. Since the outputs can be generated progressively, the receiver can provide a viewing experience with progressively increasing resolution, such as zooming in from an upper-level view to a lower-level view.

[0314] Coding process

[0315] LoD Generation: Missing FGSs (Fine Granularity Slices)

[0316] If Fine Granularity Slices (FGSs) are missing, nodes belonging to the subgroup bounding boxes cannot be found in the parent subgroups through the subsampling process. Since these nodes are required for the attribute coding process of the parent subgroups, the missing nodes must be added during the subsampling process.

[0317] FIG. 25 shows a flowchart for missing node addition processing according to embodiments. FIG. 26 shows an example of missing node addition processing according to embodiments.

[0318] Referring to FIGS. 25 and 26, if a child Fine Granularity Slice (FGS) is lost, the missing nodes can be detected in the finest LoD of the parent subgroup. According to embodiments, for this process, the interim output nodes of the geometry FGS corresponding to the current parent subgroup can be used as a reference node list. After comparing the nodes subsampled from the present child subgroups with the interim geometry slice output nodes, missing nodes that are present in the geometry output but not in the subsampled output can be appended to the LoD of the current subgroup. That is, LoD N can be generated by the nodes subsampled from LoD N+1 and the missing nodes of the current layer group.

[0319] 1) Nodes subsampled from LoD N+1: Indexed to LoD N by a G-PCC octree-based subsampling process.

[0320] 2) Missing nodes due to missing child FGSs: Detect missing nodes in the geometry FGS intermediate output and add them to LoD N.

[0321] The above is based on the case where the LoD with the farthest distance between points within the LoD (the coarest LoD) is set as LoD0; however, when the LoD with the shortest distance between points within the LoD (the finest LoD) is set as LoD0, LoD N+1 can be generated by nodes subsampled from LoD N and missing nodes of the current layer group.

[0322] Nearest Neighbor Search: Intra-layer group search

[0323] Based on the nearest neighbor search method of G-PCC, the nearest neighbor search according to the embodiments can also find prediction candidates from neighbor nodes of the higher LoD (the finer LoD) and coded nodes of the current LoD. For fast search, the search range may be limited by the number of points on the cubic boundary and Morton coding order.

[0324] Considering the boundaries of the subgroups, the embodiments may use an additional constraint called an intra-layer group search boundary as follows when a neighbor candidate is in the same layer group.

[0325] Intra-layer-group search boundary: When neighbor candidates are in the same layer group as the current node, it restricts the use of neighbor nodes only to those within the same subgroup boundary.

[0326] FIG. 27 illustrates neighbor search according to embodiments.

[0327] Referring to Fig. 27, neighbor nodes outside the subgroup bounding box cannot be neighbor candidates of the current node (the circle with the inside painted black).

[0328] Additionally, the embodiments may use the layer-group adapted position defined below during subgroup estimation and neighbor distance calculation. By taking into account the case of missing child subgroups, this can prevent a mismatch between the encoder and the decoder.

[0329] Layer-group adpted position: Refers to the value obtained by right-shifting the coded position by the number of levels of the coded descendant subgroups, and then left-shifting it by the number of levels of the coded descendant subgroups and the skipped descendant subgroups.

[0330] Nearest Neighbor Search - Inter-layer Group Search

[0331] FIG. 28 illustrates neighbor search according to embodiments.

[0332] When the current LoD is at the coarsest level of the current layer group, neighbor candidates may be in a different layer group. In this case, if the nodes are within the parent subgroup boundary, finding neighbors beyond the subgroup boundary may be allowed.

[0333] Inter-layer-group search boundary: When neighbor candidates are in a parent layer group, the use of neighbor nodes is restricted to only those within the parent subgroup boundary.

[0334] FIG. 29 illustrates neighbor search according to embodiments.

[0335] When using layer-group adapted position, the coded positioning of nodes in a parent node can be handled differently depending on whether those nodes are within the current node's subgroup bounding box.

[0336] The layer group adaptive position can be calculated as follows.

[0337] For nodes within the child subgroup bounding box, the coded position can be calculated by right-shifting by the number of LoDs of the coded descendant subgroups of the current subgroup, and then left-shifting by the number of LoDs of the coded descendant subgroups and skipped descendant subgroups of the current subgroup.

[0338] For nodes outside the child subgroup bounding box, the coded position can be calculated by right-shifting by the number of LoDs of the coded descendant subgroups of the parent subgroup, and then left-shifting by the number of LoDs of the coded descendant subgroups and skipped descendant subgroups of the parent subgroup.

[0339] That is, according to the embodiments, when a neighbor candidate is within the subgroup bounding box, the details of the geometry position can be considered down to the finest level of the child subgroup. On the other hand, when a neighbor candidate is outside the subgroup bounding box, the details of the geometry position can be considered down to the finest level of the parent subgroup.

[0340] Attribute encoding process

[0341] FIG. 30 shows a flowchart of a layer group slicing encoder for LoD-based attribute coding according to embodiments.

[0342] Referring to FIG. 30, parameters such as SPS (sequence parameter set), GPS (geometry parameter set), APS (attribute parameter set), and LGSI (layer group structure inventory) are first generated, and a layer group structure can be configured. The configuration of the layer group structure can be performed when generating the LGSI or before. After geometry coding is performed, LoD generation can be performed based on the geometry nodes. After matching the attribute coding layer (e.g., LoD, RAHT coding layer) with the layer group, quantization weights can be obtained based on the LoD. Subsequently, for each attribute coding layer, a check can be performed to determine whether the layer group has changed, and for the nodes within the attribute coding layer, a check can be performed to determine whether the subgroup has changed. When a subgroup or layer group changes, the attribute encoder for the previous subgroup is saved and the attribute encoder for the current subgroup is used; this ensures continuity of context and zerorun within the subgroup, as well as independence from neighboring subgroups. Once compression of all nodes belonging to the attribute coding layer is complete, the coded bitstream for each subgroup is packaged into its respective slice.

[0343] According to the embodiments, the contents of the flowchart in FIG. 30 can be applied not only to LoD-based attribute coding but also to other attribute coding such as RAHT.

[0344] Attribute decoding process

[0345] FIG. 31 shows a flowchart of a decoder for LoD-based attribute decoding according to embodiments.

[0346] Referring to Fig. 31, the inputs of the attribute decoder are an attribute Fine Granularity Slice (FGS) bitstream, decoded geometry data, and a layer group structure. Based on the decoded geometry data provided after geometry decoding, LoD generation can be performed, and weight derivation can be performed based on the relationships between nodes according to the LoD. The information generated at this time can be used in subsequent attribute decoding processes. The first attribute slice can operate in the same way as the previous attribute slice. Subsequently, if layer group slicing is enabled, the dependent attribute data unit header can be parsed, and layer group ID (layer_group_id) and subgroup ID (subgroup_id) information can be obtained. If necessary, information about the LoD can be signaled. Based on this, the layer group matching the current LoD can be found, and the parent subgroup can be selected. According to the embodiments, when the layer group structure is applied equally to geometry and attributes, the reference and parent information used in geometry decoding can be used. Based on this, information of dependent attribute data units can be decoded and point cloud data can be reconstructed.

[0347] For decoding, the LoD generation and weight derivation methods used in the encoder can be used.

[0348] LoD-based attribute coding can be performed by generating a LoD based on decoded geometry points and then predicting the neighbor relationships between nodes belonging to each LoD. Conventional LoD generation could only be performed after geometry decoding was completed. Therefore, even when geometry / attribute bitstreams consisted of slices, attribute slice decoding began only after all geometry slices of the region of interest had been decoded, which became a delay factor in rapidly decoding the slices of interest.

[0349] In the method according to the embodiments, geometry coding - LoD generation - attribute coding are performed in slice units / subgroup units through LoD generation in subgroup units, thereby enabling more active response to low-latency environments.

[0350] FIG. 32 shows a flowchart for the case of using Lod generation in subgroup units according to the embodiments.

[0351] Referring to FIG. 32, the biggest difference between the method according to the embodiments and the prior art is that LoD generation is performed in subgroup units, weights are derived based on the generated LoD, and attribute transformations are performed on points within the LoD.

[0352] When corresponding geometry slices and attribute slices are passed alternately, if a geometry slice remains, the geometry decoder is restarted, and then the corresponding attribute decoder is operated. If attribute slices are passed after geometry slices, the entire attribute slice can be decoded after decoding the entire geometry slice.

[0353] Preprocessing: Generating subgroup decoder input

[0354] According to the embodiments, as a method to increase the compression efficiency of subgroup-based attribute coding, a subgroup related to the current subgroup can be used. The input point cloud of the subgroup decoder may consist of points of the current subgroup and points of the parent subgroup.

[0355] Position correction can be performed on points belonging to the current subgroup. That is, since decoding can be performed before the geometry tree depth reaches full depth, the difference between the final geometry tree depth and the geometry tree depth for the current subgroup can be compensated.

[0356] Tree Level Gap (treeLvlGap) = Final Geometry Tree Depth - Maximum Geometry Depth for the Current Subgroup

[0357] if (curLayerGroup < _layerGroupParams.numLayerGroupsMinus1 - _params.numSkipLayerGroups) { / 1. nodes in the current subgroup / 1-1. non-idcm nodesauto pointCloud_subgroup_fifo = _layerGroupParams.pointCloud_tempNodes[curLayerGroup][curSubgroup];tempPointCloud.resize(pointCloud_subgroup_fifo.getPointCount());for (int i = 0; i < pointCloud_subgroup_fifo.getPointCount(); i++) {tempPointCloud[i] = pointCloud_subgroup_fifo[i] << treeLvlGap;tempIdxToPointIdx.push_back(-1);}tempPointCloudIdx.push_back(tempPointCloud.getPointCount());

[0358] After performing position precision correction between different layer groups through the process of Table 1, nodes belonging to the parent and nodes belonging to the current subgroup can be generated as inputs to the subgroup decoder.

[0359] FIG. 33 shows a subgroup structure according to embodiments.

[0360] Referring to FIG. 33, when the current subgroup is called subgroup C, the parent subgroup can be considered as subgroup A. In this case, the subgroup decoder input may include points f through q belonging to subgroup C and points d, h, and o belonging to subgroup A.

[0361] In this case, when including points belonging to the parent subgroup associated with the current subgroup, only those not included within the current subgroup's range are added. This is because, according to the definition of a LoD that a child LoD includes all points from a parent LoD, points belonging to the parent subgroup's bottom LoD are all included in the child subgroup's top LoD.

[0362] FIG. 34 shows subsampling for a subgroup structure according to embodiments.

[0363] When considering subsampling in the LoD generation process, points of a lower LoD can be sampled into an upper LoD as shown in FIG. 34. Referring to FIG. 33 and FIG. 34, points h and o belonging to subgroup A can be sampled from subgroup C, and point d can be sampled from subgroup B.

[0364] If all points within the parent subgroup are included without considering subsampling, h and o are included redundantly, which causes a mismatch with the encoder and results in an attribute decoder error. To generate the subgroup decoder input while avoiding point duplication, only points outside the subgroup bounding box can be included as shown in Table 2 (e.g., point d in Fig. 34).

[0365] if (curLayerGroup > 0) { / 2. parent subgroup nodes out of the current bboxauto bbox_min = _layerGroupParams.subgrpBboxOrigin[curLayerGroup][curSubgroup];auto bbox_max = bbox_min + _layerGroupParams.subgrpBboxSize[curLayerGroup][curSubgroup];int treeLvlGap_parent = _sps->root_node_size_log2.max() - accLayer[parentLayerGroup];auto pointCloud_subgroup_fifo_parent = _layerGroupParams.pointCloud_tempNodes[parentLayerGroup][parentSubgroup];int pointCount_parentNonIdcm = pointCloud_subgroup_fifo_parent.getPointCount();auto bbox_min_parent = _layerGroupParams.subgrpBboxOrigin[parentLayerGroup][parentSubgroup];auto bbox_max_parent = bbox_min_parent + _layerGroupParams.subgrpBboxSize[parentLayerGroup][parentSubgroup];int pointCount = tempPointCloud.getPointCount();tempPointCloud.resize(pointCount + pointCount_parentNonIdcm);int count = 0;if ( !(bbox_min == bbox_min_parent && bbox_max == bbox_max_parent) {for (int i = 0; i < pointCount_parentNonIdcm; i++) {auto pos = pointCloud_subgroup_fifo_parent[i] << treeLvlGap_parent;if (!(pos >= bbox_min && pos < bbox_max)) {int tempIdx = pointCount + count++;tempPointCloud[tempIdx] = pos;if (attr_sps.attr_num_dimensions_minus1 == 0)tempPointCloud.setReflectance(tempIdx, pointCloud_subgroup_fifo_parent.getReflectance(i));else if (attr_sps.attr_num_dimensions_minus1 == 2)tempPointCloud.setColor(tempIdx, pointCloud_subgroup_fifo_parent.getColor(i));tempIdxToPointIdx.push_back(-1);}}}elsetempPointCloud.resize(pointCount + count);tempPointCloudIdx.push_back(tempPointCloud.getPointCount());}.

[0366] Create Subgroup LoD

[0367] According to the embodiments, the inputs of the LoD generation process are the current point and the parent point, and the outputs of the LoD generation process are the parent bottom LoD and the current LoDs.

[0368] Through the aforementioned preprocessing, the input for creating a subgroup LoD includes both the current point and parent points located outside the current subgroup boundary. Points newly included in each LoD through LoD creation are included in the indexes; however, prior to performing a neighbor search, only points belonging within the subgroup boundary can be selected to perform the neighbor search, as shown in Table 3. Additionally, the point position resolution can consider the geometry resolution of the current subgroup.

[0369] for (int i = startIndex; i < endIndex; i++) {int packedVoxelIdx = indexes[i];int pointIndex = packedVoxel[packedVoxelIdx].index;auto pos = pointCloud[pointIndex];if (pos >= bbox_min && pos < bbox_max)indexes_subgroup.push_back(packedVoxelIdx);}for (int k = 0; k < indexes_subgroup.size(); k++) {auto point = pointCloud[packedVoxel[indexes_subgroup[k]].index];biasedPos_indexes.push_back(times((point >> shiftLayerGroup) << (shiftLayerGroup + treeLvlGap), aps.lodNeighBias));}

[0370] During the subsampling process, points existing in the upper LoD are included in the retained area, and among them, only points included in the subgroup boundary to which the parent LoD belongs can be selectively used for neighbor search. As another method according to the embodiments, as shown in Table 4, for all parent LoDs, it can be determined whether to use them for Nearest Neighbor (NN) search based on the bounding box of the parent subgroup.

[0371] for (int i = 0; i < retained.size(); i++) {int packedVoxelIdx = retained[i];int pointIdx = packedVoxel[packedVoxelIdx].index;auto pos = pointCloud[pointIdx];if (pos >= parent_bbox_min && pos < parent_bbox_max)retained_subgroup.push_back(packedVoxelIdx);}

[0372] According to the embodiments, for points currently outside the subgroup bounding box as shown in Table 5, the geometry precision can be made to use the geometry precision of the parent subgroup.

[0373] for (int k = 0; k < retained_subgroup.size(); k++) {auto point = pointCloud[packedVoxel[retained_subgroup[k]].index];if (prtLayerGroup != curLayerGroup) {if (pos >= cur_bbox_min && pos.x() < cur_bbox_max)biasedPos_retained.push_back(times((point >> shiftLayerGroup) << (shiftLayerGroup + treeLvlGap), aps.lodNeighBias));elsebiasedPos_retained.push_back(times((point >> shiftPrtLayerGroup) << (shiftPrtLayerGroup + treeLvlGap), aps.lodNeighBias));}elsebiasedPos_retained.push_back(times((point >> shiftLayerGroup) << (shiftLayerGroup + treeLvlGap), aps.lodNeighBias));}

[0374] Post-processing: Inherit decoded attribute

[0375] According to the definition of a LoD, which states that a child LoD contains all points of a parent LoD, all points belonging to the bottom LoD of a parent subgroup are included in the top LoD of a child subgroup. In other words, some of the points in the top LoD of the child subgroup match points in the bottom LoD of the parent subgroup, and the attributes of the points in the top LoD of the child subgroup can be inherited from the bottom LoD of the parent subgroup.

[0376] FIG. 35 shows the attribute inheritance relationship of the LoD structure according to the embodiments.

[0377] Referring to Fig. 35, among points d, h, and o belonging to the bottom layer of the parent subgroup, the points h and o that match points f, h, I, and o belonging to the top layer of the current subgroup (subgroup C) are, and the decoded attributes of points h and o can be matched as indicated by the arrows based on the subsampling relationship.

[0378] According to the embodiments, as shown in Table 6, among the points belonging to the bottom layer of the parent subgroup, the points belonging to the current subgroup bounding box can inherit attributes by referring to the parent attribute for the points whose positions match those of the points belonging to the top layer of the current subgroup.

[0379] if (curLayerGroup > 0) {int parentLayerGroup = curLayerGroup - 1;int parentSubgroup = layerGroupParams.parentSubgroupId[curLayerGroup][curSubgroup];int accLayer = 0;for (int i = 0; i <= parentLayerGroup; i++)accLayer += layerGroupParams.numLayersPerLayerGroup[i];int treeLvlGap_parent = sps.root_node_size_log2.max() - accLayer;auto bbox_min = layerGroupParams.subgrpBboxOrigin[curLayerGroup][curSubgroup];auto bbox_max = bbox_min + layerGroupParams.subgrpBboxSize[curLayerGroup][curSubgroup];std::vector <int>pointIndexInTheBbox;auto pointCloud_subgroup_fifo_parent = layerGroupParams.pointCloud_tempNodes[parentLayerGroup][parentSubgroup];int currentSubgroupIdxEnd = tempPointCloudIdx[layerGroupParams.numLayersPerLayerGroup[curLayerGroup]];for (int i = 0; i < _lods.numPointsInLod[0]; i++) {int pointIdx = _lods.indexes[i];if (pointIdx < currentSubgroupIdxEnd)pointIndexInTheBbox.push_back(pointIdx);}int numParentNodesInTheBbox = pointIndexInTheBbox.size();int sampledNodeIdx = numParentNodesInTheBbox - 1;for (int fifoIdx = 0; fifoIdx < pointCloud_subgroup_fifo_parent.getPointCount(); fifoIdx++) {pcc::Vec3 <int>pos_parent = pointCloud_subgroup_fifo_parent[fifoIdx] << treeLvlGap_parent;if (pos_parent >= bbox_min && pos_parent < bbox_max)int pointIdx = pointIndexInTheBbox[sampledNodeIdx];auto pos_lod0 = (pointCloud[pointIdx] >> treeLvlGap_parent) << treeLvlGap_parent;if (mortonAddr(pos_lod0) == mortonAddr(pos_parent)) {if (attr_desc.attr_num_dimensions_minus1 == 0)pointCloud.setReflectance(pointIdx, pointCloud_subgroup_fifo_parent.getReflectance(fifoIdx));else if (attr_desc.attr_num_dimensions_minus1 == 2)pointCloud.setColor(pointIdx, pointCloud_subgroup_fifo_parent.getColor(fifoIdx));if (!sampledNodeIdx)break;elsesampledNodeIdx--;}else {fifoIdx--;sampledNodeIdx--;}

[0380] The attribute obtained through the above process can be referenced as a neighbor during the subsequent attribute transformation process.

[0381] FIG. 36 shows a bitstream according to embodiments.

[0382] Transmitting device / method or encoding method / device according to embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, encoding based on a layer structure of FIG. 13, bitstream segments of FIG. 14 to 15, bitstream alignment of FIG. 16 to 17, geometry and attribute selection of FIG. 18 to 19, encoding based on slice configuration of FIG. 20, encoding based on a layer group of FIG. 21 to 22, encoder of FIG. 24, encoding based on subsampling of FIG. 25 to 26, encoding based on nearest neighbor point search of FIG. 27 to 29, encoding of FIG. 30, encoding based on a subgroup of FIG. 33 to 35, FIG. 36 to The bitstream and parameter information (syntax elements) of FIG. 39, partial encoding of FIG. 40, encoding method of FIG. 41, etc.) can encode point cloud data, generate the bitstream of FIG. 36, encapsulate a file containing the bitstream, and transmit it to a decoder.

[0383] A receiving device / method or a decoding method / device according to embodiments (receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, decoding based on a layer structure of FIG. 13, bitstream segment of FIG. 14 to 15, bitstream alignment of FIG. 16 to 17, geometry and attribute selection of FIG. 18 to 19, decoding based on slice configuration of FIG. 20, decoding based on a layer group of FIG. 21 to 22, decoder of FIG. 24, decoding based on subsampling of FIG. 25 to 26, and nearest neighbor point search based on FIG. 27 to 29 Decoding, decoding of FIGS. 31 to 32, decoding based on subgroups of FIGS. 33 to 35, acquisition of bitstream and parameter information (syntax elements) of FIGS. 36 to 39, partial decoding of FIG. 40, decoding method of FIG. 42, etc.) can receive a file containing the bitstream of FIG. 36 and decode point cloud data based on parameter information included in the bitstream.

[0384] Referring to FIG. 36, each abbreviation signifies 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: Attrobite bitstream = attribute blick header + attribute brick data

[0385] According to embodiments of the present invention, information regarding separated slices can be defined in parameter sets and SEI messages. It can be defined in SPS (Sequence Parameter Set), GPS (Geometry Parameter Set), APS (Attribute Parameter Set), Geometry Slice Header, and Attribute Slice Header, and depending on the application or system, it can be defined in corresponding locations or separate locations to use different scopes of application, application methods, etc. That is, depending on the location where the signal is transmitted, it may have different meanings; if defined in SPS, it may be applied uniformly to the entire sequence, and if defined in GPS, it may indicate that it is used for location restoration. If defined in APS, it may indicate that it is applied to attribute restoration, and if defined in TPS, it may indicate that the corresponding signaling is applied only to points within a tile. If transmitted at the slice level, it may indicate that the signaling is applied only to that slice.

[0386] In addition, depending on the application or system, the scope of application and the method of application may be used differently by defining it in a corresponding location or a separate location. Also, if the syntax elements of FIGS. 37 to 39 can be applied not only to the current point cloud data stream but also to multiple point cloud data streams, they can be transmitted through a higher-level concept parameter set, etc.

[0387] In the embodiments, the information is described as being defined independently of the coding technique, but it can be defined in conjunction with the coding method and can be defined in the TPS (Tile Parameter Set) to support regionally different scalability. Additionally, if the syntax elements of FIGS. 37 to 39 can be applied not only to the current point cloud data stream but also to multiple point cloud data streams, they can be transmitted through a higher-level parameter set, etc.

[0388] Alternatively, bitstreams can be selected at the system level by defining a NAL (Network abstract layer) unit and passing relevant information that allows selecting a layer, such as a layer_id.

[0389] FIG. 37 shows the sequence parameter set syntax according to the embodiments.

[0390] The value obtained by adding 1 to the number of layer groups (num_layer_groups_minus1) specifies the number of layer groups, where a layer group represents a group of consecutive tree levels within the occupancy tree.

[0391] The layer group ID (layer_group_id[i]) specifies the indicator for the i-th layer group within the layer group structure associated with the slice. The range of layer_group_id[i] is from 0 to num_layer_groups_minus1. If it does not exist, layer_group_id[i] is inferred to be 0.

[0392] The value obtained by adding 1 to the number of layers (num_layers_minus1[ i ]) specifies the number of tree levels of the i-th layer-group (where i represents the layer-group index of the i-th layer-group). The total number of layer-groups should be derived by adding all (num_layers_minus1[ i ] + 1) from i 0 to num_layer_groups_minus1.

[0393] If the value of the subgroup_enabled_flag is 1, it specifies that the layer groups of the current slice contain two or more subgroups. If the value of subgroup_enabled_flag is 0, it specifies that all layer groups contain one subgroup.

[0394] The value obtained by adding 1 to the subgroup bounding box origin bit (subgroup_bbox_origin_bits_minus1) specifies the bit length of the subgroup_bbox_origin syntax element.

[0395] The value obtained by adding 1 to the subgroup_bbox_size_bits_minus1 specifies the bit length of the subgroup_bbox_size syntax element.

[0396] Root_subgroup_bbox_origin specifies the location of the bounding box origin of the root subgroup.

[0397] Root subgroup bounding box size (root_subgroup_bbox_size) specifies the size of the root subgroup bounding box.

[0398] FIG. 38 shows the dependent attribute data unit header syntax according to embodiments.

[0399] The attribute parameter set ID (dadu_attribute_parameter_set_id) specifies the active APS (attribute parameter set) indicated by aps_attr_parameter_set_id. The value of dadu_attribute_parameter_set_id must be the same as the adu_geometry_parameter_set_id value of the corresponding slice.

[0400] The sequence parameter set attribute index (dadu_sps_attr_idx) identifies coded attributes through indices within the active SPS (sequence parameter set) attribute list.

[0401] The slice ID (dadu_slice_id) specifies the attribute slice to which the current dependent attribute data unit belongs.

[0402] The layer group ID (dadu_layer_group_id) specifies the layer-group indicator of the slice. The range of dadu_layer_group_id must be between 0 and num_layer_groups_minus1. If it does not exist, dadu_layer_group_id is inferred to be 0.

[0403] The subgroup ID (dadu_subgroup_id) specifies the subgroup indicator of the layer-group referenced by dadu_layer_group_id. The range of dadu_subgroup_id must be between 0 and num_subgroups_minus1[dadu_layer_group_id], where dadu_subgroup_id represents the order of slices within the same dadu_layer_group_id. If it does not exist, dadu_subgroup_id is inferred to be 0.

[0404] LoD-based attribute coding can be performed by generating a LoD based on decoded geometry points and then predicting the neighbor relationships between nodes belonging to each LoD. Conventional LoD generation could only be performed after geometry decoding was completed. Therefore, even when geometry / attribute bitstreams consisted of slices, attribute slice decoding began only after all geometry slices of the region of interest had been decoded, which became a delay factor in rapidly decoding the slices of interest.

[0405] In the method according to the embodiments, geometry coding - LoD generation - attribute coding are performed in slice units / subgroup units through LoD generation in subgroup units, thereby enabling more active response to low-latency environments.

[0406] FGS (Fine Granularity Slice) Default Attribute Generation Method

[0407] FIG. 39 shows the fgs_attribute_raw syntax according to the embodiments.

[0408] The FGS default attribute generation method can be applied when the attribute coding type (attr_coding_type) is 3, that is, when raw attribute decoding is performed.

[0409] The attribute value must be set to be the same as the corresponding raw_attr_value syntax element as shown in Table 7.

[0410] for (ptIdx = 0; ptIdx < SubgroupAttrNodeCnt[layerGroupIdx][subgroupIdx]; ptIdx++)for (c = 0; c < AttrDim; c++)PointAttr[ptIdx][c] = raw_attr_value[ptIdx][c]

[0411] The raw attribute component length (fgs_raw_attr_component_length) specifies the length in bytes of each syntax element fgs_raw_attr_value, if present.

[0412] The raw attribute value (fgs_raw_attr_value[ptIdx][c]) specifies the attribute value for the c-th component of the ptIdx-th point in canonical decoding order. The bit length of each syntax element is specified by the RawAttrValueBits expression.

[0413] LoD minimum level (LoDMinLevel) and LoD maximum level (LoDMaxLevel)

[0414] In the Levels of Detail (LoDs) generation process of the attribute decoding process, the subsampling process is performed from the finest detail level to the coarest detail level. In Fine Granularity Slices (FGSs), the subsampling process is performed on a partial occupancy tree, and the finest detail level and the coarest detail level are defined as LodMinLevel and LodMaxLevel, respectively.

[0415] However, conventionally, LodMinLevel and LodMaxLevel were calculated in the opposite way as shown below.

[0416] LodMinLevel = occtreeMaxDepthMinus1 - (startDepth-1)

[0417] LodMaxLevel = LodMinLevel+num_layers_minus1[layer_group_id]+1

[0418] The embodiments may include a method for defining LodMinLevel and LodMaxLevel based on startDepth and endDepth, which are used to describe the range of a partial occupancy tree in a geometry decoding process, in order to avoid confusion.

[0419] General generation process

[0420] When fgs_layer_group_enabled is 1, which specifies whether the slice contains multiple FGSs (fine granularity slices) of partial slice geometry or partial slice attributes, the minimum level of the LoD is specified in the variable LodMinLevel. The maximum level of the LoD can be set to overlap with the minimum level of the LoD of the parent FGS.

[0421] According to the embodiments, the finest detail level can be identified by the detail level index LoDMinLevel. LodMinLevel may be the log2 quantized node size of the finest level of the partial occupancy tree of the FGS geometry.

[0422] According to the embodiments, the coarest detail level can be identified by the detail level index LodMaxLevel. LodMaxLevel may be the log2 quantized node size of the coarseest level of the partial occupancy tree of the FGS geometry.

[0423] Detail levels must be iteratively subsampled starting from the finest detail level until a single point remains or a subsampled detail level identified by LoDMaxLevel is generated. The variable Lvl identifies the detail level to be subsampled.

[0424] According to the embodiments, as shown in Table 8, the minimum level of Levels of Detail (LoDs) for an FGS attribute identified as LodMinLevel can be calculated as the difference between the maximum depth of the full occupancy tree minus 1 (occtreeMaxDepthMinus1) and the end depth of the partial occupancy tree of the FGS geometry corresponding to the FGS attribute.

[0425] According to the embodiments, as shown in Table 8, the maximum level of Levels of Detail (LoDs) for an FGS attribute identified as LodMaxLevel can be calculated as the difference between the maximum depth of the full occupancy tree minus 1 (occtreeMaxDepthMinus1) and the start depth of the partial occupancy tree of the FGS geometry corresponding to the FGS attribute (startDepth).

[0426] LodMinLevel = occtreeMaxDepthMinus1 - endDepthLodMaxLevel = occtreeMaxDepthMinus1 - startDepthif(layer_group_id>0)LodMaxLevel++Lvl = LodMinLevelfor (; Lvl < LodMaxLevel; Lvl++) {if (LodPtCnt[Lvl] == 1)break... / * subsample LodPtIdx[Lvl] * / }LodCnt = Lvl + 1

[0427] The coarsest detail level can be identified by the detail level index LodCnt-1. All points belonging to the coarsest detail level must be assigned to the coarsest level's refinement list.

[0428] variable LodCnt

[0429] Levels of Detail (LoDs) can be specified by the following state variables. The index lvl identifies the detail level.

[0430] The variable LodCnt specifies the maximum number of detail levels generated in FGS (fine granularity slice) geometry.

[0431] FGS (Fine granularity slice) decoding process

[0432] General

[0433] When fgs_layer_group_enabled is equal to 1, the FGS within the coded point cloud frame must be decoded as follows:

[0434] 1. Point positions are decoded from one geometry data unit (GDU) and zero or more dependent geometry data units (DGDU), as specified in E.4.2.3 FGS geometry decoding process of standard document ISO / IEC 23090-38.

[0435] 2. Default attribute values ​​are set for each attribute as specified in 8.3.4 Default attribute values ​​of standard document ISO / IEC 23090-38.

[0436] 3. Point attributes are decoded in one attribute data unit (ADU) and zero or more dependent attribute data units (DADU), as specified in E.4.2.4 FGS attribute decoding process of standard document ISO / IEC 23090-38. The ADU and DADU must be decoded after the decoding of the GDU and DGDU, which are represented by the same layer_group_id and subgroup_id pairs.

[0437] 4. The decoded point position is offset and the output point count is incremented as specified in 8.3.6 At the end of a slice of standard document ISO / IEC 23090-38.

[0438] State variables

[0439] FGS decoding can be specified by the following state variables:

[0440] The variable startDepth represents the starting depth of the tree level within the data unit.

[0441] The variable endDepth represents the end depth of the tree level within the data unit.

[0442] FGS Geometry Decoding Process

[0443] Geometry Data Units (GDUs) must be decoded before all dependent Geometry Data Units (DGDUs) within a slice. DGDUs of child subgroups must be decoded after DGDUs of parent subgroups.

[0444] As described above, a root layer group includes one subgroup, and the FGS geometry for the said subgroup may include a geometry data unit (GDU). Additionally, a layer group excluding the root layer group may include multiple subgroups, and the FGS geometry for each subgroup may include a dependent geometry data unit (DGDU).

[0445] According to the embodiments, when decoding the GDU, startDepth and endDepth can be set to 0 and (num_layers_minus1[0] + 1), respectively.

[0446] According to the embodiments, when decoding DGDU, startDepth may be set to the cumulative value of (num_layers_minus1[k] + 1) where k is in the range from 0 to layer_group_id - 1. endDepth may be set to the cumulative value of (num_layers_minus1[k] + 1) where k is in the range from 0 to layer_group_id.

[0447] The expression SubgroupNodePos[layerGroupIdx][subgroupIdx][nodeIdx][k] is an alias for the position of the node at the end depth of the subgroup identified by the layer group index and the subgroup index, where layerGroupIdx is equal to layer_group_id and subgroupIdx is equal to subgroup_id.

[0448] As shown in Table 9, the k-th position of the node at the end depth of the subgroup identified by the layer group index and the subgroup index (SubgroupNodePos[layerGroupIdx][subgroupIdx][nodeIdx][k]) can be assigned the same as the position of the node at the end depth of the occupation tree included in the subgroup specified by the layer group index (layerGroupIdx) and the subgroup index (subgroupIdx).

[0449] SubgroupNodePos[layerGroupIdx][subgroupIdx][nodeIdx][k] := OccNodeLoc[endDepth][nodeIdx][k]

[0450] The expression SubgroupNodeCnt[layerGroupIdx][subgroupIdx] is an alias for the number of nodes at the end depth of a subgroup identified by the layer group index and the subgroup index.

[0451] As shown in Table 10, the number of nodes at the end depth of a subgroup identified by the layer group index and the subgroup index (SubgroupNodeCnt[layerGroupIdx][subgroupIdx]) can be assigned to be equal to the number of nodes at the end depth of the occupation tree included in the subgroup specified by the layer group index (layerGroupIdx) and the subgroup index (subgroupIdx).

[0452] SubgroupNodeCnt[layerGroupIdx][subgroupIdx] := OccNodeCnt[endDepth]

[0453] The expressions SubgroupBBoxMin[layerGroupIdx][subgroupIdx] and SubgroupBBoxMax[layerGroupIdx][subgroupIdx] are aliases for the minimum and maximum point positions of the bounding box of the subgroup identified by the layer group index and the subgroup index.

[0454] As shown in Table 11, the minimum point position of the bounding box of the subgroup (SubgroupBBoxMin[layerGroupIdx][subgroupIdx][k]) can be assigned to the origin value of the subgroup bounding box (subgroup_bbox_origin[k]), and the maximum point position of the bounding box of the subgroup (SubgroupBBoxMax[layerGroupIdx][subgroupIdx][k]) can be assigned to the value obtained by adding the size value of the subgroup bounding box (subgroup_bbox_size[k]) to the origin value of the subgroup bounding box (subgroup_bbox_origin[k]).

[0455] SubgroupBBoxMin[layerGroupIdx][subgroupIdx][k] := subgroup_bbox_origin[k]SubgroupBBoxMax[layerGroupIdx][subgroupIdx][k] := subgroup_bbox_origin[k] + subgroup_bbox_size[k]

[0456] The sparse array SubgroupOccNeighPatEq0[CurrLayerGroupIdx][CurrSubgroupIdx][ns][nt][nv] identifies whether the identified node of the parent subgroup does not have a node in the occupied neighborhood pattern.

[0457] The sparse array SubgroupOccNodeChildCnt[CurrLayerGroupIdx][CurrSubgroupIdx][k][ns][nt][nv] identifies the number of child nodes of the parent subgroup.

[0458] The expression SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx] is an alias for the number of points of direct nodes at the end depth of the subgroup identified by the layer group index and the subgroup index.

[0459] As shown in Table 12, when the layer group ID (layer_group_id) is equal to num_layer_groups_minus1, the positions of nodes within the subgroup are copied to the output point cloud.

[0460] if (layer_group_id == num_layer_groups_minus1) {for (i = 0; i < SubgroupNodeCnt[layerGroupIdx][subgroupIdx]; i++, PointCnt++)for (k = 0; k < 3; k++)PointPos[PointCnt][k] = SubgroupNodePos[layerGroupIdx][subgroupIdx][i][k]}

[0461] Node positions must be decoded and reconstructed as specified in E.5 Fine granularity slice geometry of standard document ISO / IEC 23090-38.

[0462] FGS attribute decoding process

[0463] An Attribute Data Unit (ADU) must be decoded before all Dependent Attribute Data Units (DADU) within a slice. A DADU of a child subgroup must be decoded after a DADU of the parent subgroup.

[0464] As described above, a root layer group includes one subgroup, and an FGS attribute for said subgroup may include an Attribute Data Unit (ADU). Additionally, a layer group excluding the root layer group may include multiple subgroups, and an FGS attribute for each subgroup may include a Dependent Attribute Data Unit (DADU).

[0465] ADU and DADU must be decoded, and reconstructed attribute values ​​must be stored in the corresponding output point cloud attributes or in the leaf nodes of the occupation tree within the subgroup.

[0466] When decoding ADU, startDepth and endDepth are set to 0 and (num_layers_minus1

[0000] + 1), respectively.

[0467] When decoding DADU, startDepth is set to the cumulative value of (num_layers_minus1[ k ] + 1) where k is in the range from 0 to layer_group_id - 1. endDepth is set to the cumulative value of (num_layers_minus1[ k ] + 1) where k is in the range from 0 to layer_group_id.

[0468] DirectNodePointCnt is set to the number of coded points in the direct nodes of each subgroup, as shown in Table 13.

[0469] DirectNodePointCnt := SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx]

[0470] The expression SlicePointAttr[ptIdx][c] is an alias for the array of output point cloud attributes of the points within the slice.

[0471] As shown in Table 14, the c-th attribute value of the ptIdx-th point within the slice can be assigned to the c-th attribute value of the attribute identified as RecCloudPointCnt + AttrIdx of the ptIdx-th point within the attribute array of the entire restored point cloud frame.

[0472] SlicePointAttr[ptIdx][c] := RecCloudAttr[RecCloudPointCnt + ptIdx][AttrIdx][c]

[0473] The expression SubgroupNodeAttr[layerGroupIdx][subgroupIdx][ptIdx][c] is an alias for an array of attributes of a subgroup identified by a layer group index and a subgroup index, where layerGroupIdx is equal to layer_group_id and subgroupIdx is equal to subgroup_id.

[0474] As shown in Table 15, SubgroupNodeAttr[layerGroupIdx][subgroupIdx][ptIdx][c] can be assigned the value of PointAttr[ptIdx][c], which is an alias of the point attribute array currently being decoded within the FGS.

[0475] SubgroupNodeAttr[layerGroupIdx][subgroupIdx][ptIdx][c] := pointAttr[ptIdx][c]

[0476] The expression SubgroupNodeAttrCnt[layerGroupIdx][subgroupIdx] is an alias for the number of direct coded nodes and occupancy tree generated nodes at the endDepth of the subgroup identified by the layer group index and the subgroup index. As shown in Table 16, SubgroupNodeAttrCnt[layerGroupIdx][subgroupIdx] can be set to the sum of the above-mentioned SubgroupNodeCnt[layerGroupIdx][subgroupIdx] and SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx].

[0477] SubgroupAttrNodeCnt[layerGroupIdx][subgroupIdx] := SubgroupNodeCnt[layerGroupIdx][subgroupIdx] + SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx]

[0478] Once FGS attribute decoding is complete, the attributes within the subgroup are copied to the output point cloud as shown in Table 17.

[0479] Referring to Table 17, whenever decoding of each layer group of the Fine Granularity Slice (FGS) is finished, 1) if it is the root layer group (layer_group_id is 0), the output attribute point count is initialized to 0, 2) if it is an intermediate layer group (layer_group_id is less than num_layer_groups_minus1), only the attributes of the direct nodes are copied to SlicePointAttr, and 3) if it is the last layer group (layer_group_id is equal to num_layer_groups_minus1), the attributes of all subgroup nodes including the direct nodes are copied to SlicePointAttr.

[0480] if(layer_group_id == 0)AttrPointCnt = 0if (layer_group_id < num_layer_groups_minus1)numNodes = SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx]elsenumNodes = SubgroupNodeCnt[layerGroupIdx][subgroupIdx]for (i = 0; i < numNodes; i++, AttrPointCnt++)for (c = 0; c < 3; c++)SlicePointAttr[AttrPointCnt][c] = SubgroupNodeAttr[layerGroupIdx][subgroupIdx][i][c]

[0481] Point attributes can be decoded and reconstructed as specified in E.6 Fine granularity slice attributes of standard document ISO / IEC 23090-38.

[0482] The expression AttrPos[ptIdx][k] specifies the coordinates of each point for attribute coding. As shown in Table 18, AttrPos is identical to SubgroupNodePos, which is the FGS geometry within the slice's coordinate system.

[0483] AttrPos[ptIdx][k] := SubgroupNodePos[ptIdx][k]

[0484] In this case, if there is an IDCM node, AttrPos must include the IDCM node as well as SubgroupNodePos. To this end, the embodiments may include a method of first selecting the IDCM node belonging to the current subgroup and including it in AttrPos, and then adding SubgroupNodePos. In this case, regarding the process of matching SubgroupNodeAttr to SlicePointAttr, a process of matching the position of the IDCM node with the attribute of the corresponding node may be added through a separate process of matching the index corresponding to the IDCM node among each node position in PointPos with the index of the corresponding node in AttrPos.

[0485] Partial density decoding

[0486] General

[0487] According to the embodiments, the decoder can generate a slice point cloud of lower density.

[0488] Lower density FGS point clouds can be specified by the following variables.

[0489] The variable SkippedLayerGroup specifies the application-specific number of layer groups to be skipped for partial decoding in the direction of the density. The value of SkippedLayerGroup must be in the range from 0 to num_layer_groups_minus1.

[0490] The variable MinNodeSizeLog2 specifies the minimum occupancy tree node size specified by SkippedLayerGroup.

[0491] The array SubgroupNodePos[layerGroupIdx][subgroupIdx][ptIdx][k] specifies the subgroup output nodes for layer group index layerGroupIdx and subgroup index subgroupIdx.

[0492] The array SubgroupNodeCnt[layerGroupIdx][subgroupIdx] specifies the number of nodes in the subgroup output nodes for layer group index layerGroupIdx and subgroup index subgroupIdx.

[0493] FGS selection

[0494] According to the embodiments, if SkippedLayerGroup is greater than 0, a layer group with an index in the range from 0 to OutLayerGroup may be selected to be decoded. As shown in Table 19, the maximum layer group index value for partial decoding, OutLayerGroup, may be specified as the total number of layer groups minus SkippedLayerGroup.

[0495] Referring to Table 19, if it is a root layer group (layer_group_id is 0), GDU or ADU can be decoded; if layer_group_id is less than or equal to the maximum layer group index value of partial decoding, DGDU or DADU can be decoded; and if layer_group_id is greater than the maximum layer group index value of partial decoding, DGDU or DADU can be skipped.

[0496] OutLayerGroup := num_layer_groups_minus1 - SkippedLayerGroupif (layer_group_id == 0)decode GDU or ADUelse if (layer_group_id ≤ OutLayerGroup)decode DGDU or DADUelseskip DGDU or DADU

[0497] Consequently, the geometry occupancy tree depth of partial decoding PartialDepth can be inferred as the sum of the number of layers in each layer group with indices ranging from 0 to OutLayerGroup, as shown in Table 20.

[0498] PartialDepth = 0for (i=0; I ≤ OutLayerGroup; i++)PartialDepth += num_layers_minus1[i] + 1

[0499] Geometry position compensation

[0500] According to the embodiments, the maximum depth of the geometry occupancy tree when decoding all layer groups can be inferred as the sum of the number of layers in each layer group with an index ranging from 0 to num_layer_groups_minus1, as shown in Table 21.

[0501] TotalDepth = 0for (i=0; i< num_layer_groups_minus1; i++)TotalDepth += num_layers_minus1[i] + 1

[0502] MinNodeSizeLog2 can be inferred as the difference between occtreeMaxDepthMinus1 and PartialDepth as shown in Table 22.

[0503] MinNodeSizeLog2 = occtreeMaxDepthMinus1 + 1 - PartialDepth

[0504] If MinNodeSizeLog2 is greater than 1, as shown in Table 23, the point must be located in the center of the corresponding block.

[0505] for (ptIdx = 0; ptIdx < SubgroupNodeCnt[layerGroupIdx][subgroupIdx]; ptIdx++)for (k = 0; k < 3; k++)SubgroupNode[layerGroupIdx][subgroupIdx][ptIdx][k] |= (MinNodeSizeLog2 > 1) << (MinNodeSizeLog2 - 1)

[0506] Output generation

[0507] According to the embodiments, when the layer group ID (layer_group_id) is the same as OutLayerGroup, the location of the node within the subgroup can be copied to the output point cloud as shown in Table 24.

[0508] if (layer_group_id == OutLayerGroup) {for (i = 0; i < SubgroupNodeCnt[layerGroupIdx][subgroupIdx]; i++, PointCnt++)for (k = 0; k < 3; k++)PointPos[PointCnt][k] = SubgroupNodePos[layerGroupIdx][subgroupIdx][i][k]}

[0509] According to the embodiments, when the layer group ID (layer_group_id) is the same as OutLayerGroup, attributes within the subgroup can be copied to the output point cloud as shown in Table 25.

[0510] if(layer_group_id == 0)AttrPointCnt = 0if (layer_group_id < num_layer_groups_minus1)numNodes = SubgroupDirectNodePointCnt[layerGroupIdx][subgroupIdx]elsenumNodes = SubgroupNodeCnt[layerGroupIdx][subgroupIdx]for (i = 0; i < numNodes; i++, AttrPointCnt++)for (c = 0; c < 3; c++)SlicePointAttr[AttrPointCnt][c] = SubgroupNodeAttr[layerGroupIdx][subgroupIdx][i][c]

[0511] The encoding method, decoding method, and signaling method of the above-described embodiments can provide the following effects.

[0512] From the perspective of the transmitter or encoder, the embodiments may include a method of dividing and transmitting compressed data according to certain criteria for point cloud data. According to the embodiments, when layered coding is used, the compressed data can be divided and sent according to the layer, in which case the storage and transmission efficiency of the transmitter is increased.

[0513] Referring to Fig. 11, when compressing and servicing the geometry and attributes of point cloud data, the compression rate or the number of data can be adjusted and sent according to the receiver performance or transmission environment in a PCC-based service. However, if the point cloud data is bundled into a single slice unit as in the conventional method, when the receiver performance or transmission environment changes, it is necessary to 1) convert the bitstream suitable for each environment in advance, store it separately, and select it when transmitting, or 2) perform a conversion process (transcoding) prior to transmission. In this case, if the number of receiver environments to be supported increases or the transmission environment changes frequently, storage space issues or delays caused by conversion may become a problem.

[0514] FIG. 40 illustrates partial encoding / decoding according to embodiments.

[0515] Referring to FIG. 40, when compressed data is divided and transmitted according to layers as in the embodiments, there is an advantage in that only the necessary parts of the pre-compressed data can be selectively transmitted at the bitstream stage without a separate conversion process. This is efficient in terms of storage space as only one storage space is required per stream, and efficient transmission is also possible in terms of bandwidth because only the necessary layers are selectively transmitted before transmission (bitstream selector).

[0516] From the perspective of a receiver or encoder, the embodiments may include a method of dividing and transmitting compressed data according to a certain standard for point cloud data. When using layered coding according to the embodiments, compressed data can be divided and sent according to the layer, in which case the efficiency of the receiver is increased.

[0517] Referring to Fig. 12, when information is transmitted that can restore the entire PCC data regardless of the receiver's performance, the receiver needs to perform a process (data selection or sub-sampling) to select only the data corresponding to the required layer after restoring the point cloud data through decoding. In this case, since the transmitted bitstream is already decoded, a delay may occur in a receiver aiming for low latency, or decoding may not be possible depending on the receiver's performance.

[0518] Referring to FIG. 40, when a bitstream is divided into slice units and transmitted according to embodiments, the receiver can selectively transmit the bitstream to the decoder based on the density of the point cloud data to be represented, depending on the decoder performance or application field. In this case, since the selection is made before decoding, the decoder efficiency is increased, and there is an advantage in that it can support decoders of various performance levels.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] FIG. 41 illustrates an encoding method according to embodiments.

[0523] The encoding method according to the embodiments may include the step of encoding geometry data of point cloud data (S4100); and / or the step of encoding attribute data of point cloud data (S4110). The step of encoding geometry data (S4100) and / or the step of encoding attribute data (S4110) may include the encoding operation of point cloud data described in FIGS. 1 to 40.

[0524] Referring together to FIGS. 21 and 22, the encoding method according to the embodiments is such that geometry data is encoded based on a layer group structure related to tree levels of an occupancy tree, attribute data is encoded based on the layer group structure, and the layer group structure may include layer groups identified by a layer group index.

[0525] An encoding method according to embodiments comprises a layer group in the layer group structure including subgroups, geometry data including Fine Granularity Slice (FGS) geometry for a subgroup in the subgroups, attribute data including FGS attributes for a subgroup, FGS geometry including data of a partial occupancy tree for the subgroup, a step of encoding geometry including a step of encoding FGS geometry based on a partial occupancy tree, a step of encoding attributes including a step of encoding FGS attributes using levels of detail (LoDs), a minimum level of LoDs for FGS attributes derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and a maximum level of LoDs for FGS attributes derived based on the maximum depth of the occupancy tree and It can be derived based on the difference in the starting depth of the partial occupancy tree.

[0526] The encoding method according to the embodiments comprises a layer group being a root layer group, the root layer group including a subgroup, geometry data including Fine Granularity Slice (FGS) geometry for the subgroup, attribute data including FGS attributes for the subgroup, FGS geometry including data of a partial occupancy tree for the subgroup, the step of encoding geometry including the step of encoding FGS geometry based on the partial occupancy tree, and the step of encoding attributes including the step of encoding FGS attributes using levels of detail (LoDs), wherein the minimum level of LoDs for FGS attributes is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and the maximum level of LoDs for FGS attributes can be derived from the maximum depth of the occupancy tree.

[0527] The encoding method is performed by an encoding device. The encoding device includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data. The at least one processor may be configured to perform the operations of the above-described method.

[0528] The embodiments further include a computer-readable storage medium for storing a bitstream generated by the method according to FIG. 41.

[0529] The embodiments further include a method comprising the steps of: acquiring a bitstream for point cloud data; generating the bitstream 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.

[0530] FIG. 42 illustrates a decoding method according to embodiments.

[0531] The decoding method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream (S4200); and / or the step of decoding attribute data of point cloud data (S4210).

[0532] Referring to FIGS. 21 and 22, the decoding method according to the embodiments is such that geometry data is decoded based on a layer group structure related to tree levels of an occupancy tree, attribute data is decoded based on a layer group structure, and the layer group structure may include layer groups identified by a layer group index.

[0533] A decoding method according to the embodiments comprises a layer group in the layer group structure including subgroups, geometry data including Fine Granularity Slice (FGS) geometry for one subgroup in the subgroups, attribute data including FGS attributes for one subgroup, FGS geometry including data of a partial occupancy tree for the subgroup, a step of decoding geometry including a step of decoding FGS geometry based on a partial occupancy tree, and a step of decoding attributes including a step of decoding FGS attributes using levels of detail (LoDs).

[0534] In the decoding method according to the embodiments, the finest level of the LoDs for the FGS attribute is related to the node size of the finest level of the partial occupancy tree of the FGS geometry, and the coarse level of the LoDs for the FGS attribute is related to the node size of the coarse level of the partial occupancy tree of the FGS geometry.

[0535] In the decoding method according to the embodiments, the minimum level of LoDs for an FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and the maximum level of LoDs for an FGS attribute can be derived based on the difference between the maximum depth of the occupancy tree and the start depth of the partial occupancy tree.

[0536] A decoding method according to the embodiments comprises a layer group being a root layer group, the root layer group including a subgroup, geometry data including Fine Granularity Slice (FGS) geometry for the subgroup, attribute data including FGS attributes for the subgroup, FGS geometry including data of a partial occupancy tree for the subgroup, a step of decoding geometry including a step of decoding FGS geometry based on a partial occupancy tree, a step of decoding attributes including a step of decoding FGS attributes using levels of detail (LoDs), the minimum level of LoDs for FGS attributes is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and the maximum level of LoDs for FGS attributes can be derived from the maximum depth of the occupancy tree.

[0537] The decoding method according to the embodiments has an index of the layer group equal to the number of layer groups, and the step of decoding FGS attributes may include the step of copying attributes for the direct nodes of the subgroup to the output point cloud; or the step of copying attributes for the nodes of the partial occupancy tree of the subgroup to the output point cloud.

[0538] The decoding method according to the embodiments has an index of layer groups smaller than the number of layer groups, and the step of decoding FGS attributes may include the step of copying attributes for direct nodes of subgroups to an output point cloud.

[0539] Referring together with FIG. 38, the decoding method according to the embodiments includes a layer group in the layer group structure comprising subgroups, geometry data comprising Fine Granularity Slice (FGS) geometry for one subgroup in the subgroups, attribute data comprising FGS attributes for one subgroup, the FGS attributes comprising dependent attribute data units, the dependent attribute data units comprising dependent attribute unit headers, and the dependent attribute unit headers may include information about an indicator of an attribute slice to which the dependent attribute data units belong, information about an indicator of a layer group, or information about an indicator of one subgroup.

[0540] The decoding method is performed by a decoding device. The decoding device includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: decode geometry data of point cloud data within a bitstream; and decode attribute data of point cloud data. The at least one processor may be configured to perform the operations of the aforementioned method.

[0541] 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.

[0542] 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.

[0543] 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.

[0544] 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" only, 2) "B" only, or 3) "A and B." In other words, "or" in this document may mean "additionally or alternatively."

[0545] 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.

[0546] 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."

[0547] 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.

[0548] 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.

[0549] 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.

[0550]

[0551]

[0552] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0553]

[0554] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0555] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0556] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.< / int> < / int>

Claims

A step of decoding geometry data of point cloud data within a bitstream; and A step of decoding attribute data of the above point cloud data; comprising Decryption method. In paragraph 1, The above geometry data is decoded based on a layer group structure related to the tree levels of an occupancy tree, and The above attribute data is decoded based on the above layer group structure, and The above layer group structure includes layer groups identified by a layer group index, Decryption method. In paragraph 2, A layer group in the layer group structure includes subgroups, and The above geometry data includes Fine Granularity Slice (FGS) geometry for a subgroup within the subgroups, and The above attribute data includes FGS attributes for the above one subgroup, and The above FGS geometry includes data of a partial occupancy tree for the subgroup, and The step of decoding the geometry includes the step of decoding the FGS geometry based on the partial occupancy tree, and The step of decoding the above attribute includes the step of decoding the above FGS attribute using LoDs (levels of detail). Decryption method. In paragraph 3, The finest level of the LoDs for the above FGS attribute is related to the node size of the finest level of the partial occupancy tree of the above FGS geometry, and The coarsest level of the LoDs for the above FGS attribute is related to the node size of the coarsest level of the partial occupancy tree of the above FGS geometry, Decryption method. In paragraph 3, The minimum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and The maximum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the starting depth of the partial occupancy tree, Decryption method. In paragraph 2, The above layer group is the root layer group, and The above root layer group includes one subgroup, and The above geometry data includes Fine Granularity Slice (FGS) geometry for the one subgroup, and The above attribute data includes FGS attributes for the above one subgroup, and The above FGS geometry includes data of a partial occupancy tree for the subgroup, and The step of decoding the geometry includes the step of decoding the FGS geometry based on the partial occupancy tree, and The step of decoding the above attributes includes the step of decoding the above FGS attributes using LoDs (levels of detail), and The minimum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and The maximum level of the LoDs for the above FGS attribute is derived from the maximum depth of the above occupancy tree, Decryption method. In paragraph 3, The index of the above layer group is equal to the number of the above layer groups, and The step of decoding the above FGS attributes is, A step of copying attributes for the direct nodes of the above subgroup to an output point cloud; or A step comprising copying attributes for the nodes of the partial occupancy tree of the above subgroup to the output point cloud, Decryption method. In paragraph 3, The index of the above layer group is smaller than the number of the above layer groups, and The step of decoding the above FGS attributes is, A step comprising copying attributes for the direct nodes of the above subgroup to an output point cloud, Decryption method. In paragraph 2, A layer group in the layer group structure includes subgroups, and The above geometry data includes Fine Granularity Slice (FGS) geometry for a subgroup within the subgroups, and The above attribute data includes FGS attributes for the above one subgroup, and The above FGS attribute includes a dependent attribute data unit, and The above dependent attribute data unit includes a dependent attribute unit header, and The above dependent attribute unit header includes any one of the following: information about an indicator of an attribute slice to which the dependent attribute data unit belongs, information about an indicator of a layer group, or information about an indicator of a sub-group. Decryption method. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Decoding geometry data of point cloud data within a bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decoding device. A step of encoding the geometry data of the point cloud data; and A step of encoding attribute data of the above point cloud data; comprising Encoding method. In Paragraph 11, The above geometry data is encoded based on a layer group structure related to the tree levels of an occupancy tree, and The above attribute data is encoded based on the above layer group structure, and The above layer group structure includes layer groups identified by a layer group index, Encoding method. In Paragraph 12, A layer group in the layer group structure includes subgroups, and The above geometry data includes Fine Granularity Slice (FGS) geometry for a subgroup within the subgroups, and The above attribute data includes FGS attributes for the above one subgroup, and The above FGS geometry includes data of a partial occupancy tree for the subgroup, and The step of encoding the geometry includes the step of encoding the FGS geometry based on the partial occupancy tree, and The step of encoding the above attributes includes the step of encoding the FGS attributes using LoDs (levels of detail), and The minimum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and The maximum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the starting depth of the partial occupancy tree, Encoding method. In Paragraph 12, The above layer group is the root layer group, and The above root layer group includes one subgroup, and The above geometry data includes Fine Granularity Slice (FGS) geometry for the one subgroup, and The above attribute data includes FGS attributes for the above one subgroup, and The above FGS geometry includes data of a partial occupancy tree for the subgroup, and The step of encoding the geometry includes the step of encoding the FGS geometry based on the partial occupancy tree, and The step of encoding the above attributes includes the step of encoding the FGS attributes using LoDs (levels of detail), and The minimum level of the LoDs for the above FGS attribute is derived based on the difference between the maximum depth of the occupancy tree and the end depth of the partial occupancy tree, and The maximum level of the LoDs for the above FGS attribute is derived from the maximum depth of the above occupancy tree, Encoding method. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Encoding the geometry data of the point cloud data; and Configured to encode the attribute data of the above point cloud data; Decoding device. A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 11. Step of acquiring a bitstream for point cloud data, The bitstream is generated based on the step of encoding geometry data of the point cloud data; and the step of encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream above.

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