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

Abstract

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

Description

Technology Field

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

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

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

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

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

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

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

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

[0009] Drawings are included to further understand the embodiments, and the drawings illustrate the embodiments along with descriptions related to the embodiments. For a better understanding of the various embodiments described below, one must refer to the description of the embodiments below in relation to the following drawings, which include parts corresponding to similar reference numerals throughout the drawings. FIG. 1 shows an example of a point cloud content provision system according to embodiments. FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments. FIG. 3 shows an example of a point cloud encoder according to embodiments. FIG. 4 shows examples of octree and occupancy codes according to embodiments. Figure 5 shows an example of a point configuration by LOD according to embodiments. Figure 6 shows an example of a point configuration by LOD according to embodiments. FIG. 7 shows an example of a point cloud decoder according to embodiments. FIG. 8 is an example of a transmission device according to embodiments. FIG. 9 is an example of a receiving device according to embodiments. FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments. FIG. 11 shows a spinning lidar model according to embodiments. FIG. 12 shows an encoding device according to embodiments. FIG. 13 shows a decoding device according to embodiments. FIG. 14 illustrates a method for generating a reference frame according to embodiments. FIG. 15 illustrates a method for generating a reference frame according to embodiments. FIG. 16 illustrates a method for generating a reference frame according to embodiments. FIG. 17 shows a bitstream according to embodiments. FIG. 18 shows a set of sequence parameters (SPS) in a bitstream according to embodiments. FIG. 19 shows a set of geometry parameters (GPS) in a bitstream according to embodiments. FIG. 20 shows a set of attribute parameters (APS) in a bitstream according to embodiments. FIG. 21 shows a set of tile parameters (TPS) in a bitstream according to embodiments. FIG. 22 shows a geometry slice header (GSH) within a bitstream according to embodiments. FIG. 23 shows an attribute slice header (ASH) within a bitstream according to embodiments. FIG. 24 illustrates a encoding method according to embodiments. FIG. 25 illustrates a decoding method according to embodiments. Specific details for implementing the invention

[0010] Best form for carrying out the invention

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

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

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

[0014] The point cloud content providing system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) can communicate via wired or wireless means to transmit and receive point cloud data.

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

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

[0017] A point cloud video acquisition unit (10001) according to the embodiments acquires a point cloud video through processing steps such as capture, synthesis, or generation. The point cloud video is a point cloud content represented as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data, etc. The point cloud video according to the embodiments may include one or more frames. A frame represents a still image / picture. Accordingly, the point cloud video may include a point cloud image / frame / picture and may be referred to as any one of a point cloud image, a frame, and a picture.

[0018] A point cloud video encoder (10002) according to the embodiments encodes the obtained point cloud video data. The point cloud video encoder (10002) can encode the point cloud video data based on point cloud compression coding. The point cloud compression coding according to the embodiments may include Geometry-based Point Cloud Compression (G-PCC) coding and / or Video-based Point Cloud Compression (V-PCC) coding or next-generation coding. Furthermore, the point cloud compression coding according to the embodiments is not limited to the embodiments described above. The point cloud video encoder (10002) can output a bitstream containing the encoded point cloud video data. The bitstream may include not only the encoded point cloud video data but also signaling information related to the encoding of the point cloud video data.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0070] A point cloud encoder according to the embodiments (e.g., a surface approximation analysis unit (30003)) can determine a specific level of an octree (where the level is smaller than the depth d of the octree) and, starting from that level, perform trisoop geometry encoding to reconstruct the position of points within a node region based on voxels using a surface model (trisoop mode). The point cloud encoder according to the embodiments can specify the level to which trisoop geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in trisoop mode. That is, the point cloud encoder according to the embodiments can operate in trisoop mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic region of nodes at a specified level according to the embodiments is referred to as a block. A block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented as a surface. A surface according to the embodiments may intersect each edge of the block at most once.

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

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

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

[0074]

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

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

[0077] n triangles

[0078] 3 (1,2,3)

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

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

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

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

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

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

[0085] 10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)

[0086] 11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)

[0087] 12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

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

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

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

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

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

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

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

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

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

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

[0098] graph. Attribute prediction residuals quantization pseudo code

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

[0100] if( value >=0) {

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

[0102] } else {

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

[0104] }

[0105] }

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

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

[0108] if( quantStep ==0) {

[0109] return value;

[0110] } else {

[0111] return value * quantStep;

[0112] }

[0113] }

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

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

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

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

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

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

[0120] 5) Lift update process: For all predictors, the attribute value of the update array is divided by the weight value of the update weight array at the predictor index, and the original attribute value is added back to the divided value.

[0121] 6) For all predictors, the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight (stored in QW) updated through the lift prediction process. A point cloud encoder according to the embodiments (e.g., coefficient quantizer (30011)) quantizes the predicted attribute value. Additionally, a point cloud encoder (e.g., arismetic encoder (30012)) entropies the quantized attribute value.

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

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

[0124]

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

[0126]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0180] The HMD (Head-Mount Display) (1070) represents one of the types in which an XR device and / or PCC device according to the embodiments may be implemented. A device of the HMD type according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit, etc.

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

[0182] <PCC+XR>

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

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

[0185] <PCC+XR+모바일폰>

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

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

[0188] <PCC+자율주행+XR>

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

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

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

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

[0193] VR (Virtual Reality) technology, AR (Augmented Reality) technology, MR (Mixed Reality) technology and / or PCC (Point Cloud Compression) technology according to the embodiments can be applied to various devices.

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

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

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

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

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

[0199] The encoding method / device according to the embodiments may include and perform operations such as a transmitting device (10000) of FIG. 1, an acquisition unit (10001), an encoder (10002), a transmitter (10003), acquisition (20000) of FIG. 2, encoding (20001), transmission (20002), encoders of FIG. 3, FIG. 8, FIG. 12, each device of FIG. 10, reference frame generation of FIG. 14 to FIG. 15, bitstream generation of FIG. 17 to FIG. 23, encoding of FIG. 24, etc.

[0200] The decoding method / device according to the embodiments may include and perform operations such as a receiving device (10004) of FIG. 1, a receiving unit (10005), a decoder (10006), a renderer (10007), decoding (20003) of FIG. 2, rendering (20004), decoders of FIG. 7, FIG. 9, FIG. 13, each device of FIG. 10, reference frame generation of FIG. 14 to FIG. 15, bitstream parsing of FIG. 17 to FIG. 23, decoding of FIG. 25, etc.

[0201] The encoding / decoding device according to the embodiments of each drawing may be composed of memory and a processor. The components of each drawing may correspond to hardware, software, a processor, and / or a combination thereof. The encoding / decoding method / device according to the embodiments may be referred to as the method / device for short.

[0202] The method / device according to the embodiments may include and perform reference frame buffer management for 3D contents captured by spinning LiDAR.

[0203] The embodiments include a reference frame buffer management method for inter-prediction compression of Geometry-based Point Cloud Compression (G-PCC) for point cloud frames captured by Spinning LiDAR equipment. For example, it may include a reference frame buffer management method for inter-prediction of geometry and attributes and / or a method for signaling parameter information related to reference frame buffer management for inter-prediction.

[0204] The embodiments relate to a method for increasing the compression efficiency of G-PCC Geometry-based Point Cloud Compression () for 3D point cloud data compression.

[0205] A point cloud consists of a set of points, and each point can have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.) or / and reflection values.

[0206] The G-PCC encoding process can divide the point cloud into tiles according to region and divide each tile into slices for parallel processing. It can be composed of a process of compressing geometry on a slice-by-slice basis and compressing attribute information based on reconstructed geometry (decoded geometry) with location information changed through compression.

[0207] The G-PCC decoding process can be composed of receiving a geometry bitstream and an attribute bitstream in the form of encoded slice units, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0208] For geometry information compression, octree (or accupancy tree) based, predictive tree based, or trisoup based compression techniques can be used.

[0209] For attribute information compression, predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used.

[0210] The embodiments include a reference frame buffer management method for efficiently applying compression through inter-prediction of content captured by LiDAR equipment.

[0211] FIG. 11 shows a spinning lidar model according to embodiments.

[0212] The method and apparatus according to the embodiments can extract depth information through lidar equipment utilizing a radar system that measures the position coordinates of a reflector by emitting a laser pulse and measuring the time it takes for the reflected object to return, for the purpose of capturing point cloud content. The point cloud content generated by the lidar equipment may consist of multiple frames, or multiple frames may be integrated into a single content.

[0213] Lidar at different elevations θ(i) i=1..N It is composed of N lasers (N=16, 32, 64, etc.), and the lasers can capture point cloud data while spinning along an azimuth Φ with respect to the Z-axis (see Fig. 11). This type is called a spinning LiDAR model, and the content captured by the spinning LiDAR model has angular characteristics.

[0214] Laser i hits object M, and the position of M can be estimated as (x, y, z) in the Cartesian coordinate system (see Fig. 11). Due to the fixed position of the laser sensors, their straight-line movement characteristics, and the fact that the sensors rotate at a certain azimuth angle, the position of M can have characteristics that allow the rules between points to be induced favorably for compression when expressed as (r, Φ, i) rather than (x, y, z) in the Cartesian coordinate system.

[0215] Therefore, by utilizing these characteristics, for data captured by spinning LiDAR equipment, compression efficiency can be higher by applying an angular mode during the geometry encoding / decoding process. An angular mode is a method of compressing using (r, Φ, i) instead of (x, y, z).

[0216] Geometry information is encoded and decoded, and geometry location information is reconstructed to compress attribute information based on that location. At this stage, attribute information can selectively use either a Cartesian coordinate system or an angular mode. Generally, the Cartesian coordinate system is primarily used.

[0217] Therefore, when storing reference frames in a buffer for inter-prediction, if different coordinate systems are used for geometry and attribute information, it may result in storing information from two different coordinate systems for a single reference frame. If only one is stored, the decoder must perform additional coordinate transformations, which increases computational load and complexity; if both types of information are stored, memory usage increases, potentially burdening the decoder. Consequently, the application of Bi-Prediction may require more computation and memory usage.

[0218] Additionally, there may be cases where global / local motion is applied only to geometry or only to attributes. Such cases can also increase the number of reference frames.

[0219] The embodiments include a reference frame buffer management method for applying predictive compression techniques through reference frames to a point cloud having multiple frames captured by a spinning lidar. The embodiments have the effect of minimizing computational load and minimizing memory usage.

[0220] The encoding / decoding method according to the embodiments includes the following reference frame buffer management method. For example, it may include 1) a reference frame buffer configuration step, 2) a reference frame generation step for encoding or decoding prediction tree-based geometry (or may be referred to as geometry data), 3) a reference frame generation step for encoding or decoding attribute information (or may be referred to as attribute data), and / or 4) an integrated reference frame generation step for geometry data and / or attribute data. The operation of each step is described below.

[0221] 1) Reference Frame Buffer Configuration Step:

[0222] The method / device according to the embodiments uses a reference frame for inter prediction. Points in the reference frame that match the geometry with the point in the current frame are found, and predictions are performed from the selected reference points. The residual value can be entropy-coded based on the reference point having the smallest residual value among the differences between the predicted value and the value of the current point, and then transmitted to a decoder.

[0223] Therefore, the reference frame can be the previous frame by default, a cumulative frame, or, in the case of using bi-prediction, one or more frames. The current frame can be referred to as the first frame, and the reference frame as the second frame.

[0224] Points within a reference frame may have geometry information based on an orthogonal coordinate system or geometry information based on an angular coordinate system. They may also have both types of information.

[0225] 2) Step for generating reference frames for prediction tree-based geometry (or geometry data) encoding or decoding:

[0226] When encoding or decoding the geometry data of point cloud data based on a prediction tree for content (point cloud data) acquired by a spinning lidar, it may be efficient to convert the encoding / decoding mode to an angle mode to compress and restore it.

[0227] When predicting tree-based geometry inter-predictive coding, reference frames can be stored as reference point information based on azimuth Φ.

[0228] To divide the entire 360 ​​degrees into zones, the entire angle can be divided into arbitrary values ​​(geom_quant: quantization value). The quantization (quant) value can be transmitted from the encoder to the decoder as parameter signaling information, or it can be set to an arbitrary value and used within the encoder / decoder.

[0229] In each region divided by quantization values, a specific number (geom_N) of points can be stored. A single point can be stored, or multiple points can be stored. The number of points (geom_N) can be set optionally and can be transmitted from the encoder to the decoder as parameter signaling information. The decoder must also store the number of points (geom_N) so that both the encoder and decoder can perform inter-prediction using the same number of reference points. For example, in predictive geometry encoding / decoding, the number of points (geom_N) can be set to 1.

[0230] If the points are sorted based on the azimuth Φ value, the azimuth Φ is first divided by the quantization value (geom_quant) to obtain the region, and the points within the region can be stored. Various methods can be applied to store the number of points (geom_N) within the region.

[0231] For example, points up to geom_N can be stored starting from the first point in sorted order, points up to geom_N can be stored in order of smallest value within an azimuth range, points up to geom_N can be stored starting from the last point, points up to geom_N can be stored in order of largest value within an azimuth range, and / or points up to geom_N can be stored by downsampling (e.g., points within an azimuth can be sampled from points that exist in detail based on a specific angle, and the sampled points can be stored).

[0232] Information regarding the method of storing reference point information of a reference frame can be transmitted from the encoder to the decoder as parameter information. The encoder and decoder can perform inter-prediction using the same number of reference points.

[0233] 3) Step for creating a reference frame for encoding or decoding attribute information (or attribute data):

[0234] Cartesian coordinate system mode can be used when compressing (predictive transformation, lifting transformation, RAHT transformation) or restoring attribute information (attribute data). Angle mode can also be used for attribute information compression / restoration. Reference frames for inter-predicting attribute information can also store point information of the coordinate system used in the current frame.

[0235] When using a coordinate system different from geometry coding in attribute coding (encoding / decoding), a separate reference frame can be created.

[0236] When using the same coordinate system as geometry coding in attribute coding (encoding / decoding), the reference frame used in geometry can be used together.

[0237] The coordinate system information can be identified through parameter information, which is signaling information within the G-PCC bitstream, for example, the spherical coordinate system flag (spherical_coord_flag) within the geometry parameter set and / or attribute parameter set.

[0238] When reference frames are stored separately because the coordinate systems used for geometry coding and attribute coding are different, you can specify how many points to store for the attribute reference frame using the point count information (attr_N). If attr_N=-1, all coordinates can be stored. The attr_N points can be transmitted from the encoder to the decoder as parameter signaling information.

[0239] Various methods can be applied to save points to reference frames.

[0240] For example, if the angle mode is an orthogonal coordinate system, up to attr_N points can be stored on nodes at a specific depth level of the octree. The octree depth level (attr_occtree_node_level) is signaled from the encoder to the decoder as parameter signaling information.

[0241] For example, if the angle mode is an angle coordinate system, the Φ value can be divided by the quantization (attr_quant) value based on the angle coordinate system to define a region, and the points corresponding to that region can be selected and stored in a reference frame using the following method.

[0242] For example, you can store only up to attr_N points starting from the first point, store only up to attr_N points in order of the smallest value in the azimuth range, store only up to attr_N points starting from the last point, store only up to attr_N points in order of the largest value in the azimuth range, and / or store only up to attr_N points by downsampling (e.g., sampling and storing points within the azimuth range that exist in detail based on a specific angle).

[0243] Although the attribute information coordinates are in the Cartesian coordinate system, Cartesian coordinate system point values ​​can also be stored by changing only the container based on the angular coordinate system.

[0244] The storage method is also signaled to the decoder. Accordingly, the encoder and decoder can perform inter-prediction through the same reference point. In the case of an angular coordinate system, the attr_quant value can be transmitted to the decoder or set to an arbitrary value for use within the encoder and decoder.

[0245] 4) Step for generating an integrated reference frame for geometry data and / or attribute data

[0246] To minimize computational load and memory usage, a unified reference frame can be generated for geometry and attribute coding. Whether to use a unified reference frame can be signaled to the decoder. Whether the same coordinate system is used between geometry coding and attribute coding can be determined through parameter information, which is signaling information within the bitstream.

[0247] When using a unified reference frame, the coordinate system of the attribute information takes precedence. Since compression efficiency is better when attribute information references points with higher Φ values, the reference frame is created with the attribute information's coordinate system as the priority. In other words, geometry information and attribute information can be stored in the reference frame according to the coordinate system used during attribute compression. A unified reference frame can be referred to as an attribute reference frame. Geometry and attribute inter-predictive encoding / decoding can be performed using an attribute reference frame.

[0248] When geometry, information, and attributes all use an orthogonal coordinate system, up to attr_N points can be stored on nodes at a specific depth level of the octree.

[0249] The octree depth level (attr_occtree_node_level) and attr_N value are signaled to the decoder.

[0250] When using an angular coordinate system for geometry compression, or when using an angular coordinate system for both geometry and attribute compression, regions can be defined by dividing the Φ value by the attr_quant value based on the angular coordinate system, and attr_N points corresponding to those regions can be stored. The Attr_N value is signaled to the decoder.

[0251] N points attr can be selected and stored in a reference frame using the following methods: storing only N points starting from the first point; storing only N points attr in order of the smallest value in the azimuth range; storing only N points starting from the last point; storing only N points attr in order of the largest value in the azimuth range; and / or storing only N points attr by downsampling (e.g., sampling and storing points within the azimuth range that exist in detail based on a specific angle); etc.

[0252] The attr_quant value, attr_N value, and point selection method can be transmitted to the decoder. Alternatively, they can be set to arbitrary values ​​and used within the encoder / decoder.

[0253] When using an integrated reference frame, the reference frame is generated based on the coordinate system of the attribute information. When using a reference frame in Predicted Tree Geometry Intercompression, you can configure how to specify reference points for each segmented area (within an octree node or azimuth range). You can also separately specify how many points (geom_N) to use for the geometry. The number of points (geom_N) can have a value of 1.

[0254] With respect to point selection, the method device according to the embodiments may select only up to geom_N points from the first point; select only up to geom_N points in order of having the smallest value in the azimuth range; select only up to geom_N points from the last point; select only up to geom_N points in order of having the largest value in the azimuth range; and / or select only up to geom_N points by downsampling (e.g., selecting points within the azimuth by sampling points that exist in detail based on a specific angle); etc.

[0255] If the attribute information and geometry have different coordinate systems, coordinate system conversion can be performed only for selected points. This can be done within the decoder / encoder. Therefore, inter-prediction can be performed using the reference frame buffer with minimal memory and minimal transformation operations.

[0256] FIG. 12 shows an encoding device according to embodiments.

[0257] FIG. 12 illustrates PCC data encoding, and each component may correspond to hardware, software, a processor, and / or a combination thereof. PCC data is fed into the input of the encoder and encoded so that a geometry information bitstream and an attribute information bitstream can be output.

[0258] The encoding device according to the embodiments of FIG. 12 can perform operations described in FIG. 1, such as the transmitting device (10000), the acquisition unit (10001), the encoder (10002), the transmitter (10003), FIG. 2, the acquisition (20000), the encoding (20001), the transmission (20002), FIG. 3, FIG. 8, the encoder, FIG. 10, each device, FIG. 14 to 15, the frame generation, FIG. 17 to 23, the bitstream generation, FIG. 24, the encoding, etc. The device of FIG. 12 is composed of memory and a processor, and the processor can perform the functions of each unit of FIG. 12.

[0259] The data input unit can read and configure the received data (e.g., ply, configuration file, etc.) input by the acquisition unit.

[0260] The coordinate system transformation unit can perform coordinate system changes, such as changing the xyz axes of the coordinate system or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0261] The geometry information conversion quantization processing unit can adjust the scale by multiplying the geometry position x, y, and z values ​​of the point cloud points by the scale (geometry quantization value) according to the scale setting.

[0262] The spatial partition can be divided into tiles or slices for area-based access or parallel processing of content.

[0263] The voxelization processing unit can round the geometry position values ​​of scaled points to integers.

[0264] The geometry information intra prediction unit can apply geometry intra coding. Intra coding methods may include octree coding, predictive tree coding, trisoup coding methods, etc.

[0265] The LPU / PU splitting unit can divide the points divided into slices into LPUs / PUs to support inter-prediction when the frame is a P-frame, and find and assign motion vectors corresponding to the split areas.

[0266] The motion compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0267] The geometry information inter-prediction unit can perform octree-based inter-coding, predictive-tree-based inter-coding, and trisoup-based inter-coding based on the difference between the predicted value between the current frame and the reference frame with motion compensation applied.

[0268] The geometry entropy encoding unit can entropy code the result of the geometry information intra / inter prediction unit.

[0269] The color conversion processing unit can convert attribute types, such as changing RGB colors to YUV.

[0270] The color recalculation unit can predict an attribute value suitable for the changed location when the geometry is scaled and the location information (geometry data) value is changed.

[0271] The attribute information intra prediction unit can perform attribute information intra coding. The intra coding method may include a Predicting Transform coding method, a Lift Transform coding method, a RAHT coding method, etc.

[0272] The attribute information inter-prediction unit can generate residual values ​​based on the difference in attribute prediction values ​​between the current frame and the reference frame with motion compensation applied.

[0273] Attribute information entropy encoding can entropy code the results of the attribute information intra / inter prediction section.

[0274] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0275] The operation of the reference frame generation unit according to the embodiments is as follows:

[0276] The reference frame creation unit can store the reference frame in the reference frame buffer.

[0277] The reference frame generation unit can store a reference frame for geometry coding. Refer to the description of step 2) above.

[0278] The reference frame generation unit can store a reference frame for attribute coding. Refer to the description of step 3) above.

[0279] The reference frame generation unit can use a single reference frame when the coordinate system used for geometry and attribute coding is the same.

[0280] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be saved through the integrated frame generation process described in step 4) above.

[0281] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be transmitted in accordance with the process of transmitting geometry information from the integrated frame described in step 4) above.

[0282] FIG. 13 shows a decoding device according to embodiments.

[0283] FIG. 13 is a PCC data decoder, and each component may correspond to hardware, software, a processor, and / or a combination thereof. An encoded geometry information bitstream and an attribute information bitstream are input to the decoder, and PCC data that has been decoded and restored can be output.

[0284] The decoding device of FIG. 13 can perform operations described in FIG. 1, such as the receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007), FIG. 2 decoding (20003), rendering (20004), FIG. 7, FIG. 9, FIG. 13 decoder, FIG. 10 device, FIG. 14 to FIG. 15 reference frame generation, FIG. 17 to FIG. 23 bitstream parsing, FIG. 25 decoding, etc. The device of FIG. 13 is composed of memory and a processor, and the processor can perform the functions of each unit of FIG. 13.

[0285] The geometry entropy decoder can entropy decode a geometry bitstream.

[0286] The geometry information intra prediction restoration unit can restore predicted values ​​predicted by geometry intra coding. The intra coding method may include octree coding, predictive-tree coding, trisoup coding, etc.

[0287] The LPU / PU splitting unit can split the reference frame into LPU / PU by restoring the signaled area value for the LPU / PU splitting indication to support inter-prediction when the frame is a P-frame.

[0288] The motion compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0289] The geometry information inter-prediction restoration unit can restore predicted values ​​predicted by geometry inter-coding. Inter-coding methods may include octree-based inter-coding, predictive-tree-based inter-coding, trisoup-based inter-coding methods, etc.

[0290] The coordinate system inverse transformation unit can restore the changed xyz axes or inversely transform the transformed coordinate system into an xyz orthogonal coordinate system.

[0291] The geometry information conversion inverse quantization processing unit can restore the signaled scale (geometry quantization value) and apply it to the geometry position x, y, and z values ​​of the restored point to restore it.

[0292] Attribute residual information entropy decoding can entropy decode the attribute bitstream.

[0293] The attribute information intra prediction restoration unit can restore predicted values ​​predicted by attribute information intra coding. Intra coding methods may include Predicting Transform coding methods, Lift Transform coding methods, RAHT coding methods, etc.

[0294] The attribute information inter-prediction restoration unit can restore predicted values ​​predicted by attribute information inter-coding.

[0295] The color inverse conversion processing unit can restore the converted attribute to RGB color.

[0296] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0297] The reference frame generation unit according to the embodiments can perform the following operations.

[0298] The reference frame creation unit can store the reference frame in the reference frame buffer.

[0299] The reference frame generation unit can store a reference frame for geometry coding. It can be stored through the process described in step 2) above.

[0300] The reference frame generation unit can store a reference frame for attribute coding. It can be stored through the process described in step 3) above.

[0301] The reference frame generation unit can use a single reference frame when the coordinate system used for geometry and attribute coding is the same.

[0302] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be saved through the integrated frame generation process described in step 4) above.

[0303] In the reference frame generation section, if the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be transmitted in accordance with the process of transmitting geometry information from the integrated frame described in step 4) above.

[0304] Detailed flowcharts of the reference frame generation method in the encoder and decoder are as shown in FIGS. 14 to 16.

[0305] FIG. 14 illustrates a method for generating a reference frame according to embodiments.

[0306] When the encoder and / or decoder requests the storage of a reference frame, determine whether the geometry coding coordinate system and the attribute coding coordinate system are the same or different. If they are the same, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, N points per zone (or region) can be sampled by a specified method based on octree nodes and stored in the frame buffer. If the attribute coding coordinate system is not an orthogonal coordinate system but an angular coordinate system, zones (or regions) can be created by dividing angles by quantization values ​​based on azimuths, and N points per zone can be sampled by a specified method and stored in the frame buffer.

[0307] If the geometry coding coordinate system and the attribute coding coordinate system are not identical, it is determined whether to use a unified frame for the geometry and attributes. If a unified frame is used, as described above, depending on whether the attribute coding coordinate system is an orthogonal coordinate system or an angular coordinate system, a zone can be established based on an octree node or an azimuth, and N points within the zone can be stored in a reference frame according to a specified method.

[0308] If a unified frame is not used, check whether the geometry coding coordinate system is an orthogonal coordinate system. If the geometry coding coordinate system is an orthogonal coordinate system, create zones based on octree nodes and sample N points per zone according to the specified method and store them in the frame buffer.

[0309] If the geometry coding coordinate system is not an orthogonal coordinate system but an angular coordinate system, zones are created based on the azimuth angle, and N points per zone are sampled according to the specified method and stored in the frame buffer.

[0310] FIG. 15 illustrates a method for generating a reference frame according to embodiments.

[0311] When the encoder and / or decoder requests a reference point from a reference frame for geometry coding, check whether the geometry coding coordinate system and the attribute coding coordinate system are the same. If the two coordinate systems are the same, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, the region-specific geometry reference point (geom_N) and the reference point can be selected and passed to the encoder and / or decoder based on the octree node and the specified method. If the attribute coding coordinate system is an angular coordinate system rather than an orthogonal coordinate system, the region-specific point (geom_N) generated by dividing the angle by the quantization value based on the azimuth and the reference point can be selected and passed to the encoder and / or decoder, and the reference point can be stored in the reference frame buffer.

[0312] If the geometry coding coordinate system and the attribute coding coordinate system are not identical, it is possible to determine whether to use a unified frame. If a unified frame is used, depending on whether the attribute coding coordinate system is an orthogonal coordinate system or an angular coordinate system, one of the aforementioned octree node-based reference point selection or the aforementioned azimuth-based reference point selection can be performed, and the reference point can be stored in the reference frame buffer.

[0313] If the integrated frame is not used, check whether the geometry coding coordinate system is a Cartesian coordinate system. If the geometry coding coordinate system is a Cartesian coordinate system, perform the aforementioned octree node-based reference point selection. If the geometry coding coordinate system is not a Cartesian coordinate system but an angular coordinate system, perform the aforementioned azimuth-based reference point selection and store the reference point in the reference frame buffer.

[0314] When the geometry coding coordinate system and the attribute coding coordinate system are different from each other, the coordinate system of the points selected based on the geometry coding coordinate system can be changed to store reference points in the reference frame buffer.

[0315] FIG. 16 illustrates a method for generating a reference frame according to embodiments.

[0316] When the encoder and / or decoder requests a reference point from a reference frame for attribute coding, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, select a region-specific geometry reference point (N) generated based on an octree node and a reference point according to a specified method, and store the reference point in the reference frame buffer.

[0317] If the attribute coding coordinate system is not an orthogonal coordinate system, check whether the attribute coding coordinate system is an angular coordinate system. If the attribute coding coordinate system is an angular coordinate system, select a reference point according to a specified method and the region-specific point (N) generated as a quantized value based on the azimuth, and store the reference point in the reference frame buffer.

[0318] FIG. 17 shows a bitstream according to embodiments.

[0319] An encoding method / device according to embodiments (a transmitting device (10000) in FIG. 1, an acquisition unit (10001), an encoder (10002), a transmitter (10003), an acquisition (20000) in FIG. 2, an encoding (20001), a transmission (20002), encoders in FIG. 3, FIG. 8, FIG. 12, each device in FIG. 10, frame generation in FIG. 14 to 15, bitstream generation in FIG. 17 to 23, encoding in FIG. 24, etc.) encodes point cloud data, generates related parameter information, and the encoded point

[0320] A decoding method / device according to embodiments (FIG. 1 receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007), FIG. 2 decoding (20003), rendering (20004), FIG. 7, FIG. 9, FIG. 13 decoder, FIG. 10 each device, FIG. 14 to FIG. 15 reference frame generation, FIG. 25 decoding, etc.) can parse a bitstream and decode point cloud data based on parameter set information within the bitstream (FIG. 18 to FIG. 23, etc.).

[0321] Each abbreviation referring to information within a bitstream means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning: SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometry bitstream = geometry slice header+ [geometry PU header + Geometry PU data] | geometry slice data, Attr: Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data.

[0322] The embodiments may signal by adding reference frame buffer-related option information to the SPS, GPS, and / or TPS. The aforementioned reference frame buffer-related option information may be signaled by adding it to the TPS, or to the geometry header and / or attribute header for each slice. Tile or slice is provided to allow the point cloud to be processed by dividing it into regions. When dividing by regions, different neighbor point set generation options can be set for each region to provide a choice that offers low complexity but slightly lower reliability of the result, or conversely, high complexity but high reliability. This can be configured differently depending on the receiver's capacity. Therefore, when point cloud data is divided into tiles, different options can be applied to each tile. When point cloud data is divided into slices, different options can be applied to each slice.

[0323] In the following, parameter information contained within the bitstream is explained with reference to each drawing.

[0324] FIG. 18 shows a set of sequence parameters (SPS) in a bitstream according to embodiments.

[0325] The embodiments can signal by adding reference frame buffer related option information to the Sequence Parameter Set to support efficient inter-predictive signaling and inter-predictive compression.

[0326] Profile (profile_idc): Represents the profile that the bitstream adheres to.

[0327] Profile compatibility flags (profile_compatibility_flags): If 1, it indicates that the bitstream follows the profile where profile_idc is j.

[0328] Number of attribute sets (sps_num_attribute_sets): Represents the number of coded attributes in the bitstream. The value of sps_num_attribute_sets can be in the range of 0 to 63.

[0329] Attribute Dimension(attribute_dimension[i]): Represents the number of components of the i-th attribute.

[0330] Attribute instance ID (attribute_instance_id[i]): Represents the instance ID of the i-th attribute.

[0331] Integrated_reference_frame_buffer_usage_flag: Indicates whether to use the integrated reference frame buffer for the sequence.

[0332] FIG. 19 shows a set of geometry parameters (GPS) in a bitstream according to embodiments.

[0333] The embodiments can signal by adding reference frame buffer related option information for the geometry information encoding / decoding process to the geometry parameter set for efficient inter-predictive signaling.

[0334] Geometry parameter set ID (gps_geom_parameter_set_id): Represents a GPS identifier that other syntax elements can reference. The value of gps_seq_parameter_set_id can be in the range of 0 to 15.

[0335] Sequence parameter set ID (gps_seq_parameter_set_id): Represents the value of sps_seq_parameter_set_id for the active SPS. The value of gps_seq_parameter_set_id can be in the range of 0 to 15.

[0336] Geometry tree type (geom_tree_type): If geom_tree_type is 0, it indicates that location information is coded using an octree (accupancy tree). If geom_tree_type is 1, it indicates that location information is coded using a prediction tree.

[0337] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0338] Geometry quantization (geom_quant): Represents a value (quantization) for dividing the azimuth applied to the frame into arbitrary regions.

[0339] Geometry count (geom_N): Represents the maximum number of points for storing points per zone divided by the azimuth applied to the frame.

[0340] Geometry Sampling Method Type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the frame. For example, if this value is 0, only geom_N points are selected and stored from the first point; if 1, only geom_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only geom_N points are selected and stored from the last point; if 3, only geom_N points are selected and stored in order of the largest value in the azimuth range; if 4, points are selected and stored by downsampling; the sampling method type information can be signaled as follows.

[0341] Geometry Extraction Count (geom_extract_N): Applied to the frame; when using an integrated reference frame, indicates the maximum number of reference points to take from the reference frame and use.

[0342] Geometry extraction sampling method type (geom_extract_sampling_method_type): Applied to the frame, and when using an integrated reference frame, indicates the method of selecting geom_N points for each region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; the point extraction sampling method type can be signaled as follows.

[0343] FIG. 20 shows a set of attribute parameters (APS) in a bitstream according to embodiments.

[0344] The embodiments can signal by adding parameter information for the reference frame buffer to the attribute parameter set for efficient reference frame buffer support.

[0345] Attribute parameter set ID (aps_attr_parameter_set_id): Represents an identifier for the APS that other syntax elements can reference. The value of aps_attr_parameter_set_id can be in the range of 0 to 15.

[0346] Sequence parameter set ID (aps_seq_parameter_set_id): Represents the value of sps_seq_parameter_set_id for the active SPS. The value of aps_seq_parameter_set_id can be in the range of 0 to 15.

[0347] Lifting nearest neighbor count (lifting_num_pred_nearest_neighbours): Represents the maximum number of nearest neighbors to use for prediction.

[0348] Maximum number of lifting direct predictors (lifting_max_num_direct_predictors): Indicates the maximum number of predictors to use for direct prediction. The value of lifting_max_num_direct_predictors can range from 0 to lifting_num_pred_nearest_neighbours. The values ​​of the variable MaxNumPredictors used in the decoding process are as follows:

[0349] MaxNumPredictors = MaxNumDirectPredictors + 1

[0350] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0351] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the frame.

[0352] Attribute Tree Node Level (attr_occtree_node_level): Represents the octree depth level value when using an orthogonal coordinate system applied to the frame. This value is used to define intervals based on the octree (accupancy tree).

[0353] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the frame into arbitrary regions.

[0354] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the frame.

[0355] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points for each region divided by the azimuth applied to the frame. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the type of method for sampling attributes can be signaled.

[0356] FIG. 21 shows a set of tile parameters (TPS) in a bitstream according to embodiments.

[0357] The embodiments can signal by adding parameter information for the reference frame buffer to the Tile Parameter Set for efficient reference frame buffer support.

[0358] Count of tiles (num_tiles): Represents the number of tiles signaled for the bitstream. If this value does not exist, num_tiles is inferred to be 0.

[0359] Tile bounding box offset x(tile_bounding_box_offset_x[i]): Represents the x-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_x[0] is inferred as sps_bounding_box_offset_x.

[0360] Tile bounding box offset y(tile_bounding_box_offset_y[i]): Represents the y-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_y[0] is inferred as sps_bounding_box_offset_y.

[0361] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for tiles.

[0362] Geometry Quantization (geom_quant): Represents a value for dividing the azimuth applied to the tile into arbitrary zones.

[0363] Geometry Count (geom_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0364] Geometry sampling method type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the tile. For example, if this value is 0, only geom_N points are selected and stored from the first point; if 1, only geom_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only geom_N points are selected and stored from the last point; if 3, only geom_N points are selected and stored in order of the largest value in the azimuth range; if 4, points are selected and stored by downsampling; and so on, the type of geometry point sampling method can be signaled.

[0365] Geometry Extraction Count (geom_extract_N): Applied to tiles, and indicates the maximum number of reference points to take from the reference frame when using an integrated reference frame.

[0366] Geometry extract sampling method type (geom_extract_sampling_method_type): Applied to tiles, and when using a unified reference frame, indicates a method for selecting geom_N points per region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; and so on.

[0367] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0368] Attribute Octree Node Level (attr_occtree_node_level): Represents the octree (accupancy tree) depth level value when using the Cartesian coordinate system applied to the tile. It is used to define intervals based on the octree (accupancy tree).

[0369] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the tile into arbitrary zones.

[0370] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0371] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points per zone divided by the azimuth applied to the tile. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the attribute sampling method type can be signaled.

[0372] FIG. 22 shows a geometry slice header (GSH) within a bitstream according to embodiments.

[0373] The embodiments can signal LPU / PU-specific geometry prediction tree coding option information for the geometry information encoding / decoding process by adding it to the geometry slice header.

[0374] Geometry parameter set ID (gsh_geometry_parameter_set_id): Represents the gps_geom_parameter_set_id value of the active GPS.

[0375] Tile ID (gsh_tile_id): Represents the tile ID value referenced by GSH.

[0376] Slice ID (gsh_slice_id): Represents the slice header that other syntax elements will reference.

[0377] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the slice.

[0378] Geometry Quantization (geom_quant): Represents a value for dividing the azimuth applied to the slice into arbitrary regions.

[0379] Geometry count (geom_N): Represents the maximum number of points for storing points per zone divided by the azimuth applied to the slice.

[0380] Geometry sampling method type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the slice. For example, if this value is 0, only geom_N points are selected and stored from the first point; 1 = only geom_N points are selected and stored in order of the smallest value in the azimuth range; 2 = only geom_N points are selected and stored from the last point; 3 = only geom_N points are selected and stored in order of the largest value in the azimuth range; 4 = downsampled and selected and stored; and so on, the geometry sampling method type can be signaled.

[0381] Geometry Extraction Count (geom_extract_N): Applied to slices, and indicates the maximum number value to take and use from the reference frame when using an integrated reference frame.

[0382] Geometry Extraction Sampling Method Type (geom_extract_sampling_method_type): Applied to slices, and when using a unified reference frame, indicates a method for selecting geom_N points per region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; and so on, the geometry extraction sampling method type can be signaled.

[0383] FIG. 23 shows an attribute slice header (ASH) within a bitstream according to embodiments.

[0384] The embodiments can signal by adding parameter information for reflectance attribute coding to the Attribute Slice Header to support efficient reflectance attribute compression.

[0385] Attribute parameter set ID (abh_attr_parameter_set_id): Represents the aps_attr_parameter_set_id value of the active APS.

[0386] Attribute SPS attribute index (abh_attr_sps_attr_idx): Represents the attribute set of the active SPS. The value of abh_attr_sps_attr_idx can be in the range of 0 to sps_num_attribute_sets for the active SPS.

[0387] Attribute Geometry Slice ID (ash_attr_geom_slice_id): Represents the slice ID (gsh_slice_id) value of the active Geometry Slice Header.

[0388] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0389] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0390] Attribute Octree Node Level (attr_occtree_node_level): Represents the octree (accupancy tree) depth level value when using the Cartesian coordinate system applied to the slice. It is used to define intervals based on the octree (accupancy tree).

[0391] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the slice into arbitrary regions.

[0392] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the slice.

[0393] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points for each region divided by the azimuth applied to the slice. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the attribute sampling method type can be signaled.

[0394] FIG. 24 illustrates a encoding method according to embodiments.

[0395] The encoding method according to the embodiments may include the step of encoding point cloud data (S2400); and / or the step of transmitting a bitstream containing point cloud data (S2410).

[0396] Point cloud data may be represented based on at least one of radius, azimuth, and laser index. The step of encoding point cloud data (S2400) includes: a step of encoding geometry data of point cloud data based on inter prediction, and a step of encoding attribute data of point cloud data based on inter prediction, wherein the step of encoding geometry data and the step of encoding attribute data may each further include a step of generating a reference frame.

[0397] The step of generating a reference frame includes generating a reference frame for decoding geometry data based on a prediction tree for point cloud data, wherein reference points in the reference frame are aligned based on azimuth angles, and at least one reference point for a zone is stored in a buffer for the reference frame based on a zone generated based on a quantization value in the reference frame, and if there are multiple reference points within a zone, a first number of points among the multiple reference points may be stored in a buffer based on at least one of an aligned order or an azimuth angle range.

[0398] The step of generating a reference frame includes: generating a reference frame for decoding attribute data when the coordinate system for attribute data is different from the coordinate system for geometry data; when the coordinate system for attribute data is the same as the coordinate system for geometry data, attribute data is decoded based on the reference frame for geometry data; when the coordinate system for attribute data is an orthogonal coordinate system, a first number of attribute data is generated as a reference frame based on an octree for point cloud data; when the coordinate system for attribute data is an angular coordinate system, a first number of attribute data is generated as a reference frame based on an azimuth angle; and the first number of attribute data may be selected based on at least one of an aligned order or an azimuth angle.

[0399] The embodiments may further include a computer-readable storage medium for storing a bitstream generated by the method according to FIG. 24.

[0400] The embodiments may further include a method comprising the steps of: acquiring a bitstream for point cloud data, the bitstream being generated based on the step of encoding the point cloud data; and transmitting data including the bitstream.

[0401] FIG. 25 illustrates a decoding method according to embodiments.

[0402] The decoding method according to the embodiments may include the step of receiving a bitstream containing point cloud data (S2500); and / or the step of decoding the point cloud data (S2510). The decoding method of FIG. 25 may follow the reverse process of the encoding method of FIG. 24.

[0403] Referring together to the LiDAR data characteristics of FIG. 11 and the decoder configuration of FIG. 13, the point cloud data is represented based on at least one of radius, azimuth, and laser index, and the step of decoding the point cloud data (S2510) includes: the step of decoding the geometry data of the point cloud data based on inter-prediction, and the step of decoding the attribute data of the point cloud data based on inter-prediction, and the step of decoding the geometry data and the step of decoding the attribute data may each further include the step of generating a reference frame.

[0404] Regarding the generation of a reference frame for geometry coding based on a prediction tree, the step of generating a reference frame includes generating a reference frame for decoding geometry data based on a prediction tree for point cloud data, wherein reference points in the reference frame are aligned based on azimuth angles, and at least one reference point for a region is stored in a buffer for the reference frame based on a region generated based on a quantization value in the reference frame, and if there are multiple reference points within a region, a first number of points among the multiple reference points may be stored in a buffer based on at least one of an aligned order or an azimuth angle range.

[0405] Regarding the generation of a reference frame for information coding, the step of generating a reference frame includes: generating a reference frame for decoding the attribute data when the coordinate system for the attribute data is different from the coordinate system for the geometry data; when the coordinate system for the attribute data is the same as the coordinate system for the geometry data, the attribute data is decoded based on the reference frame for the geometry data; when the coordinate system for the attribute data is an orthogonal coordinate system, a first number of attribute data is generated as a reference frame based on the octree for the point cloud data; when the coordinate system for the attribute data is an angular coordinate system, a first number of attribute data is generated as a reference frame based on the azimuth angle; and the first number of attribute data may be selected based on at least one of an aligned order or an azimuth angle.

[0406] Regarding the generation of a geometry / attribute integrated reference frame, the step of generating the reference frame includes: generating a first number of attribute data as a reference frame based on an octree when the coordinate system for geometry data and the coordinate system for attribute data are orthogonal coordinate systems, and generating a first number of attribute data as a reference frame based on an azimuth when the coordinate system for geometry data and the coordinate system for attribute data are angular coordinate systems, wherein the first number of attribute data may be selected based on at least one of an aligned order or an azimuth.

[0407] In relation to the SPS, GPS, and APS of FIGS. 17 to 20, the bitstream includes at least one of a sequence parameter set, a geometry parameter set, an attribute parameter set, a tile parameter set, a geometry data header, or an attribute data header; the sequence parameter set includes information indicating whether an integrated reference frame buffer is used; the geometry parameter set includes at least one of information indicating whether an integrated reference frame buffer is used, a quantization value related to an azimuth for the reference frame, information on the number of points related to a zone for the azimuth, information indicating a method for selecting points within a zone for the azimuth, information on the number of reference points related to the integrated reference frame, or a method for selecting reference points related to the integrated reference frame; and the attribute parameter set may include at least one of information indicating whether an integrated reference frame buffer is used, information on the maximum number of points within a zone related to an azimuth for the reference frame, information on the depth level of an octree related to the reference frame, a quantization value related to the reference frame, or a method for selecting points within a zone related to an azimuth.

[0408] Regarding the TPS of FIG. 21, the tile parameter set may include at least one of information indicating whether an integrated reference frame buffer for the tile is used, a quantization value related to the azimuth applied to the tile, information on the maximum number of points within a zone related to the azimuth applied to the tile, a method for selecting points within a zone related to the azimuth, information on the number of reference points related to the integrated reference frame applied to the tile, a method for selecting reference points within the integrated reference frame applied to the tile, information on the number of attribute data within a zone related to the azimuth applied to the tile, octree depth level information applied to the tile, azimuth quantization information of attribute data applied to the tile, or a method for selecting attribute data within a zone related to the azimuth applied to the tile.

[0409] With respect to the GSH and ASH of FIGS. 22 and 23, the geometry data header may include at least one of information indicating whether an integrated reference frame buffer for the slice is used, a quantization value related to the azimuth angle applied to the slice, information on the maximum number of points within a zone related to the azimuth angle applied to the slice, a method for selecting points within a zone related to the azimuth angle, information on the number of reference points related to an integrated reference frame applied to the slice, or a method for selecting reference points within an integrated reference frame applied to the slice, and the attribute data header may include at least one of information indicating whether an integrated reference frame buffer for the slice is used, information on the maximum number of attribute data within a zone related to the azimuth angle applied to the slice, octree depth level information applied to the slice, a quantization value for attribute data applied to the slice, or a method for selecting attribute data within a zone.

[0410] The decoding method can be performed by a decoding device as shown in FIG. 13. 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: receive a bitstream containing point cloud data; and decode the point cloud data.

[0411] The method / device according to the embodiments provides the following technical effects.

[0412] When storing reference frames in a buffer for inter-prediction, if geometry and attribute information use different coordinate systems, information from two different coordinate systems may be stored for a single reference frame. Storing only one of them increases complexity by incurring computational load because the decoder must perform additional coordinate transformations; conversely, storing both increases memory usage, which can burden the decoder. When Bi-Prediction is applied, more computation and memory usage may be required. Furthermore, global or local motion may be applied only to geometry or only to attributes. Such cases can also increase the number of reference frames.

[0413] The embodiments have the effect of solving the aforementioned technical problems through a reference frame buffer management method for applying predictive compression technology through reference frames to a point cloud having multiple frames captured by a spinning lidar.

[0414] Therefore, the embodiments have the effect of providing a method to minimize computational load and memory usage. Since the precision of the reference frame can be adjusted, in the case of noisy data, the effect of removing noise is also provided, thereby increasing the compression efficiency for inter-prediction of point cloud data. Accordingly, the transmitting method / device according to the embodiments can efficiently compress point cloud data and transmit the data, and by transmitting signaling information for this purpose, the receiving method / device according to the embodiments can also efficiently decode / restore the point cloud data.

[0415] 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.

[0416] 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 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.

[0417] The encoding method / device according to the embodiments may include and perform operations such as a transmitting device (10000) of FIG. 1, an acquisition unit (10001), an encoder (10002), a transmitter (10003), acquisition (20000) of FIG. 2, encoding (20001), transmission (20002), encoders of FIG. 3, FIG. 8, FIG. 12, each device of FIG. 10, reference frame generation of FIG. 14 to FIG. 15, bitstream generation of FIG. 17 to FIG. 23, encoding of FIG. 24, etc.

[0418] The decoding method / device according to the embodiments may include and perform operations such as a receiving device (10004) of FIG. 1, a receiving unit (10005), a decoder (10006), a renderer (10007), decoding (20003) of FIG. 2, rendering (20004), decoders of FIG. 7, FIG. 9, FIG. 13, each device of FIG. 10, reference frame generation of FIG. 14 to FIG. 15, bitstream parsing of FIG. 17 to FIG. 23, decoding of FIG. 25, etc.

[0419] The encoding / decoding device according to the embodiments of each drawing may be composed of memory and a processor. The components of each drawing may correspond to hardware, software, a processor, and / or a combination thereof. The encoding / decoding method / device according to the embodiments may be referred to as the method / device for short.

[0420] The method / device according to the embodiments may include and perform reference frame buffer management for 3D contents captured by spinning LiDAR.

[0421] The embodiments include a reference frame buffer management method for inter-prediction compression of Geometry-based Point Cloud Compression (G-PCC) for point cloud frames captured by Spinning LiDAR equipment. For example, it may include a reference frame buffer management method for inter-prediction of geometry and attributes and / or a method for signaling parameter information related to reference frame buffer management for inter-prediction.

[0422] The embodiments relate to a method for increasing the compression efficiency of G-PCC Geometry-based Point Cloud Compression () for 3D point cloud data compression.

[0423] A point cloud consists of a set of points, and each point can have geometry information and attribute information. Geometry information is 3D position (XYZ) information, and attribute information is color (RGB, YUV, etc.) or / and reflection values.

[0424] The G-PCC encoding process can divide the point cloud into tiles according to region and divide each tile into slices for parallel processing. It can be composed of a process of compressing geometry on a slice-by-slice basis and compressing attribute information based on reconstructed geometry (decoded geometry) with location information changed through compression.

[0425] The G-PCC decoding process can be composed of receiving a geometry bitstream and an attribute bitstream in the form of encoded slice units, decoding the geometry, and decoding attribute information based on the geometry reconstructed through the decoding process.

[0426] For geometry information compression, octree (or accupancy tree) based, predictive tree based, or trisoup based compression techniques can be used.

[0427] For attribute information compression, predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used.

[0428] The embodiments include a reference frame buffer management method for efficiently applying compression through inter-prediction of content captured by LiDAR equipment.

[0429] FIG. 11 shows a spinning lidar model according to embodiments.

[0430] The method and apparatus according to the embodiments can extract depth information through lidar equipment utilizing a radar system that measures the position coordinates of a reflector by emitting a laser pulse and measuring the time it takes for the reflected object to return, for the purpose of capturing point cloud content. The point cloud content generated by the lidar equipment may consist of multiple frames, or multiple frames may be integrated into a single content.

[0431] Lidar at different elevations θ(i) i=1..N It is composed of N lasers (N=16, 32, 64, etc.), and the lasers can capture point cloud data while spinning along an azimuth Φ with respect to the Z-axis (see Fig. 11). This type is called a spinning LiDAR model, and the content captured by the spinning LiDAR model has angular characteristics.

[0432] Laser i hits object M, and the position of M can be estimated as (x, y, z) in the Cartesian coordinate system (see Fig. 11). Due to the fixed position of the laser sensors, their straight-line movement characteristics, and the fact that the sensors rotate at a certain azimuth angle, the position of M can have characteristics that allow the rules between points to be induced favorably for compression when expressed as (r, Φ, i) rather than (x, y, z) in the Cartesian coordinate system.

[0433] Therefore, by utilizing these characteristics, for data captured by spinning LiDAR equipment, compression efficiency can be higher by applying an angular mode during the geometry encoding / decoding process. An angular mode is a method of compressing using (r, Φ, i) instead of (x, y, z).

[0434] Geometry information is encoded and decoded, and geometry location information is reconstructed to compress attribute information based on that location. At this stage, attribute information can selectively use either a Cartesian coordinate system or an angular mode. Generally, the Cartesian coordinate system is primarily used.

[0435] Therefore, when storing reference frames in a buffer for inter-prediction, if different coordinate systems are used for geometry and attribute information, it may result in storing information from two different coordinate systems for a single reference frame. If only one is stored, the decoder must perform additional coordinate transformations, which increases computational load and complexity; if both types of information are stored, memory usage increases, potentially burdening the decoder. Consequently, the application of Bi-Prediction may require more computation and memory usage.

[0436] Additionally, there may be cases where global / local motion is applied only to geometry or only to attributes. Such cases can also increase the number of reference frames.

[0437] The embodiments include a reference frame buffer management method for applying predictive compression techniques through reference frames to a point cloud having multiple frames captured by a spinning lidar. The embodiments have the effect of minimizing computational load and minimizing memory usage.

[0438] The encoding / decoding method according to the embodiments includes the following reference frame buffer management method. For example, it may include 1) a reference frame buffer configuration step, 2) a reference frame generation step for encoding or decoding prediction tree-based geometry (or may be referred to as geometry data), 3) a reference frame generation step for encoding or decoding attribute information (or may be referred to as attribute data), and / or 4) an integrated reference frame generation step for geometry data and / or attribute data. The operation of each step is described below.

[0439] 1) Reference Frame Buffer Configuration Step:

[0440] The method / device according to the embodiments uses a reference frame for inter prediction. Points in the reference frame that match the geometry with the point in the current frame are found, and predictions are performed from the selected reference points. The residual value can be entropy-coded based on the reference point having the smallest residual value among the differences between the predicted value and the value of the current point, and then transmitted to a decoder.

[0441] Therefore, the reference frame can be the previous frame by default, a cumulative frame, or, in the case of using bi-prediction, one or more frames. The current frame can be referred to as the first frame, and the reference frame as the second frame.

[0442] Points within a reference frame may have geometry information based on an orthogonal coordinate system or geometry information based on an angular coordinate system. They may also have both types of information.

[0443] 2) Step for generating reference frames for prediction tree-based geometry (or geometry data) encoding or decoding:

[0444] When encoding or decoding the geometry data of point cloud data based on a prediction tree for content (point cloud data) acquired by a spinning lidar, it may be efficient to convert the encoding / decoding mode to an angle mode to compress and restore it.

[0445] When predicting tree-based geometry inter-predictive coding, reference frames can be stored as reference point information based on azimuth Φ.

[0446] To divide the entire 360 ​​degrees into zones, the entire angle can be divided into arbitrary values ​​(geom_quant: quantization value). The quantization (quant) value can be transmitted from the encoder to the decoder as parameter signaling information, or it can be set to an arbitrary value and used within the encoder / decoder.

[0447] In each region divided by quantization values, a specific number (geom_N) of points can be stored. A single point can be stored, or multiple points can be stored. The number of points (geom_N) can be set optionally and can be transmitted from the encoder to the decoder as parameter signaling information. The decoder must also store the number of points (geom_N) so that both the encoder and decoder can perform inter-prediction using the same number of reference points. For example, in predictive geometry encoding / decoding, the number of points (geom_N) can be set to 1.

[0448] If the points are sorted based on the azimuth Φ value, the azimuth Φ is first divided by the quantization value (geom_quant) to obtain the region, and the points within the region can be stored. Various methods can be applied to store the number of points (geom_N) within the region.

[0449] For example, points up to geom_N can be stored starting from the first point in sorted order, points up to geom_N can be stored in order of smallest value within an azimuth range, points up to geom_N can be stored starting from the last point, points up to geom_N can be stored in order of largest value within an azimuth range, and / or points up to geom_N can be stored by downsampling (e.g., points within an azimuth can be sampled from points that exist in detail based on a specific angle, and the sampled points can be stored).

[0450] Information regarding the method of storing reference point information of a reference frame can be transmitted from the encoder to the decoder as parameter information. The encoder and decoder can perform inter-prediction using the same number of reference points.

[0451] 3) Step for creating a reference frame for encoding or decoding attribute information (or attribute data):

[0452] Cartesian coordinate system mode can be used when compressing (predictive transformation, lifting transformation, RAHT transformation) or restoring attribute information (attribute data). Angle mode can also be used for attribute information compression / restoration. Reference frames for inter-predicting attribute information can also store point information of the coordinate system used in the current frame.

[0453] When using a coordinate system different from geometry coding in attribute coding (encoding / decoding), a separate reference frame can be created.

[0454] When using the same coordinate system as geometry coding in attribute coding (encoding / decoding), the reference frame used in geometry can be used together.

[0455] The coordinate system information can be identified through parameter information, which is signaling information within the G-PCC bitstream, for example, the spherical coordinate system flag (spherical_coord_flag) within the geometry parameter set and / or attribute parameter set.

[0456] When reference frames are stored separately because the coordinate systems used for geometry coding and attribute coding are different, you can specify how many points to store for the attribute reference frame using the point count information (attr_N). If attr_N=-1, all coordinates can be stored. The attr_N points can be transmitted from the encoder to the decoder as parameter signaling information.

[0457] Various methods can be applied to save points to reference frames.

[0458] For example, if the angle mode is an orthogonal coordinate system, up to attr_N points can be stored on nodes at a specific depth level of the octree. The octree depth level (attr_occtree_node_level) is signaled from the encoder to the decoder as parameter signaling information.

[0459] For example, if the angle mode is an angle coordinate system, the Φ value can be divided by the quantization (attr_quant) value based on the angle coordinate system to define a region, and the points corresponding to that region can be selected and stored in a reference frame using the following method.

[0460] For example, you can store only up to attr_N points starting from the first point, store only up to attr_N points in order of the smallest value in the azimuth range, store only up to attr_N points starting from the last point, store only up to attr_N points in order of the largest value in the azimuth range, and / or store only up to attr_N points by downsampling (e.g., sampling and storing points within the azimuth range that exist in detail based on a specific angle).

[0461] Although the attribute information coordinates are in the Cartesian coordinate system, Cartesian coordinate system point values ​​can also be stored by changing only the container based on the angular coordinate system.

[0462] The storage method is also signaled to the decoder. Accordingly, the encoder and decoder can perform inter-prediction through the same reference point. In the case of an angular coordinate system, the attr_quant value can be transmitted to the decoder or set to an arbitrary value for use within the encoder and decoder.

[0463] 4) Step for generating an integrated reference frame for geometry data and / or attribute data

[0464] To minimize computational load and memory usage, a unified reference frame can be generated for geometry and attribute coding. Whether to use a unified reference frame can be signaled to the decoder. Whether the same coordinate system is used between geometry coding and attribute coding can be determined through parameter information, which is signaling information within the bitstream.

[0465] When using a unified reference frame, the coordinate system of the attribute information takes precedence. Since compression efficiency is better when attribute information references points with higher Φ values, the reference frame is created with the attribute information's coordinate system as the priority. In other words, geometry information and attribute information can be stored in the reference frame according to the coordinate system used during attribute compression. A unified reference frame can be referred to as an attribute reference frame. Geometry and attribute inter-predictive encoding / decoding can be performed using an attribute reference frame.

[0466] When geometry, information, and attributes all use an orthogonal coordinate system, up to attr_N points can be stored on nodes at a specific depth level of the octree.

[0467] The octree depth level (attr_occtree_node_level) and attr_N value are signaled to the decoder.

[0468] When using an angular coordinate system for geometry compression, or when using an angular coordinate system for both geometry and attribute compression, regions can be defined by dividing the Φ value by the attr_quant value based on the angular coordinate system, and attr_N points corresponding to those regions can be stored. The Attr_N value is signaled to the decoder.

[0469] N points attr can be selected and stored in a reference frame using the following methods: storing only N points starting from the first point; storing only N points attr in order of the smallest value in the azimuth range; storing only N points starting from the last point; storing only N points attr in order of the largest value in the azimuth range; and / or storing only N points attr by downsampling (e.g., sampling and storing points within the azimuth range that exist in detail based on a specific angle); etc.

[0470] The attr_quant value, attr_N value, and point selection method can be transmitted to the decoder. Alternatively, they can be set to arbitrary values ​​and used within the encoder / decoder.

[0471] When using an integrated reference frame, the reference frame is generated based on the coordinate system of the attribute information. When using a reference frame in Predicted Tree Geometry Intercompression, you can configure how to specify reference points for each segmented area (within an octree node or azimuth range). You can also separately specify how many points (geom_N) to use for the geometry. The number of points (geom_N) can have a value of 1.

[0472] With respect to point selection, the method device according to the embodiments may select only up to geom_N points from the first point; select only up to geom_N points in order of having the smallest value in the azimuth range; select only up to geom_N points from the last point; select only up to geom_N points in order of having the largest value in the azimuth range; and / or select only up to geom_N points by downsampling (e.g., selecting points within the azimuth by sampling points that exist in detail based on a specific angle); etc.

[0473] If the attribute information and geometry have different coordinate systems, coordinate system conversion can be performed only for selected points. This can be done within the decoder / encoder. Therefore, inter-prediction can be performed using the reference frame buffer with minimal memory and minimal transformation operations.

[0474] FIG. 12 shows an encoding device according to embodiments.

[0475] FIG. 12 illustrates PCC data encoding, and each component may correspond to hardware, software, a processor, and / or a combination thereof. PCC data is fed into the input of the encoder and encoded so that a geometry information bitstream and an attribute information bitstream can be output.

[0476] The encoding device according to the embodiments of FIG. 12 can perform operations described in FIG. 1, such as the transmitting device (10000), the acquisition unit (10001), the encoder (10002), the transmitter (10003), FIG. 2, the acquisition (20000), the encoding (20001), the transmission (20002), FIG. 3, FIG. 8, the encoder, FIG. 10, each device, FIG. 14 to 15, the frame generation, FIG. 17 to 23, the bitstream generation, FIG. 24, the encoding, etc. The device of FIG. 12 is composed of memory and a processor, and the processor can perform the functions of each unit of FIG. 12.

[0477] The data input unit can read and configure the received data (e.g., ply, configuration file, etc.) input by the acquisition unit.

[0478] The coordinate system transformation unit can perform coordinate system changes, such as changing the xyz axes of the coordinate system or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0479] The geometry information conversion quantization processing unit can adjust the scale by multiplying the geometry position x, y, and z values ​​of the point cloud points by the scale (geometry quantization value) according to the scale setting.

[0480] The spatial partition can be divided into tiles or slices for area-based access or parallel processing of content.

[0481] The voxelization processing unit can round the geometry position values ​​of scaled points to integers.

[0482] The geometry information intra prediction unit can apply geometry intra coding. Intra coding methods may include octree coding, predictive tree coding, trisoup coding methods, etc.

[0483] The LPU / PU splitting unit can divide the points divided into slices into LPUs / PUs to support inter-prediction when the frame is a P-frame, and find and assign motion vectors corresponding to the split areas.

[0484] The motion compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0485] The geometry information inter-prediction unit can perform octree-based inter-coding, predictive-tree-based inter-coding, and trisoup-based inter-coding based on the difference between the predicted value between the current frame and the reference frame with motion compensation applied.

[0486] The geometry entropy encoding unit can entropy code the result of the geometry information intra / inter prediction unit.

[0487] The color conversion processing unit can convert attribute types, such as changing RGB colors to YUV.

[0488] The color recalculation unit can predict an attribute value suitable for the changed location when the geometry is scaled and the location information (geometry data) value is changed.

[0489] The attribute information intra prediction unit can perform attribute information intra coding. The intra coding method may include a Predicting Transform coding method, a Lift Transform coding method, a RAHT coding method, etc.

[0490] The attribute information inter-prediction unit can generate residual values ​​based on the difference in attribute prediction values ​​between the current frame and the reference frame with motion compensation applied.

[0491] Attribute information entropy encoding can entropy code the results of the attribute information intra / inter prediction section.

[0492] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0493] The operation of the reference frame generation unit according to the embodiments is as follows:

[0494] The reference frame creation unit can store the reference frame in the reference frame buffer.

[0495] The reference frame generation unit can store a reference frame for geometry coding. Refer to the description of step 2) above.

[0496] The reference frame generation unit can store a reference frame for attribute coding. Refer to the description of step 3) above.

[0497] The reference frame generation unit can use a single reference frame when the coordinate system used for geometry and attribute coding is the same.

[0498] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be saved through the integrated frame generation process described in step 4) above.

[0499] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be transmitted in accordance with the process of transmitting geometry information from the integrated frame described in step 4) above.

[0500] FIG. 13 shows a decoding device according to embodiments.

[0501] FIG. 13 is a PCC data decoder, and each component may correspond to hardware, software, a processor, and / or a combination thereof. An encoded geometry information bitstream and an attribute information bitstream are input to the decoder, and PCC data that has been decoded and restored can be output.

[0502] The decoding device of FIG. 13 can perform operations described in FIG. 1, such as the receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007), FIG. 2 decoding (20003), rendering (20004), FIG. 7, FIG. 9, FIG. 13 decoder, FIG. 10 device, FIG. 14 to FIG. 15 reference frame generation, FIG. 17 to FIG. 23 bitstream parsing, FIG. 25 decoding, etc. The device of FIG. 13 is composed of memory and a processor, and the processor can perform the functions of each unit of FIG. 13.

[0503] The geometry entropy decoder can entropy decode a geometry bitstream.

[0504] The geometry information intra prediction restoration unit can restore predicted values ​​predicted by geometry intra coding. The intra coding method may include octree coding, predictive-tree coding, trisoup coding, etc.

[0505] The LPU / PU splitting unit can split the reference frame into LPU / PU by restoring the signaled area value for the LPU / PU splitting indication to support inter-prediction when the frame is a P-frame.

[0506] The motion compensation application unit can generate a predicted point cloud by applying motion vectors to the divided LPU / PU.

[0507] The geometry information inter-prediction restoration unit can restore predicted values ​​predicted by geometry inter-coding. Inter-coding methods may include octree-based inter-coding, predictive-tree-based inter-coding, trisoup-based inter-coding methods, etc.

[0508] The coordinate system inverse transformation unit can restore the changed xyz axes or inversely transform the transformed coordinate system into an xyz orthogonal coordinate system.

[0509] The geometry information conversion inverse quantization processing unit can restore the signaled scale (geometry quantization value) and apply it to the geometry position x, y, and z values ​​of the restored point to restore it.

[0510] Attribute residual information entropy decoding can entropy decode the attribute bitstream.

[0511] The attribute information intra prediction restoration unit can restore predicted values ​​predicted by attribute information intra coding. Intra coding methods may include Predicting Transform coding methods, Lift Transform coding methods, RAHT coding methods, etc.

[0512] The attribute information inter-prediction restoration unit can restore predicted values ​​predicted by attribute information inter-coding.

[0513] The color inverse conversion processing unit can restore the converted attribute to RGB color.

[0514] The reference frame generation unit can store the restored geometry and restored attribute information in the reference frame buffer and transfer the reference frame data from the reference frame to other modules.

[0515] The reference frame generation unit according to the embodiments can perform the following operations.

[0516] The reference frame creation unit can store the reference frame in the reference frame buffer.

[0517] The reference frame generation unit can store a reference frame for geometry coding. It can be stored through the process described in step 2) above.

[0518] The reference frame generation unit can store a reference frame for attribute coding. It can be stored through the process described in step 3) above.

[0519] The reference frame generation unit can use a single reference frame when the coordinate system used for geometry and attribute coding is the same.

[0520] If the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be saved through the integrated frame generation process described in step 4) above.

[0521] In the reference frame generation section, if the coordinate systems used for geometry and attribute coding are different and a single integrated reference frame is to be used, the reference frame can be transmitted in accordance with the process of transmitting geometry information from the integrated frame described in step 4) above.

[0522] Detailed flowcharts of the reference frame generation method in the encoder and decoder are as shown in FIGS. 14 to 16.

[0523] FIG. 14 illustrates a method for generating a reference frame according to embodiments.

[0524] When the encoder and / or decoder requests the storage of a reference frame, determine whether the geometry coding coordinate system and the attribute coding coordinate system are the same or different. If they are the same, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, N points per zone (or region) can be sampled by a specified method based on octree nodes and stored in the frame buffer. If the attribute coding coordinate system is not an orthogonal coordinate system but an angular coordinate system, zones (or regions) can be created by dividing angles by quantization values ​​based on azimuths, and N points per zone can be sampled by a specified method and stored in the frame buffer.

[0525] If the geometry coding coordinate system and the attribute coding coordinate system are not identical, it is determined whether to use a unified frame for the geometry and attributes. If a unified frame is used, as described above, depending on whether the attribute coding coordinate system is an orthogonal coordinate system or an angular coordinate system, a zone can be established based on an octree node or an azimuth, and N points within the zone can be stored in a reference frame according to a specified method.

[0526] If a unified frame is not used, check whether the geometry coding coordinate system is an orthogonal coordinate system. If the geometry coding coordinate system is an orthogonal coordinate system, create zones based on octree nodes and sample N points per zone according to the specified method and store them in the frame buffer.

[0527] If the geometry coding coordinate system is not an orthogonal coordinate system but an angular coordinate system, zones are created based on the azimuth angle, and N points per zone are sampled according to the specified method and stored in the frame buffer.

[0528] FIG. 15 illustrates a method for generating a reference frame according to embodiments.

[0529] When the encoder and / or decoder requests a reference point from a reference frame for geometry coding, check whether the geometry coding coordinate system and the attribute coding coordinate system are the same. If the two coordinate systems are the same, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, the region-specific geometry reference point (geom_N) and the reference point can be selected and passed to the encoder and / or decoder based on the octree node and the specified method. If the attribute coding coordinate system is an angular coordinate system rather than an orthogonal coordinate system, the region-specific point (geom_N) generated by dividing the angle by the quantization value based on the azimuth and the reference point can be selected and passed to the encoder and / or decoder, and the reference point can be stored in the reference frame buffer.

[0530] If the geometry coding coordinate system and the attribute coding coordinate system are not identical, it is possible to determine whether to use a unified frame. If a unified frame is used, depending on whether the attribute coding coordinate system is an orthogonal coordinate system or an angular coordinate system, one of the aforementioned octree node-based reference point selection or the aforementioned azimuth-based reference point selection can be performed, and the reference point can be stored in the reference frame buffer.

[0531] If the integrated frame is not used, check whether the geometry coding coordinate system is a Cartesian coordinate system. If the geometry coding coordinate system is a Cartesian coordinate system, perform the aforementioned octree node-based reference point selection. If the geometry coding coordinate system is not a Cartesian coordinate system but an angular coordinate system, perform the aforementioned azimuth-based reference point selection and store the reference point in the reference frame buffer.

[0532] When the geometry coding coordinate system and the attribute coding coordinate system are different from each other, the coordinate system of the points selected based on the geometry coding coordinate system can be changed to store reference points in the reference frame buffer.

[0533] FIG. 16 illustrates a method for generating a reference frame according to embodiments.

[0534] When the encoder and / or decoder requests a reference point from a reference frame for attribute coding, check whether the attribute coding coordinate system is an orthogonal coordinate system. If the attribute coding coordinate system is an orthogonal coordinate system, select a region-specific geometry reference point (N) generated based on an octree node and a reference point according to a specified method, and store the reference point in the reference frame buffer.

[0535] If the attribute coding coordinate system is not an orthogonal coordinate system, check whether the attribute coding coordinate system is an angular coordinate system. If the attribute coding coordinate system is an angular coordinate system, select a reference point according to a specified method and the region-specific point (N) generated as a quantized value based on the azimuth, and store the reference point in the reference frame buffer.

[0536] FIG. 17 shows a bitstream according to embodiments.

[0537] An encoding method / device according to embodiments (a transmitting device (10000) in FIG. 1, an acquisition unit (10001), an encoder (10002), a transmitter (10003), an acquisition (20000) in FIG. 2, an encoding (20001), a transmission (20002), encoders in FIG. 3, FIG. 8, FIG. 12, each device in FIG. 10, frame generation in FIG. 14 to 15, bitstream generation in FIG. 17 to 23, encoding in FIG. 24, etc.) encodes point cloud data, generates related parameter information, and the encoded point

[0538] A decoding method / device according to embodiments (FIG. 1 receiving device (10004), receiving unit (10005), decoder (10006), renderer (10007), FIG. 2 decoding (20003), rendering (20004), FIG. 7, FIG. 9, FIG. 13 decoder, FIG. 10 each device, FIG. 14 to FIG. 15 reference frame generation, FIG. 25 decoding, etc.) can parse a bitstream and decode point cloud data based on parameter set information within the bitstream (FIG. 18 to FIG. 23, etc.).

[0539] Each abbreviation referring to information within a bitstream means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning: SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometry bitstream = geometry slice header+ [geometry PU header + Geometry PU data] | geometry slice data, Attr: Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data.

[0540] The embodiments may signal by adding reference frame buffer-related option information to the SPS, GPS, and / or TPS. The aforementioned reference frame buffer-related option information may be signaled by adding it to the TPS, or to the geometry header and / or attribute header for each slice. Tile or slice is provided to allow the point cloud to be processed by dividing it into regions. When dividing by regions, different neighbor point set generation options can be set for each region to provide a choice that offers low complexity but slightly lower reliability of the result, or conversely, high complexity but high reliability. This can be configured differently depending on the receiver's capacity. Therefore, when point cloud data is divided into tiles, different options can be applied to each tile. When point cloud data is divided into slices, different options can be applied to each slice.

[0541] In the following, parameter information contained within the bitstream is explained with reference to each drawing.

[0542] FIG. 18 shows a set of sequence parameters (SPS) in a bitstream according to embodiments.

[0543] The embodiments can signal by adding reference frame buffer related option information to the Sequence Parameter Set to support efficient inter-predictive signaling and inter-predictive compression.

[0544] Profile (profile_idc): Represents the profile that the bitstream adheres to.

[0545] Profile compatibility flags (profile_compatibility_flags): If 1, it indicates that the bitstream follows the profile where profile_idc is j.

[0546] Number of attribute sets (sps_num_attribute_sets): Represents the number of coded attributes in the bitstream. The value of sps_num_attribute_sets can be in the range of 0 to 63.

[0547] Attribute Dimension(attribute_dimension[i]): Represents the number of components of the i-th attribute.

[0548] Attribute instance ID (attribute_instance_id[i]): Represents the instance ID of the i-th attribute.

[0549] Integrated_reference_frame_buffer_usage_flag: Indicates whether to use the integrated reference frame buffer for the sequence.

[0550] FIG. 19 shows a set of geometry parameters (GPS) in a bitstream according to embodiments.

[0551] The embodiments can signal by adding reference frame buffer related option information for the geometry information encoding / decoding process to the geometry parameter set for efficient inter-predictive signaling.

[0552] Geometry parameter set ID (gps_geom_parameter_set_id): Represents a GPS identifier that other syntax elements can reference. The value of gps_seq_parameter_set_id can be in the range of 0 to 15.

[0553] Sequence parameter set ID (gps_seq_parameter_set_id): Represents the value of sps_seq_parameter_set_id for the active SPS. The value of gps_seq_parameter_set_id can be in the range of 0 to 15.

[0554] Geometry tree type (geom_tree_type): If geom_tree_type is 0, it indicates that location information is coded using an octree (accupancy tree). If geom_tree_type is 1, it indicates that location information is coded using a prediction tree.

[0555] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0556] Geometry quantization (geom_quant): Represents a value (quantization) for dividing the azimuth applied to the frame into arbitrary regions.

[0557] Geometry count (geom_N): Represents the maximum number of points for storing points per zone divided by the azimuth applied to the frame.

[0558] Geometry Sampling Method Type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the frame. For example, if this value is 0, only geom_N points are selected and stored from the first point; if 1, only geom_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only geom_N points are selected and stored from the last point; if 3, only geom_N points are selected and stored in order of the largest value in the azimuth range; if 4, points are selected and stored by downsampling; the sampling method type information can be signaled as follows.

[0559] Geometry Extraction Count (geom_extract_N): Applied to the frame; when using an integrated reference frame, indicates the maximum number of reference points to take from the reference frame and use.

[0560] Geometry extraction sampling method type (geom_extract_sampling_method_type): Applied to the frame, and when using an integrated reference frame, indicates the method of selecting geom_N points for each region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; the point extraction sampling method type can be signaled as follows.

[0561] FIG. 20 shows a set of attribute parameters (APS) in a bitstream according to embodiments.

[0562] The embodiments can signal by adding parameter information for the reference frame buffer to the attribute parameter set for efficient reference frame buffer support.

[0563] Attribute parameter set ID (aps_attr_parameter_set_id): Represents an identifier for the APS that other syntax elements can reference. The value of aps_attr_parameter_set_id can be in the range of 0 to 15.

[0564] Sequence parameter set ID (aps_seq_parameter_set_id): Represents the value of sps_seq_parameter_set_id for the active SPS. The value of aps_seq_parameter_set_id can be in the range of 0 to 15.

[0565] Lifting nearest neighbor count (lifting_num_pred_nearest_neighbours): Represents the maximum number of nearest neighbors to use for prediction.

[0566] Maximum number of lifting direct predictors (lifting_max_num_direct_predictors): Indicates the maximum number of predictors to use for direct prediction. The value of lifting_max_num_direct_predictors can range from 0 to lifting_num_pred_nearest_neighbours. The values ​​of the variable MaxNumPredictors used in the decoding process are as follows:

[0567] MaxNumPredictors = MaxNumDirectPredictors + 1

[0568] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0569] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the frame.

[0570] Attribute Tree Node Level (attr_occtree_node_level): Represents the octree depth level value when using an orthogonal coordinate system applied to the frame. This value is used to define intervals based on the octree (accupancy tree).

[0571] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the frame into arbitrary regions.

[0572] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the frame.

[0573] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points for each region divided by the azimuth applied to the frame. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the type of method for sampling attributes can be signaled.

[0574] FIG. 21 shows a set of tile parameters (TPS) in a bitstream according to embodiments.

[0575] The embodiments can signal by adding parameter information for the reference frame buffer to the Tile Parameter Set for efficient reference frame buffer support.

[0576] Count of tiles (num_tiles): Represents the number of tiles signaled for the bitstream. If this value does not exist, num_tiles is inferred to be 0.

[0577] Tile bounding box offset x(tile_bounding_box_offset_x[i]): Represents the x-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_x[0] is inferred as sps_bounding_box_offset_x.

[0578] Tile bounding box offset y(tile_bounding_box_offset_y[i]): Represents the y-offset of the i-th tile in Cartesian coordinates. If not present, the value of tile_bounding_box_offset_y[0] is inferred as sps_bounding_box_offset_y.

[0579] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for tiles.

[0580] Geometry Quantization (geom_quant): Represents a value for dividing the azimuth applied to the tile into arbitrary zones.

[0581] Geometry Count (geom_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0582] Geometry sampling method type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the tile. For example, if this value is 0, only geom_N points are selected and stored from the first point; if 1, only geom_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only geom_N points are selected and stored from the last point; if 3, only geom_N points are selected and stored in order of the largest value in the azimuth range; if 4, points are selected and stored by downsampling; and so on, the type of geometry point sampling method can be signaled.

[0583] Geometry Extraction Count (geom_extract_N): Applied to tiles, and indicates the maximum number of reference points to take from the reference frame when using an integrated reference frame.

[0584] Geometry extract sampling method type (geom_extract_sampling_method_type): Applied to tiles, and when using a unified reference frame, indicates a method for selecting geom_N points per region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; and so on.

[0585] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0586] Attribute Octree Node Level (attr_occtree_node_level): Represents the octree (accupancy tree) depth level value when using the Cartesian coordinate system applied to the tile. It is used to define intervals based on the octree (accupancy tree).

[0587] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the tile into arbitrary zones.

[0588] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0589] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points per zone divided by the azimuth applied to the tile. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the attribute sampling method type can be signaled.

[0590] FIG. 22 shows a geometry slice header (GSH) within a bitstream according to embodiments.

[0591] The embodiments can signal LPU / PU-specific geometry prediction tree coding option information for the geometry information encoding / decoding process by adding it to the geometry slice header.

[0592] Geometry parameter set ID (gsh_geometry_parameter_set_id): Represents the gps_geom_parameter_set_id value of the active GPS.

[0593] Tile ID (gsh_tile_id): Represents the tile ID value referenced by GSH.

[0594] Slice ID (gsh_slice_id): Represents the slice header that other syntax elements will reference.

[0595] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the slice.

[0596] Geometry Quantization (geom_quant): Represents a value for dividing the azimuth applied to the slice into arbitrary regions.

[0597] Geometry count (geom_N): Represents the maximum number of points for storing points per zone divided by the azimuth applied to the slice.

[0598] Geometry sampling method type (geom_sampling_method_type): Indicates the method of selecting geom_N points for each region divided by the azimuth applied to the slice. For example, if this value is 0, only geom_N points are selected and stored from the first point; 1 = only geom_N points are selected and stored in order of the smallest value in the azimuth range; 2 = only geom_N points are selected and stored from the last point; 3 = only geom_N points are selected and stored in order of the largest value in the azimuth range; 4 = downsampled and selected and stored; and so on, the geometry sampling method type can be signaled.

[0599] Geometry Extraction Count (geom_extract_N): Applied to slices, and indicates the maximum number value to take and use from the reference frame when using an integrated reference frame.

[0600] Geometry Extraction Sampling Method Type (geom_extract_sampling_method_type): Applied to slices, and when using a unified reference frame, indicates a method for selecting geom_N points per region divided by azimuth from the reference frame. For example, if this value is 0, only geom_N points are selected from the first point; if 1, only geom_N points are selected in order of the smallest value in the azimuth range; if 2, only geom_N points are selected from the last point; if 3, only geom_N points are selected in order of the largest value in the azimuth range; if 4, only geom_N points are selected by downsampling; and so on, the geometry extraction sampling method type can be signaled.

[0601] FIG. 23 shows an attribute slice header (ASH) within a bitstream according to embodiments.

[0602] The embodiments can signal by adding parameter information for reflectance attribute coding to the Attribute Slice Header to support efficient reflectance attribute compression.

[0603] Attribute parameter set ID (abh_attr_parameter_set_id): Represents the aps_attr_parameter_set_id value of the active APS.

[0604] Attribute SPS attribute index (abh_attr_sps_attr_idx): Represents the attribute set of the active SPS. The value of abh_attr_sps_attr_idx can be in the range of 0 to sps_num_attribute_sets for the active SPS.

[0605] Attribute Geometry Slice ID (ash_attr_geom_slice_id): Represents the slice ID (gsh_slice_id) value of the active Geometry Slice Header.

[0606] Integrated Reference Frame Buffer Usage Flag (integrated_reference_frame_buffer_usage_flag): Indicates whether to use the integrated reference frame buffer for the frame.

[0607] Attribute Count (attr_N): Represents the maximum number value for storing points per zone divided by the azimuth applied to the tile.

[0608] Attribute Octree Node Level (attr_occtree_node_level): Represents the octree (accupancy tree) depth level value when using the Cartesian coordinate system applied to the slice. It is used to define intervals based on the octree (accupancy tree).

[0609] Attribute Quantization (attr_quant): Represents a value used to divide the azimuth applied to the slice into arbitrary regions.

[0610] Attribute Count (attr_N): Represents the maximum number of points to store per zone divided by the azimuth applied to the slice.

[0611] Attribute sampling method type (attr_sampling_method_type): Indicates the method of selecting attr_N points for each region divided by the azimuth applied to the slice. For example, if this value is 0, only attr_N points are selected and stored from the first point; if 1, only attr_N points are selected and stored in order of the smallest value in the azimuth range; if 2, only attr_N points are selected and stored from the last point; if 3, only attr_N points are selected and stored in order of the largest value in the azimuth range; if 4, only attr_N points are selected and stored by downsampling; and so on, the attribute sampling method type can be signaled.

[0612] FIG. 24 illustrates a encoding method according to embodiments.

[0613] The encoding method according to the embodiments may include the step of encoding point cloud data (S2400); and / or the step of transmitting a bitstream containing point cloud data (S2410).

[0614] Point cloud data may be represented based on at least one of radius, azimuth, and laser index. The step of encoding point cloud data (S2400) includes: a step of encoding geometry data of point cloud data based on inter prediction, and a step of encoding attribute data of point cloud data based on inter prediction, wherein the step of encoding geometry data and the step of encoding attribute data may each further include a step of generating a reference frame.

[0615] The step of generating a reference frame includes generating a reference frame for decoding geometry data based on a prediction tree for point cloud data, wherein reference points in the reference frame are aligned based on azimuth angles, and at least one reference point for a zone is stored in a buffer for the reference frame based on a zone generated based on a quantization value in the reference frame, and if there are multiple reference points within a zone, a first number of points among the multiple reference points may be stored in a buffer based on at least one of an aligned order or an azimuth angle range.

[0616] The step of generating a reference frame includes: generating a reference frame for decoding attribute data when the coordinate system for attribute data is different from the coordinate system for geometry data; when the coordinate system for attribute data is the same as the coordinate system for geometry data, attribute data is decoded based on the reference frame for geometry data; when the coordinate system for attribute data is an orthogonal coordinate system, a first number of attribute data is generated as a reference frame based on an octree for point cloud data; when the coordinate system for attribute data is an angular coordinate system, a first number of attribute data is generated as a reference frame based on an azimuth angle; and the first number of attribute data may be selected based on at least one of an aligned order or an azimuth angle.

[0617] The embodiments may further include a computer-readable storage medium for storing a bitstream generated by the method according to FIG. 24.

[0618] The embodiments may further include a method comprising the steps of: acquiring a bitstream for point cloud data, the bitstream being generated based on the step of encoding the point cloud data; and transmitting data including the bitstream.

[0619] FIG. 25 illustrates a decoding method according to embodiments.

[0620] The decoding method according to the embodiments may include the step of receiving a bitstream containing point cloud data (S2500); and / or the step of decoding the point cloud data (S2510). The decoding method of FIG. 25 may follow the reverse process of the encoding method of FIG. 24.

[0621] Referring together to the LiDAR data characteristics of FIG. 11 and the decoder configuration of FIG. 13, the point cloud data is represented based on at least one of radius, azimuth, and laser index, and the step of decoding the point cloud data (S2510) includes: the step of decoding the geometry data of the point cloud data based on inter-prediction, and the step of decoding the attribute data of the point cloud data based on inter-prediction, and the step of decoding the geometry data and the step of decoding the attribute data may each further include the step of generating a reference frame.

[0622] Regarding the generation of a reference frame for geometry coding based on a prediction tree, the step of generating a reference frame includes generating a reference frame for decoding geometry data based on a prediction tree for point cloud data, wherein reference points in the reference frame are aligned based on azimuth angles, and at least one reference point for a region is stored in a buffer for the reference frame based on a region generated based on a quantization value in the reference frame, and if there are multiple reference points within a region, a first number of points among the multiple reference points may be stored in a buffer based on at least one of an aligned order or an azimuth angle range.

[0623] Regarding the generation of a reference frame for information coding, the step of generating a reference frame includes: generating a reference frame for decoding the attribute data when the coordinate system for the attribute data is different from the coordinate system for the geometry data; when the coordinate system for the attribute data is the same as the coordinate system for the geometry data, the attribute data is decoded based on the reference frame for the geometry data; when the coordinate system for the attribute data is an orthogonal coordinate system, a first number of attribute data is generated as a reference frame based on the octree for the point cloud data; when the coordinate system for the attribute data is an angular coordinate system, a first number of attribute data is generated as a reference frame based on the azimuth angle; and the first number of attribute data may be selected based on at least one of an aligned order or an azimuth angle.

[0624] Regarding the generation of a geometry / attribute integrated reference frame, the step of generating the reference frame includes: generating a first number of attribute data as a reference frame based on an octree when the coordinate system for geometry data and the coordinate system for attribute data are orthogonal coordinate systems, and generating a first number of attribute data as a reference frame based on an azimuth when the coordinate system for geometry data and the coordinate system for attribute data are angular coordinate systems, wherein the first number of attribute data may be selected based on at least one of an aligned order or an azimuth.

[0625] In relation to the SPS, GPS, and APS of FIGS. 17 to 20, the bitstream includes at least one of a sequence parameter set, a geometry parameter set, an attribute parameter set, a tile parameter set, a geometry data header, or an attribute data header; the sequence parameter set includes information indicating whether an integrated reference frame buffer is used; the geometry parameter set includes at least one of information indicating whether an integrated reference frame buffer is used, a quantization value related to an azimuth for the reference frame, information on the number of points related to a zone for the azimuth, information indicating a method for selecting points within a zone for the azimuth, information on the number of reference points related to the integrated reference frame, or a method for selecting reference points related to the integrated reference frame; and the attribute parameter set may include at least one of information indicating whether an integrated reference frame buffer is used, information on the maximum number of points within a zone related to an azimuth for the reference frame, information on the depth level of an octree related to the reference frame, a quantization value related to the reference frame, or a method for selecting points within a zone related to an azimuth.

[0626] Regarding the TPS of FIG. 21, the tile parameter set may include at least one of information indicating whether an integrated reference frame buffer for the tile is used, a quantization value related to the azimuth applied to the tile, information on the maximum number of points within a zone related to the azimuth applied to the tile, a method for selecting points within a zone related to the azimuth, information on the number of reference points related to the integrated reference frame applied to the tile, a method for selecting reference points within the integrated reference frame applied to the tile, information on the number of attribute data within a zone related to the azimuth applied to the tile, octree depth level information applied to the tile, azimuth quantization information of attribute data applied to the tile, or a method for selecting attribute data within a zone related to the azimuth applied to the tile.

[0627] With respect to the GSH and ASH of FIGS. 22 and 23, the geometry data header may include at least one of information indicating whether an integrated reference frame buffer for the slice is used, a quantization value related to the azimuth angle applied to the slice, information on the maximum number of points within a zone related to the azimuth angle applied to the slice, a method for selecting points within a zone related to the azimuth angle, information on the number of reference points related to an integrated reference frame applied to the slice, or a method for selecting reference points within an integrated reference frame applied to the slice, and the attribute data header may include at least one of information indicating whether an integrated reference frame buffer for the slice is used, information on the maximum number of attribute data within a zone related to the azimuth angle applied to the slice, octree depth level information applied to the slice, a quantization value for attribute data applied to the slice, or a method for selecting attribute data within a zone.

[0628] The decoding method can be performed by a decoding device as shown in FIG. 13. 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: receive a bitstream containing point cloud data; and decode the point cloud data.

[0629] The method / device according to the embodiments provides the following technical effects.

[0630] When storing reference frames in a buffer for inter-prediction, if geometry and attribute information use different coordinate systems, information from two different coordinate systems may be stored for a single reference frame. Storing only one of them increases complexity by incurring computational load because the decoder must perform additional coordinate transformations; conversely, storing both increases memory usage, which can burden the decoder. When Bi-Prediction is applied, more computation and memory usage may be required. Furthermore, global or local motion may be applied only to geometry or only to attributes. Such cases can also increase the number of reference frames.

[0631] The embodiments have the effect of solving the aforementioned technical problems through a reference frame buffer management method for applying predictive compression technology through reference frames to a point cloud having multiple frames captured by a spinning lidar.

[0632] Therefore, the embodiments have the effect of providing a method to minimize computational load and memory usage. Since the precision of the reference frame can be adjusted, in the case of noisy data, the effect of removing noise is also provided, thereby increasing the compression efficiency for inter-prediction of point cloud data. Accordingly, the transmitting method / device according to the embodiments can efficiently compress point cloud data and transmit the data, and by transmitting signaling information for this purpose, the receiving method / device according to the embodiments can also efficiently decode / restore the point cloud data.

[0633] 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.

[0634] 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 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.

[0635] In this document, “ / ” and “,” are interpreted as “and / or.” For example, “A / B” is interpreted as “A and / or B,” and “A, B” is interpreted as “A and / or B.” Additionally, “A / B / C” means “at least one of A, B and / or C.” Also, “A, B, C” means “at least one of A, B and / or C.” Additionally, in this document, “or” is interpreted as “and / or.” For example, “A or B” may mean 1) “A” alone, 2) “B” alone, or 3) “A and B.” In other words, “or” in this document may mean “additionally or alternatively.”

[0636] 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.

[0637] 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."

[0638] 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.

[0639] 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.

[0640] 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.

[0641] Form for carrying out the invention

[0642] As described above, the relevant details have been explained in the best mode for carrying out the embodiments. Industrial applicability

[0643] As described above, the embodiments can be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0644] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0645] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

Claim 1 A decoding method comprising: receiving a bitstream containing point cloud data; and decoding the point cloud data. Claim 2 A decoding method according to claim 1, wherein the point cloud data is represented based on at least one of a radius, an azimuth angle, and a laser index, and the step of decoding the point cloud data comprises: a step of decoding the geometry data of the point cloud data based on inter-prediction, and a step of decoding the attribute data of the point cloud data based on inter-prediction, wherein the step of decoding the geometry data and the step of decoding the attribute data each further comprise a step of generating a reference frame. Claim 3 In paragraph 2, the step of generating the reference frame comprises: generating a reference frame for decoding the geometry data based on a prediction tree for the point cloud data, wherein reference points in the reference frame are aligned based on an azimuth angle, and at least one reference point for a region is stored in a buffer for the reference frame based on a region generated based on a quantization value in the reference frame, and if a plurality of reference points exist within the region, a first number of points among the plurality of reference points are stored in the buffer based on at least one of an aligned order or an azimuth angle range. Claim 4 In paragraph 2, the step of generating the reference frame comprises: generating a reference frame for decoding the attribute data when the coordinate system for the attribute data is different from the coordinate system for the geometry data; when the coordinate system for the attribute data is the same as the coordinate system for the geometry data, the attribute data is decoded based on the reference frame for the geometry data; when the coordinate system for the attribute data is an orthogonal coordinate system, a first number of attribute data is generated as the reference frame based on the octree for the point cloud data; when the coordinate system for the attribute data is an angular coordinate system, a first number of attribute data is generated as the reference frame based on the azimuth angle; and the first number of attribute data is selected based on at least one of the sorted order or the azimuth angle. Claim 5 In paragraph 2, the step of generating the reference frame comprises: generating a first number of attribute data as a reference frame based on an octree when the coordinate system for the geometry data and the coordinate system for the attribute data are orthogonal coordinate systems, and generating a first number of attribute data as a reference frame based on an azimuth angle when the coordinate system for the geometry data and the coordinate system for the attribute data are angular coordinate systems, wherein the first number of attribute data is selected based on at least one of an aligned order or an azimuth angle. Claim 6 A decoding method according to claim 1, wherein the bitstream comprises at least one of a sequence parameter set, a geometry parameter set, an attribute parameter set, a tile parameter set, a geometry data header, or an attribute data header, wherein the sequence parameter set comprises information indicating whether an integrated reference frame buffer is used, the geometry parameter set comprises at least one of information indicating whether an integrated reference frame buffer is used, a quantization value related to an azimuth angle for a reference frame, information on the number of points related to a zone for the azimuth angle, information indicating a method for selecting points within a zone for the azimuth angle, information on the number of reference points related to an integrated reference frame, or a method for selecting reference points related to an integrated reference frame, and wherein the attribute parameter set comprises at least one of information indicating whether an integrated reference frame buffer is used, information on the maximum number of points within a zone related to an azimuth angle for a reference frame, information on the depth level of an octree related to the reference frame, a quantization value related to the reference frame, or a method for selecting points within a zone related to the azimuth angle. Claim 7 A decoding method according to claim 6, wherein the tile parameter set comprises at least one of: information indicating whether an integrated reference frame buffer for the tile is used; a quantization value related to an azimuth applied to the tile; information on the maximum number of points within a zone related to the azimuth applied to the tile; a method for selecting points within a zone related to the azimuth; information on the number of reference points related to an integrated reference frame applied to the tile; a method for selecting reference points within an integrated reference frame applied to the tile; information on the number of attribute data within a zone related to the azimuth applied to the tile; octree depth level information applied to the tile; azimuth quantization information of attribute data applied to the tile; or a method for selecting attribute data within a zone related to the azimuth applied to the tile. Claim 8 In claim 6, the geometry data header comprises at least one of information indicating whether an integrated reference frame buffer for the slice is used, a quantization value related to an azimuth angle applied to the slice, information on the maximum number of points within a zone related to the azimuth angle applied to the slice, a method for selecting points within a zone related to the azimuth angle, information on the number of reference points related to an integrated reference frame applied to the slice, or a method for selecting reference points within an integrated reference frame applied to the slice, and the attribute data header comprises at least one of information indicating whether an integrated reference frame buffer for the slice is used, information on the maximum number of attribute data within a zone related to the azimuth angle applied to the slice, octree depth level information applied to the slice, a quantization value for attribute data applied to the slice, or a method for selecting attribute data within a zone. Claim 9 A decoding device comprising: a memory; and at least one processor connected to the memory; wherein the at least one processor is configured to: receive a bitstream containing point cloud data; and decode the point cloud data. Claim 10 A encoding method comprising: a step of encoding point cloud data; and a step of transmitting a bitstream containing the point cloud data. Claim 11 In claim 10, the point cloud data is represented based on at least one of radius, azimuth, and laser index, and the step of encoding the point cloud data includes: the step of encoding the geometry data of the point cloud data based on inter-prediction, and the step of encoding the attribute data of the point cloud data based on inter-prediction, wherein the step of encoding the geometry data and the step of encoding the attribute data each further include the step of generating a reference frame. Claim 12 In claim 11, the step of generating the reference frame comprises: generating a reference frame for decoding the geometry data based on a prediction tree for the point cloud data, wherein reference points in the reference frame are aligned based on an azimuth angle, and at least one reference point for a region is stored in a buffer for the reference frame based on a region generated based on a quantization value in the reference frame, and if a plurality of reference points exist within the region, a first number of points among the plurality of reference points are stored in the buffer based on at least one of an aligned order or an azimuth angle range. Claim 13 In claim 11, the step of generating the reference frame comprises: generating a reference frame for decoding the attribute data when the coordinate system for the attribute data is different from the coordinate system for the geometry data; when the coordinate system for the attribute data is the same as the coordinate system for the geometry data, the attribute data is decoded based on the reference frame for the geometry data; when the coordinate system for the attribute data is an orthogonal coordinate system, a first number of attribute data is generated as the reference frame based on the octree for the point cloud data; when the coordinate system for the attribute data is an angular coordinate system, a first number of attribute data is generated as the reference frame based on the azimuth angle; and the first number of attribute data is selected based on at least one of the sorted order or the azimuth angle. Claim 14 A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 10. Claim 15 A method comprising the steps of: acquiring a bitstream for point cloud data, wherein the bitstream is generated based on the step of encoding the point cloud data; and transmitting data including the bitstream.

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