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

Abstract

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

Description

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

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

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

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

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

[0005] A method according to the embodiments may include the step of decoding geometry data of point cloud data within a bitstream; and the step of decoding attribute data of point cloud data. A method according to the embodiments may include the step of encoding geometry data of point cloud data; and the step of encoding attribute data of point cloud data.

[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.

[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.

[0010] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0011] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0012] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0013] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0014] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0015] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0016] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0017] FIG. 8 is an example of a transmission device according to embodiments.

[0018] FIG. 9 is an example of a receiving device according to embodiments.

[0019] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0020] FIG. 11 illustrates a predictor search according to embodiments.

[0021] FIG. 12 shows downscaling according to embodiments.

[0022] FIG. 13 shows an encoding device according to embodiments.

[0023] FIG. 14 shows an attribute encoder according to embodiments.

[0024] FIG. 15 shows a decoding device according to embodiments.

[0025] FIG. 16 shows an attribute decoder according to embodiments.

[0026] FIG. 17 shows a LoD generation unit according to embodiments.

[0027] FIG. 18 shows a Morton code assignment and alignment unit according to embodiments.

[0028] FIG. 19 shows a nearest neighbor search unit according to embodiments.

[0029] FIG. 20 shows a bitstream according to embodiments.

[0030] FIG. 21 shows the syntax of an attribute parameter set within a bitstream according to embodiments.

[0031] FIG. 22 shows the attribute data unit header syntax within a bitstream according to embodiments.

[0032] FIG. 23 illustrates an atlas search within a reference frame according to embodiments.

[0033] FIG. 24 illustrates an atlas search within a reference frame according to embodiments.

[0034] FIG. 25 shows attribute encoding according to embodiments.

[0035] FIG. 26 illustrates attribute decoding according to embodiments.

[0036] FIG. 27 shows a molton code assignment and alignment unit according to embodiments.

[0037] FIG. 28 shows a nearest neighbor search unit according to embodiments.

[0038] FIG. 29 shows an inter-neighbor search unit according to embodiments.

[0039] FIG. 30 shows the syntax of a set of attribute parameters in a bitstream according to embodiments.

[0040] FIG. 31 shows the attribute data unit header syntax within a bitstream according to embodiments.

[0041] FIG. 32 illustrates a encoding method according to embodiments.

[0042] FIG. 33 illustrates a decoding method according to embodiments.

[0043] 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.

[0044] 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.

[0045] FIG. 1 shows an example of a point cloud content provision system according to embodiments.

[0046] 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.

[0047] A transmission device (10000) according to embodiments can acquire, process, and transmit point cloud video (or point cloud content). According to embodiments, the transmission device (10000) may include a fixed station, a base transceiver system (BTS), a network, an AI (Artificial Intelligence) device and / or system, a robot, an AR / VR / XR device and / or server, etc. Additionally, according to embodiments, the transmission device (10000) may include a device that communicates with a base station and / or other wireless devices using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a mobile device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] 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)).

[0053] 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).

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] The elements of the point cloud content delivery system illustrated in FIG. 1 can be implemented using hardware, software, processors, and / or combinations thereof.

[0061] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0062] 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).

[0063] 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.).

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.).

[0069] 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.

[0070] FIG. 3 shows an example of a point cloud encoder according to embodiments.

[0071] 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.

[0072] 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.

[0073] 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).

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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).

[0085] 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.

[0086] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or LOD generation unit (30009).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0091] An arismetic encoder (30012) according to the embodiments encodes quantized attributes based on arismetic coding.

[0092] 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).

[0093] FIG. 4 shows examples of octree and occupancy codes according to embodiments.

[0094] 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.

[0095] 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.

[0096] d =Ceil(Log2(Max(x_n^int,y_n^int,z_n^int,n=1,…,N)+1))

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106]

[0107] 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.

[0108] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0109] n triangles

[0110] 3 (1,2,3)

[0111] 4 (1,2,3), (3,4,1)

[0112] 5 (1,2,3), (3,4,5), (5,1,3)

[0113] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0114] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0115] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0116] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0117] 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)

[0118] 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)

[0119] 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)

[0120] 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).

[0121] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0122] 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.

[0123] 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.

[0124] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] graph. Attribute prediction residuals quantization pseudo code

[0131] int PCCQuantization(int value, int quantStep) {

[0132] if( value >=0) {

[0133] return floor(value / quantStep + 1.0 / 3.0);

[0134] } else {

[0135] return -floor(-value / quantStep + 1.0 / 3.0);

[0136] }

[0137] }

[0138] graph. Attribute prediction residuals inverse quantization pseudo code

[0139] int PCCInverseQuantization(int value, int quantStep) {

[0140] if( quantStep ==0) {

[0141] return value;

[0142] } else {

[0143] return value * quantStep;

[0144] }

[0145] }

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 3) Create temporary arrays named updateweight and update, and initialize the temporary arrays to 0.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The following equation represents the RAHT transformation matrix. g l x, y, z represents the average attribute value of the voxels at level l. g l x, y, z can be calculated from gl+1 2x, y, z and gl+1 2x+1, y, z. The weights of gl 2x, y, z and gl 2x+1, y, z are w1=wl 2x, y, z and w2=wl 2x+1, y, z.

[0156]

[0157] gl-1 x, y, z are low-pass values ​​used in the merging process at the next higher level. hl-1 x, y, z are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy-coded (e.g., encoding of an arismetic encoder (400012)). The weights are calculated as wl-1 x, y, z = wl 2x, y, z + wl 2x + 1, y, z. The root node is the last g 1 0, 0, 0 and g 1 0, 0, 1 It is generated as follows through.

[0158]

[0159] The gDC value is also quantized and entropy-coded, just like the high-pass coefficient.

[0160] FIG. 7 shows an example of a point cloud decoder according to embodiments.

[0161] 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.

[0162] As described in Fig. 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0163] 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).

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The arismetic decoder (7005) according to the embodiments decodes the attribute bitstream into arismetic coding.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] FIG. 8 is an example of a transmission device according to embodiments.

[0177] 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).

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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 about 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 ).

[0192] A slice refers to a series of syntax elements representing all or part of a coded point cloud frame.

[0193] 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.

[0194] FIG. 9 is an example of a receiving device according to embodiments.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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).

[0201] 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).

[0202] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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).

[0207] 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.

[0208] FIG. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] <PCC+XR>

[0215] 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.

[0216] 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.

[0217] <PCC+XR+모바일폰>

[0218] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0219] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0220] <PCC+자율주행+XR>

[0221] The autonomous vehicle (1020) can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying PCC technology and XR technology.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] The PCC method / device according to the embodiments can be applied to a vehicle providing autonomous driving services.

[0229] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0230] 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.

[0231] A point cloud data transmission method / device (or encoding method and device) according to embodiments includes the transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, a predictor search of FIG. 11, downscaling of FIG. 12, encoders of FIG. 13 to 14, LoD generation of FIG. 17, molton code assignment and alignment of FIG. 18, nearest neighbor search of FIG. 19, bitstream and parameter generation of FIG. 20 to 22, predictor search within reference frames of FIG. 23 to 24, encoder of FIG. 25, molton code assignment and alignment-based nearest neighbor search of FIG. 27 to 29, parameter generation of FIG. 30-31, etc.

[0232] A method / device for receiving point cloud data (or a method and device for decoding) according to embodiments includes the receiving device (10004) of FIG. 1, a receiver (10005), a point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the predictor search of FIG. 11, downscaling of FIG. 12, the decoders of FIG. 15 to 16, the LoD generation of FIG. 17, the molton code assignment and alignment of FIG. 18, the nearest neighbor search of FIG. 19, the bitstream and parameter acquisition of FIG. 20 to 22, the predictor search within the reference frame of FIG. 23 to 24, the decoder of FIG. 26, the molton code assignment and alignment-based nearest neighbor search of FIG. 27 to 29, and the parameter acquisition of FIG. 30 to 31.

[0233] In addition, the point cloud data transmission / reception method / device according to the embodiments may be referred to simply as the method / device according to the embodiments.

[0234] According to the embodiments, geometry data, geometry information, location information, etc. constituting the point cloud data are interpreted as having the same meaning. Attribute data, attribute information, attribute information, etc. constituting the point cloud data are interpreted as having the same meaning.

[0235] The method according to the embodiments includes a fast and high-accuracy nearest neighbor search method for LOD generation in inter-prediction.

[0236] The embodiments include a method for improving attribute compression speed and performance through inter-prediction of Geometry-based Point Cloud Compression (G-PCC) for 3D point cloud data compression. The embodiments include a nearest neighbor search method to increase attribute compression efficiency and reduce the time required by utilizing inter-prediction technology in generating a Level of Detail (hereinafter referred to as LOD) during the G-PCC attribute encoding and decoding process.

[0237] The embodiments increase the utilization of reference frames with high similarity during nearest neighbor search, and perform faster and more accurate neighbor search in this process. To increase the utilization of reference frames, a neighbor search method different from that of the current frame may be used, and the method of accessing points in the reference frame may be changed. Additionally, when the same neighbor search as the current frame is performed, the signaled data can be minimized to reduce the burden on data transmission.

[0238] The method according to the embodiments includes a high-speed and high-accuracy nearest neighbor search method in inter-prediction, the use of a nearest neighbor search method in a reference frame different from the current frame, minimization of transmitted signal data when using a nearest neighbor search method in a reference frame, modification of the point access method of the reference frame, and / or signaling schemes.

[0239] The embodiments include methods to increase the compression efficiency and speed of G-PCC for 3D point cloud data compression.

[0240] Hereinafter, the encoder and encoder will be referred to as encoders, and the decoder and decoder as decoders.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used to compress geometry information.

[0245] For attribute information compression, predicting transform-based, lifting transform-based, or RAHT transform-based compression techniques can be used.

[0246] The embodiments include a method for obtaining improvements in speed and compression rate by generating Level of Detail (LOD) for lifting transformations used for inter-prediction-based attribute information compression of point cloud content captured by a LiDAR RGB-D camera or LiDAR equipment, by generating LODs according to the characteristics of the point cloud content.

[0247] FIG. 11 illustrates a predictor search according to embodiments.

[0248] The encoding method and apparatus according to the embodiments (transmitting device (10000) of FIG. 1, point cloud video encoder (10002), transmitter (10003), acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, downscaling of FIG. 12, encoders of FIG. 13 to 14, LoD generation of FIG. 17, molton code assignment and alignment of FIG. 18, nearest neighbor search of FIG. 19, bitstream and parameter generation of FIG. 20 to 22, predictor search within reference frame of FIG. 23 to 24, encoder of FIG. 25, molton code assignment and alignment-based nearest neighbor search of FIG. 27 to 29, parameter generation of FIG. 30-31, etc.) can search for a predictor as in FIG. 11.

[0249] The decoding method and apparatus according to the embodiments (receiving device (10004), receiver (10005), point cloud video decoder (10006) of FIG. 1, transmission-decoding-rendering (20002-20003-20004) of FIG. 2, decoder of FIG. 7, receiving device of FIG. 9, device of FIG. 10, downscaling of FIG. 12, decoders of FIG. 15 to 16, LoD generation of FIG. 17, molton code assignment and alignment of FIG. 18, nearest neighbor search of FIG. 19, bitstream and parameter acquisition of FIG. 20 to 22, predictor search within reference frame of FIG. 23 to 24, decoder of FIG. 26, molton code assignment and alignment-based nearest inter-neighbor search of FIG. 27 to 29, parameter acquisition of FIG. 30-31, etc.) can search for a predictor as in FIG. 11.

[0250] Among the attribute information of point cloud content, color can be acquired through an RGB-D camera capable of simultaneously measuring color (RGB) and depth, while reflection values ​​can be acquired through LiDAR equipment, which measures the position coordinates and reflection values ​​of a reflector by emitting a laser pulse and measuring the time it takes for it to reflect back as well as the laser intensity. The similarity between frames of this attribute information of point cloud content along the time axis is very high.

[0251] Inter prediction techniques, which enhance compression efficiency by utilizing inter-frame similarity, are widely used in 2D image compression. When compressing the current frame from a sequence of highly similar frames, higher compression efficiency can be achieved by compressing the residual value—the difference between the current frame's attribute value and that of the reference frame—rather than directly compressing the attribute value of the current frame. This is because, due to the characteristics of entropy coding, the closer a value is to zero, the higher its probability of existence, allowing that value to be represented with fewer bits. Such inter prediction techniques are also utilized in the compression of multi-frame point clouds; for example, inter prediction is employed in the nearest neighbor search process of prediction and lifting transform techniques.

[0252] Referring to FIG. 11, the target point can be predicted using one or more points among a plurality of reference points or coded neighbor points in three-dimensional space.

[0253] The shaded blocks represent coding units or octree leaf nodes containing the points to be predicted, and the coordinates of each point are defined on a three-dimensional voxel grid.

[0254] The prediction candidate search unit searches for prediction candidates among adjacent nodes within the same level, parent nodes or child nodes, and encoded points in the coordinate axis directions (x, y, z) that exist within a predefined search distance based on the location of the point to be predicted.

[0255] As illustrated in FIG. 11, a plurality of adjacent points are distributed in the surrounding 3D space of the prediction target point, and a prediction point selector selects one of them as a prediction reference point. The selection criteria may be, for example, a minimum value of spatial distance, direction vector similarity, and minimization of prediction error, and a combination thereof.

[0256] By utilizing such a predictor search procedure, the spatial correlation of the point cloud can be efficiently leveraged, and compression efficiency can be improved by reducing prediction errors. Furthermore, by applying the same search rule to the decoding process, prediction consistency between the encoder and decoder can be ensured.

[0257] The atlas search range is interpreted as a term referring to the area where points are distributed, for example, the spatial region for predictor search.

[0258] The search for nearest neighbor points in prediction and lifting transformations is performed to generate predictors. This search process consists of an atlas search, which searches only within a limited specific area called an atlas as shown in Fig. 11, and a global search, which searches the entire area. If a certain number of neighbor points are found during the atlas search, the global search is omitted. This atlas search can be performed more effectively through an atlas search technique based on Morton code scaling.

[0259] FIG. 12 shows downscaling according to embodiments.

[0260] Following FIG. 11, the encoding / decoding method according to the embodiments can derive a search area (atlas search range) based on a downscaling method during predictor search.

[0261] The Morton (or Morton, etc.) code scaling-based atlas search technique according to the embodiments is a technique that expands the range of atlas search by downscaling position information and assigning duplicate Morton codes to multiple points when assigning Morton codes based on position information, as shown in FIG. 12 (Example of Morton Code Down-Scaling in 2D Plane). This technique increases the accuracy of atlas search, thereby reducing the number of times the entire search is performed, and thereby increases the encoding / decoding speed of attribute information compression. However, when inter-prediction is performed in the aforementioned point cloud attribute information compression, the Morton code scaling-based atlas search method lowers the encoding / decoding speed, because downscaling of position information is not applied in the reference frame.

[0262] Therefore, the present embodiments aim to improve speed and compression efficiency by compensating for the shortcomings of the nearest neighbor search process in attribute information compression utilizing inter-prediction in point cloud compression, which requires fast and accurate encoding / decoding. By applying an improved neighbor search technique, the invention aims to support a method that accelerates the decoding process and, in addition, improves the compression ratio.

[0263] Referring to FIG. 12, the point cloud encoding device sets a search range corresponding to a target block on the atlas and determines the most suitable predictor by searching for a plurality of candidate blocks within the range.

[0264] The block before downscaling in FIG. 12 represents a search area defined on the atlas of the original resolution. The search area according to the embodiments consists of 3x3 sub-blocks, and each sub-block is assigned an index (0–8). Each arrow indicates a search direction based on a Molton code.

[0265] However, in the case of high-resolution atlases, directly searching the entire search area drastically increases the amount of computation, so downscaling is applied to the search area to improve search efficiency.

[0266] The downscaled area is, for example, a 3x3 block reduced to a 2x2 block. In this case, the scale factor can be set as an adaptive parameter based on the atlas's resolution level, coding level, or prediction accuracy. Within the downscaled search area, prediction vector search is performed by considering only a small number of prediction candidates (e.g., indices 0–3) compared to the original resolution, which can significantly reduce computational complexity. The selected prediction vector can be upscaled to the original resolution coordinate system and applied; in this case, prediction accuracy can be maintained through scale compensation or term adjustment.

[0267] The computational complexity during atlas search of high-resolution point cloud data can be significantly reduced, and encoding speed can be improved by reducing the number of search candidates through downscaling. Furthermore, efficient prediction vector search is possible while minimizing quality degradation caused by prediction errors. Consequently, the technology of the present invention provides the effect of simultaneously improving prediction accuracy and encoding efficiency during the point cloud encoding and decoding processes.

[0268] The encoding method and decoding method according to the embodiments may include an atlas search (predictor search) step (step 1).

[0269] Atlas search is a method of searching by establishing an area containing a point (shaded area based on Fig. 11) based on the point being sought for the nearest neighbor, as shown in the example of Fig. 11, and establishing an area of ​​the same size as that area in all directions. When the area of ​​the size to which the point belongs is referred to as a mini-cube, a total of 27 mini-cubes are searched, including the mini-cube to which the point belongs. This area of ​​27 mini-cubes can be referred to as the atlas search range. Generally, the size of the mini-cube is set to 2x2x2, so there are a maximum of 8 points within the mini-cube, in which case the size of the atlas search range becomes 8x8x8.

[0270] When establishing the atlas search area, the Morton code possessed by each point is utilized. The Morton code is one of the space-filling curves as shown in Fig. 12. Generally, in attribute compression of G-PCC encoders / decoders, a Morton code is assigned using the 3D coordinates of each point, and attribute encoding / decoding is performed by aligning the points in the order of the Morton codes. Based on such Morton codes, the relative positions and directions of surrounding points can be determined, thereby establishing the search area; points within this area become targets for atlas search. As shown in Fig. 11, there may be no points within the atlas search area.

[0271] When performing an atlas search, the criterion for determining the closest neighbor among neighboring points is distance. When the coordinates of the current point are (x, y, z) and the coordinates of the searched neighbor point are (x', y', z'), the distance D between the two points is given by the formula ( It can be obtained as follows. The point with the smallest distance, i.e., the closest point, can be called the nearest neighbor point, and in atlas search, the three nearest neighbor points are found within the atlas search area. If there are fewer than three points within the atlas search area, the three nearest neighbor points cannot be found, so the process moves to the full search step.

[0272] The encoding method and decoding method according to the embodiments may further include a Morton code scaling-based atlas search step (step 2).

[0273] Down-scaling position information can be efficient when calculating Morton codes. When assigning a Morton code to a point with coordinates (x, y, z), if the Morton code scaling factor is N, the Morton code can be assigned with the point's coordinates downscaled by N. When the function for assigning a Morton code is expressed as mortonAddr(), the Morton code assigned through this Morton code scaling technique is given by the formula ( It can be expressed as ). Here, the coordinate values ​​representing the actual locations of the points do not change; rather, downscaled coordinates are used when assigning Morton codes. Formula ( In ), N corresponds to the numerator and d corresponds to the exponent of 2 in the denominator, and it can be understood as integer downscaling without decimal points. That is, the coordinate values It has the same effect as multiplying by and discarding the decimal point.

[0274] As explained in the aforementioned atlas search (predictor search) step, the atlas search area is set based on Morton codes; when such Morton code scaling is applied, more points can be included within the atlas search area. If more points are included within the atlas search area, the following two effects may occur.

[0275] First, as the number of nearest neighbor candidates increases during atlas search, the likelihood of finding three nearest neighbors without proceeding to a full search increases. By avoiding the time-consuming full search process, the total encoding / decoding time can be reduced. Additionally, by addressing the problem of selecting inaccurate neighbors when candidates are scarce, the accuracy of subsequent predictors can be improved, thereby enhancing the performance of attribute compression.

[0276] The encoding method and decoding method according to the embodiments may further include a step of setting multiple scaling factors for Morton code scaling-based atlas search (step 2-1).

[0277] When performing Morton code scaling, it can be more efficient to use different scaling factors for each axis rather than using the same scaling factor for all axis coordinates. For example, if a Morton code is assigned to a point with coordinates (x, y, z) by setting the scaling factor to (a, b, c), downscaling of 'a' can be applied to x, scaling of 'b' to y, and scaling of 'c' to z. This process is [according to] the formula ( It can be represented through. Formula ( In ), a, b, and c correspond to the numerator, respectively, and d corresponds to the exponent of 2 in the denominator.

[0278] The encoding method and decoding method according to the embodiments may further include a step of setting a point alignment method after Morton code scaling for Morton code scaling-based atlas search (step 2-2).

[0279] If the alignment order between points was based on Morton codes, using the Morton code scaling method can affect the alignment order between points. When coordinate values ​​are downscaled, points with overlapping Morton codes occur despite having different locations. If Morton code-based alignment is performed in this state, the order between points with overlapping Morton codes is the same as the output order of the geometry decoder. In other words, alignment based on Morton code order is performed between points with different Morton codes, while alignment based on the geometry decoder's output order is performed between points with overlapping Morton codes. While this mixed order-based alignment can be effective, other methods of order-based alignment may be effective. It may be effective to set the alignment order to the geometry decoder's output order. It may also be effective to set the alignment order to the Morton code order.

[0280] The encoding method and decoding method according to the embodiments may further include a search area expansion-based atlas search step (step 3).

[0281] When performing an atlas search, expanding the atlas search area can be an efficient method. If the atlas search area expansion factor is N, the size of a single minicube is ( The atlas search area can be expanded by setting it to ). Since the atlas search area corresponds to the size of a Minicube 27, the atlas search area expands as the size of the Minicube increases. The atlas search area expansion factor can be set as follows. The user can set a value in advance and signal it to the decoder. The atlas search area factor can be set by reflecting the density characteristics of the point cloud content input from the encoder. The criterion for determining density may be the number of points within a specific area. The criterion for determining density may be the average of the distances between all points.

[0282] Expanding the atlas search area increases the number of nearest neighbor candidates, thereby having an effect similar to Morton code scaling. This reduces instances of resorting to a full search, thereby increasing encoding / decoding speeds and decreasing the frequency of incorrect nearest neighbor selections.

[0283] The encoding method and decoding method according to the embodiments may further include a multi-region expansion coefficient setting step for a search region expansion-based atlas search step (step 3-1).

[0284] The area expansion factor can also be applied individually for the x, y, and z axes. The area expansion factor for each XYZ axis The size of the ramen and mini cube is ( ...and through this, the atlas search area can be expanded. These coefficients can also be specified by the user as in Step 3 or reflect the characteristics of density.

[0285] The encoding method and decoding method according to the embodiments may further include an axis removal-based atlas search step (step 4).

[0286] When searching an atlas, it may be efficient to remove a point from the nearest neighbor search target if its coordinates differ based on a specific axis. For example, if an axis removal method based on the Z-axis is used, when the current point's coordinates are (x, y, z) and a neighbor point within the atlas search range has coordinates (x', y', z'), if the values ​​of z and z' are different, the point can be excluded from the nearest neighbor candidate without measuring the distance to that neighbor point.

[0287] Using such an axis-removal-based atlas search method can increase encoding and decoding speeds because it eliminates the need to calculate distances between points with different values ​​relative to a specific axis. Furthermore, if the characteristics of the point cloud content include attribute values ​​(such as color or reflection values) that differ significantly regardless of distance relative to a specific axis, the axis-removal-based atlas search method can prevent the selection of inaccurate nearest neighbor points.

[0288] The encoding method and decoding method according to the embodiments may further include a point count-based axis removal step for an axis removal-based atlas search step (step 4-1).

[0289] FIG. 13 shows an encoding device according to embodiments.

[0290] FIG. 13 may include and perform the transmission device (10000) of FIG. 1, a point cloud video encoder (10002), a transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, a predictor search of FIG. 11, downscaling of FIG. 12, an encoder of FIG. 14, LOD generation of FIG. 17, molton code assignment and alignment of FIG. 18, nearest neighbor search of FIG. 19, bitstream and parameter generation of FIG. 20 to 22, predictor search within reference frames of FIG. 23 to 24, encoder of FIG. 25, molton code assignment and alignment-based nearest neighbor search of FIG. 27 to 29, parameter generation of FIG. 30-31, etc.

[0291] The encoding device of FIG. 13 may include memory and at least one processor. At least one processor is configured to perform the operation of each block of FIG. 13.

[0292] FIG. 13 is a block diagram of a PCC data encoder. Each component may correspond to hardware, software, a processor, and / or a combination thereof. PCC data is fed into the encoder and encoded so that a geometry information bitstream and an attribute information bitstream can be output.

[0293] The data input section can read and configure received data (e.g., ply, configuration file, etc.).

[0294] The coordinate system transformation unit can support coordinate system changes, such as changing the xyz axes or transforming from an xyz orthogonal coordinate system to a spherical coordinate system.

[0295] 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 (scale = geometry quantization value) according to the scale setting.

[0296] The spatial partition can be divided into tiles or slices for area-based access or parallel processing of content.

[0297] The geometry encoding unit can encode spatially partitioned geometry information to generate a geometry information bitstream.

[0298] The color conversion processing unit can support attribute type conversion, such as changing RGB colors to YUV.

[0299] The color recalculation unit can predict an attribute value suitable for the changed location when the geometry is scaled and the location information value is changed.

[0300] The attribute information encoding unit can receive the color-restored original attribute information, restored geometry information, and reference frame as inputs, perform encoding, and generate an attribute information bitstream.

[0301] 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.

[0302] Referring to FIG. 13, the point cloud encoding device of the embodiments has a structure that separates input 3D point cloud data into geometry information and attribute information and encodes each independently. The device may be broadly composed of a geometry information encoding path and an attribute information encoding path.

[0303] Data Input Unit: The data input unit receives point cloud data from the outside. The input data may include, for example, geometry data: 3D coordinates (x, y, z) of each point, attribute data: color, reflectance, surface normals of each point, etc., and encoding parameters: encoding control information such as encoding mode, block size, and search range.

[0304] Coordinate Transformation Unit: The coordinate transformation unit transforms the original coordinate system of the input points into a form suitable for encoding. For example, it can convert a global coordinate system to a local coordinate system or perform normalization operations to equalize the spatial distribution of the points. This improves the efficiency of partitioning and encoding in subsequent stages.

[0305] Geometry Encoder: The geometry encoder receives coordinate information of the transformed points and encodes the location information of the points using octree-based spatial partitioning or other hierarchical tree structures. The encoded result is output in the form of a geometry bitstream, which may include information on the existence of points, partition depth, prediction information, etc.

[0306] Attribute Reconstruction and Reference Frame Unit: The attribute reconstruction unit spatially maps or corrects attribute data using already encoded geometry information. The reference frame unit reduces temporal redundancy by referencing the reconstructed attribute information of the previous frame and utilizing it for the prediction of the current frame.

[0307] Color Conversion Unit: The Color Conversion Unit converts input color attributes into a color space with high encoding efficiency, such as RGB to YCbCr. This process reduces statistical correlation and improves compression efficiency during the subsequent encoding process.

[0308] Recoloring Unit: The recoloring unit performs recoloring to balance channels on the converted color data or to improve the quality of the restored color data. For example, it may include operations to compensate for changes in lighting or differences in sampling density between points.

[0309] Attribute Encoder: The attribute encoder receives transformed and readjusted attribute data as input, and outputs an attribute bitstream after undergoing processes such as prediction, transformation, quantization, and entropy coding.

[0310] The encoder can pack the encoded geometry bitstream and attribute bitstream into a single integrated bitstream for transmission or storage.

[0311] A point cloud encoding device can increase compression efficiency by separately optimizing the geometry and attributes of points during encoding, minimize visual quality degradation through color conversion and rebalancing processes, and ensure encoding efficiency and decoding consistency through coordinate system transformation and reference frame management functions.

[0312] FIG. 14 shows an attribute encoder according to embodiments.

[0313] FIG. 14 illustrates the attribute encoding operation of the encoder in FIG. 13.

[0314] The LOD generation unit can generate LODs by receiving segmented point cloud data as input, and point-specific neighbors used for lifting transformation and prediction transformation can be determined. In this case, the LOD generation unit can be executed when LOD parameters are available.

[0315] The lifting transform unit can generate transform coefficients by performing a frequency transform on attribute information through a prediction and update process for each LOD level using the generated LOD. The generated transform coefficients can be quantized and transmitted to the attribute information entropy encoder. Additionally, it can output the restored attribute information by internally performing an inverse transform.

[0316] Predictive transformation can be performed to predict the current point using neighbor points determined during the LOD generation process. The difference between the predicted attribute information and the original attribute information can be referred to as the transformation coefficient, which can be quantized and transmitted to the attribute information entropy encoding unit. Additionally, attribute information can be restored and output through inverse quantization and inverse predictive transformation.

[0317] RAHT can be performed when there are no LOD parameters. RAHT can receive segmented point cloud data as input and perform RAHT (Region Adaptive Hierarchical Transform) to transform it into the frequency domain and generate transformation coefficients. Subsequently, the transformation coefficients can be quantized and transmitted to the attribute information entropy encoding unit.

[0318] Referring to FIG. 14, an attribute encoder according to embodiments is configured to receive attribute information of each point from segmented point cloud data and output the attribute information as a bitstream through a multi-stage encoding process configured hierarchically (Level-of-Detail, hereinafter "LoD"). The attribute encoder may include, for example, an attribute data input unit, an LoD generation unit, an attribute prediction unit, a residual calculation unit, a transformation and quantization unit, and an entropy encoding unit.

[0319] The encoder receives attribute values ​​such as color, reflectance, and surface normals corresponding to each point of the point cloud data segmented by the geometry encoder. The input data is arranged in geometry blocks and is subsequently classified into a multi-resolution-based hierarchical structure by the LoD generation unit.

[0320] Level-of-Detail Generation Unit: The Level-of-Detail Generation Unit generates attribute sets of multiple levels (Levels-of-Detail, LoD) based on the spatial distribution or point density of the point cloud data. For example, the first level (LoD0) is an attribute set for low-resolution (global) representative points that roughly represents the overall structure, while subsequent levels (LoD1 and beyond) progressively improve precision by providing attribute sets for increasingly finer detailed points. This LoD-based configuration enhances the encoding efficiency of large-scale point clouds and enables stepwise reconstruction during decoding.

[0321] The encoder predicts the attributes of the current point based on the attribute values ​​of reference points that have already been encoded within the same or adjacent geometry blocks. In this case, the restoration results of the previous LoD step may also be used as reference information.

[0322] The encoder calculates the difference (residual) between the output predicted attribute values ​​and the actual input attribute values. This residual is used as input for subsequent transformation and quantization processes. Similar to the prediction error in pixel-based images, the residual contributes to improving encoding efficiency in the attribute dimension.

[0323] The encoder performs a transform operation to convert residual data into the frequency domain, and then compresses the data size through quantization.

[0324] The encoder encodes the transformed and quantized residual data into a bitstream based on a statistical probability model. Context-Adaptive Binary Arithmetic Coding (CABAC) or other probability-based encoding techniques may be used as the encoding method. The finally generated attribute information bitstream is integrated with the geometry information bitstream and output.

[0325] The attribute encoder can progressively reduce the complexity of large-scale point clouds through a LoD-based hierarchical structure and efficiently eliminate redundancy in attribute data by applying a prediction-residual-based encoding structure, thereby simultaneously improving encoding efficiency and visual fidelity.

[0326] FIG. 15 shows a decoding device according to embodiments.

[0327] FIG. 15 corresponds to the encoder of FIG. 13 and can follow the reverse process of the encoder.

[0328] FIG. 15 includes operations such as the receiving device (10004) of FIG. 1, the receiver (10005), the point cloud video decoder (10006), the transmission-decoding-rendering (20002-20003-20004) of FIG. 2, the decoder of FIG. 7, the receiving device of FIG. 9, the device of FIG. 10, the predictor search of FIG. 11, the downscaling of FIG. 12, the decoders of FIG. 15 to 16, the LoD generation of FIG. 17, the molton code assignment and alignment of FIG. 18, the nearest neighbor search of FIG. 19, the bitstream and parameter acquisition of FIG. 20 to 22, the predictor search within the reference frame of FIG. 23 to 24, the decoder of FIG. 26, the molton code assignment and alignment-based nearest neighbor search of FIG. 27 to 29, and the parameter acquisition of FIG. 30 to 31.

[0329] FIG. 15 is a block diagram of a PCC data decoder. 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 inputs to the decoder, and PCC data that has been decoded and restored can be output.

[0330] The geometry information decoding unit can receive a geometry information bitstream as input, decode it, and restore the geometry information.

[0331] 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.

[0332] The geometry information conversion inverse quantization processing unit can restore the signaled scale (scale = geometry quantization value) and apply it to the geometry position x, y, and z values ​​of the restored point to restore it.

[0333] Attribute residual information entropy decoding can entropy decode the attribute bitstream.

[0334] The attribute information decoding unit can receive an attribute information bitstream as input, decode it, and restore the attribute information.

[0335] The color inverse conversion processing unit can restore the converted attribute to RGB color.

[0336] 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.

[0337] Referring to FIG. 15, the decoder according to the embodiments restores geometry information and attribute information, respectively, to reconstruct a three-dimensional point cloud.

[0338] Geometry Decoding Unit: The geometry information decoding unit receives an input geometry information bitstream and decodes geometry information by decoding the geometry encoded data contained within the bitstream. The decoded geometry information represents, for example, the spatial distribution of points encoded based on an octree, and reconstructs the location coordinates of each point. According to one embodiment, the decoded geometry information can be used as a reference frame referenced in a subsequent attribute decoding process.

[0339] Attribute Decoding Unit: The attribute decoding unit receives an attribute information bitstream and restores attribute information, such as color and reflectance, of points corresponding to the restored geometry information.

[0340] Color Inverse Transformation Unit: Restores the original color representation by performing the inverse transformation of the color space conversion or prediction-based compression performed during encoding.

[0341] The decoder combines the inversely transformed attribute data with the restored geometry to output attribute information of the point cloud.

[0342] The decoder combines restored geometry information and attribute information to form a reference frame, which can be used in the prediction or interpolation process of subsequent frames. For example, between adjacent frames with high temporal correlation, efficient predictive restoration is performed by referencing the location and attribute values ​​of the restored points.

[0343] The decoder restores point locations from the geometry bitstream and inversely transforms color or reflectance information from the attribute bitstream, and completely restores the 3D point cloud by combining the restored geometry and attribute information. This allows for the reproduction of point cloud data of high visual quality while maximizing transmission efficiency.

[0344] FIG. 16 shows an attribute decoder according to embodiments.

[0345] FIG. 16 illustrates the attribute decoding operation of the decoder of FIG. 15 in detail.

[0346] The attribute information entropy decoding unit can receive an attribute information bitstream as input, decode it, and restore the conversion coefficients.

[0347] The LOD generation unit can generate LODs by receiving restored geometry information as input, and point-by-point neighbors used for lifting transformation and prediction transformation can be determined. In this case, the LOD generation unit can be performed when LOD parameters exist.

[0348] The lifting inverse transform can restore attribute information by inversely transforming the input transformation coefficients and performing frequency inverse transform through the generated LOD and the update and prediction processes for each LOD level.

[0349] Predictive inverse transformation can restore attributes by using neighbor points determined during the LOD generation process to combine the current point's predicted value with the inverse transformed value of the input transformation coefficients.

[0350] Inverse RAHT can restore attribute information by receiving restored geometry information and transformation coefficients as input and performing the inverse process of RAHT.

[0351] Referring to FIG. 16, the detailed configuration of the attribute information recovery unit of FIG. 151 is illustrated in a block diagram, and the process of generating attribute values ​​of points recovered from the attribute bitstream is illustrated step by step.

[0352] The decoder receives an attribute information bitstream as input.

[0353] Entropy Decoding Unit: The entropy decoding unit decodes the input attribute symbols according to an entropy coding method (e.g., arithmetic coding, context-adaptive binary arithmetic coding, etc.). Through this, the original attribute prediction values ​​and residual values ​​are restored.

[0354] The decoder reconstructs the original attribute values ​​using the decoded residuals according to the prediction mode.

[0355] Level-of-Detail (LoD) Generation Unit: Predictively restored attribute information is converted into a hierarchical (Level-of-Detail) structure through the LoD Generation Unit. The LoD Generation Unit classifies the restored data into multiple levels based on the geometry resolution of the point cloud or the complexity of the attributes. For example, it can store coarse-level attributes for low-resolution representation and fine-level attributes for high-resolution visualization separately.

[0356] The decoder combines with the geometry information restored by the geometry restoration unit of FIG. 15 to reconstruct the final point cloud. At this time, the restored attributes are mapped to the position coordinates of each point to provide attributes such as color, brightness, and reflectance in a visualizable form.

[0357] The attribute decoder receives an attribute bitstream as input, performs entropy decoding and predictive restoration, layers the restored attributes at the Level of Detail (LoD) level, and improves visual quality through post-processing filtering to finally output the restored attribute information. Through this structure, efficient point cloud attribute restoration is possible in various rendering environments.

[0358] FIG. 17 shows a LoD generation unit according to embodiments.

[0359] Figure 17 shows the LoD generation section of the decoder in Figure 16.

[0360] The LOD generation unit can receive restored geometry information and output a multi-layered LOD.

[0361] The Morton Code Assignment and Alignment Unit assigns Morton Codes and can then align points according to a predetermined alignment method.

[0362] The subsampling section may perform subsampling on the index list to distinguish current points by LOD.

[0363] The nearest neighbor search unit can find one or more neighboring points for each point based on geometry and store the index of the corresponding point for the prediction and update process of lifting transformation for points classified by LOD.

[0364] Referring to FIG. 17, this is a block diagram illustrating the detailed configuration of a Level of Detail (LoD) generation unit according to embodiments. This configuration represents a process of generating an index list for efficient multi-resolution (Level-of-Detail) representation by analyzing the spatial relationship between points based on restored geometry information.

[0365] The LoD generation unit receives the restored geometry information as input. The geometry information consists of the 3D coordinate values ​​of each point and represents the overall structure of the restored point cloud. In the subsequent steps, the adjacency and spatial hierarchical relationships between points are analyzed based on this geometry information.

[0366] Morton Code Assignment and Sorting Unit: Assigns Morton codes to the coordinate values ​​(x, y, z) of the input points. Morton codes are an indexing method that linearizes spatial points into a Z-order curve, ensuring that spatially adjacent points have similar code values. Subsequently, points are sorted based on the assigned Morton code values ​​to generate a sequential list of points that reflects spatial adjacency. This process serves as foundational data for efficiently performing hierarchical segmentation (e.g., octree-based segmentation) during the LoD generation phase.

[0367] The LoD generation unit generates LoDs by subsampling points assigned Morton codes.

[0368] Nearest Neighbor Search Unit: For each point, searches for nearest neighbors within a certain radius or distance threshold.

[0369] LoD Index List Generation Unit: Finally, based on the search results, a LoD index list corresponding to each level is generated. The LoD generation unit provides a multi-resolution index structure that can be selectively restored depending on the resolution of the point cloud.

[0370] The LoD generation unit receives restored geometry information, assigns Morton codes to each point and sorts them, performs octree-based segmentation, searches for nearest neighbors between points, and uses the results to generate a list of LoD indices for multi-resolution rendering. Through this, the decoder can improve data restoration efficiency and maximize the hierarchical representation and transmission efficiency of attribute information.

[0371] FIG. 18 shows a Morton code assignment and alignment unit according to embodiments.

[0372] FIG. 18 explains the detailed operation of the Morton code assignment and alignment section of the decoder in FIG. 17.

[0373] The Morton Code Assignment and Alignment Unit assigns Morton Codes to the points and can align the points according to a predetermined alignment order.

[0374] In the Morton code assignment and alignment section, Morton codes can be assigned through Morton code scaling. Information regarding the aforementioned scaling coefficients or multiple scaling coefficients can be received as a signal. Morton codes can be assigned by utilizing the received information and the coordinate values ​​of the points.

[0375] The Morton code assignment and alignment unit can align points in one of the following order: mixed order, Morton code order, or geometry decoder output order. Information regarding the alignment order according to the embodiments can be received via signaling. Points can be aligned using the received information and points.

[0376] Referring to FIG. 18, this is a block diagram illustrating the internal configuration of the Morton code assignment and alignment unit of the LoD generation process (for intra-prediction) described in FIG. 17. This configuration details the process of generating a Morton code to assign spatial order to each point from the restored geometry information, and aligning the points based on the code.

[0377] The encoding / decoding method according to the embodiments can select whether to perform Morton code scaling based on the asp_morton_code_scaling_enabled value.

[0378] The encoding / decoding method according to the embodiments can select whether to apply different scaling factors to the x-axis, y-axis, and z-axis respectively based on the multi_scaling_factor_enabled value. If multi_scaling_factor_enabled is 1, different downscaling is applied to the x, y, and z coordinate values, and if multi_scaling_factor_enabled is 0, the same downscaling is applied to the coordinate values.

[0379] The encoding / decoding method according to the embodiments can assign Morton codes to points based on the aforementioned downscaling.

[0380] The encoding / decoding method according to the embodiments may select whether to use a different sorting method when inter-prediction-based Morton code scaling is performed based on the different_sorting_enabled value. If the different_sorting_enabled value is 0, points are sorted based on the Morton code order when the Morton codes between points are different, and points are sorted according to the geometry decoder output order when the Morton codes between points are the same. If the different_sorting_enabled value is 1 and there is a Morton_code, points are sorted based on the Morton code order, and if there is no Morton_code, points are sorted based on the geometry decoder output order.

[0381] The Morton code assignment and alignment unit receives the restored geometry coordinates as input, normalizes the coordinates, generates a Morton code by intersecting the bits of each axis, and then aligns the points according to the generated Morton code to output a list of aligned points that reflect spatial adjacency. Through this process, the spatial hierarchical structure of the point cloud can be efficiently managed, and the computational efficiency of LoD generation and neighbor search can be maximized.

[0382] FIG. 19 shows a nearest neighbor search unit according to embodiments.

[0383] FIG. 19 illustrates in detail the nearest neighbor search section of the LoD generation process of FIG. 17.

[0384] The nearest neighbor search unit can set an atlas search area by utilizing the modal codes of each point. The nearest neighbor search unit performs an atlas search using the set atlas search area, and if three nearest neighbor points are not found during the atlas search, it can perform a full search. The nearest neighbor search unit can expand the atlas search area through an atlas search area expansion method. Information regarding a predetermined atlas search area expansion coefficient can be received via signaling through the method according to the embodiments. The atlas area can be expanded by utilizing the received information.

[0385] The nearest neighbor search unit can utilize an axis removal method to exclude points within the atlas search area from the neighbor search if their reference values ​​for a specific axis (one of x, y, or z) differ. It receives signals regarding the axis to be removed and the conditions for executing the axis removal method. The axis removal method can then be executed using this received information.

[0386] Referring to FIG. 19, the encoding / decoding method according to the embodiments can derive an atlas search region from subsampled points.

[0387] The encoding / decoding method according to the embodiments can select whether to perform an atlas search range expansion technique based on the enlarge_atlas_searchrange_enabled value.

[0388] The encoding / decoding method according to the embodiments can select whether to expand the atlas search area based on the multi_atlas_enlarge_factor_enabled value. If the multi_atlas_enlarge_factor_enabled value is 1, the atlas search area is expanded based on the multi-area expansion factor for x, y, and z received, and if the multi_atlas_enlarge_factor_enabled value is 0, the atlas search area is expanded based on a single atlas area expansion factor.

[0389] The encoding / decoding method according to the embodiments can select whether to perform an axis removal technique based on the discard_different_coordinate_neighbor_enabled value.

[0390] The encoding / decoding method according to the embodiments can exclude an axis from neighbor point search targets if the received axis reference coordinate value among the x, y, and z axes is different, based on the number of points within atlas search range > number of points for coordinate discard.

[0391] The encoding / decoding method according to the embodiments can search the atlas based on the methods described above, and if the number of nearest neighbors is less than 3, search the entire area again to generate a final predictor (predictor).

[0392] FIG. 20 shows a bitstream according to embodiments.

[0393] The encoding method according to the embodiments generates a bitstream of FIG. 20, and the decoding method according to the embodiments decodes point cloud data in the bitstream based on parameter information in FIG. 20.

[0394] To perform the embodiments, relevant information may be signaled. The signaling information according to the embodiments may be used at a transmitting end or a receiving end, etc. The encoded point cloud configuration is as follows. A point cloud data encoder that performs geometry encoding and / or attribute encoding processes may generate an encoded point cloud (or a bitstream containing a point cloud) as follows. Additionally, signaling information regarding the point cloud data may be generated and processed by a metadata processing unit of a point cloud data transmitting device and included in the point cloud as follows.

[0395] Each abbreviation means the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning. SPS: Sequence Parameter Set, GPS: Geometry Parameter Set, APS: Attribute Parameter Set, TPS: Tile Parameter Set, Geom: Geometry bitstream = geometry slice header + [geometry 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.

[0396] FIG. 21 shows the syntax of an attribute parameter set within a bitstream according to embodiments.

[0397] Parameter information for a high-speed nearest neighbor search technique for LOD generation can be included in the Attribute Parameter Set (APS).

[0398] The attribute parameter set ID (aps_attr_parameter_set_id) provides an APS identifier that other syntax elements can reference. The aps_attr_parameter_set_id value ranges from 0 to 15 (inclusive).

[0399] The sequence parameter set ID (aps_seq_parameter_set_id) represents the sps_seq_parameter_set_id value for the active SPS. The aps_seq_parameter_set_id value is in the range from 0 to 15 (inclusive).

[0400] Morton Code Scaling Enable (aps_morton_code_scaling_enabled): A flag that selects how to perform Morton code scaling.

[0401] aps_extension_present indicates whether the aps_extension_data syntax element is present in the APS syntax structure. In bitstreams conforming to this version of this document, aps_extension_present is 0.

[0402] Prediction with distribution_enabled indicates whether prediction coefficients are derived based on the spatial distribution of the predictor variable (if 1) or not (if 0). If Prediction with distribution_enabled is not present, it is inferred as 0.

[0403] Enlarge Atlas Search Range Enable (enlarge_atlas_searchrange_enabled): A flag that selects whether to perform the atlas search range expansion technique.

[0404] Discard Different Coordinate Neighbor Enabled: A flag that selects whether to perform the axis removal technique.

[0405] FIG. 22 shows the attribute data unit header syntax within a bitstream according to embodiments.

[0406] Parameter information for a high-speed nearest neighbor search method for LOD generation may be included in the Attribute Data Unit Header.

[0407] The attribute parameter set ID (adu_attr_parameter_set_id) represents the ASP by aps_attr_parameter_set_id.

[0408] Time ID (adu_temporal_id): Represents the time ID associated with the attribute data unit.

[0409] Different_sorting_enabled: A flag that selects whether to use a different sorting method when Morton code scaling is performed.

[0410] Multi-scaling factor enable: A flag that selects whether to apply different scaling factors to the x-axis, y-axis, and z-axis.

[0411] Morton Code Scaling Denominator (morton_code_scaling_denominator): This is the value corresponding to d in the aforementioned formulas. Formula (Morton Code Scaling Technique: ) and formula (Morton code scaling technique using multiple scaling factors: ).

[0412] Sort method after Morton code scaling (sorting_method_after_morton_code_scaling): Indicates the sorting method before input to the subsampling unit when Morton code scaling is performed. 0 = sorting based on the order output from the geometry decoder, 1 = sorting based on Morton code without applying Morton code scaling.

[0413] Multi_morton_code_scaling_factor_xyz[k]): The Morton code scaling factor to apply to the k-th xyz coordinate. Represents the scaling factor to be applied to the x-coordinate when k==0, the y-coordinate when k==1, and the z-coordinate when k==2. Formula (Morton code scaling technique using multiple scaling factors: ) reference.

[0414] Single Morton Code Scaling Factor: A Morton code scaling factor applied universally to xyz coordinates. Formula (Morton code scaling technique: ) reference.

[0415] Multi-atlas expansion factor (multi_atlas_enlarge_factor_xyz[k]): Refers to the multi-atlas search area expansion factors, which increase the size of the minicube from (2,2,2) to (2^(multi_atlas_enlarge_factor_xyz[0]), 2^(multi_atlas_enlarge_factor_xyz[1]), 2^(multi_atlas_enlarge_factor_xyz[2])).

[0416] Single Atlas Enlarge Factor: Refers to a single atlas search area expansion factor, which increases the size of the minicube from (2,2,2) to (2^(single_atlas_enlarge_factor),2^(single_atlas_enlarge_factor),2^(single_atlas_enlarge_factor)). Refer to the Atlas Search Step Based on Search Area Expansion.

[0417] Number of points for coordinate discard: Specifies the condition for performing coordinate discard when using the coordinate discard method. The coordinate discard method is performed only if the number of points within the atlas search area is greater than number_of_points_for_coordinate_discard. Refer to the Coordinate Discard-based Atlas Search step.

[0418] Coordinate_to_discard: Indicates which of the xyz axes to remove when using the axis removal method. 0= x-axis, 1= y-axis, 2= z-axis.

[0419] The encoding / decoding method according to the embodiments may include an inter-prediction-based atlas search step (step 1), a Morton code scaling-based reference atlas search step (step 2), an atlas search area setting step based on a scaling factor (step 2-1), an atlas search reference point selection step in a reference frame (step 3), a search area expansion-based reference atlas search step (step 4), an axis removal-based reference atlas search step (step 5), etc.

[0420] Inter-prediction based atlas search step (Step 1)

[0421] When performing inter-prediction-based point cloud compression for multi-frame point cloud compression, inter-prediction can be utilized in atlas search. The atlas search area described in the aforementioned atlas search step can be set in the reference frame in the same way, and subsequently, points existing in the reference frame can be used as neighbor points to generate predictors for points in the current frame. At this time, the process of atlas search in the reference frame is identical to the atlas search step described above. The atlas search method and search area of ​​the reference frame can be referred to as the reference atlas search method and the reference atlas search area, respectively.

[0422] Morton Code Scaling-Based Reference Atlas Search Step (Step 2)

[0423] When obtaining Morton codes from reference frames, it may be efficient to downscale position information using the method presented in the aforementioned Morton code scaling-based atlas search step. Here, the scale factor, which determines the degree of scaling, can be applied identically to both the current frame and the reference frame, or applied differently to each as needed. A flag can be used to specify whether the same downscaling as the current frame is applied; if the flag is true, there is no need to transmit the scale factor separately, which helps reduce the size of the bitstream. If the flag is false, the reference frame and the current frame are downscaled differently.

[0424] The scaling factor of the reference frame can be a different value from the scaling factor of the current frame.

[0425] To reduce memory usage, the scaling factor of the reference frame can be set to a low value.

[0426] Since the accuracy of a predictor utilizing neighbor points selected from a reference frame can be high, the scaling factor of the reference frame can be set to a large value.

[0427] Even if the multiple scaling coefficients presented in the aforementioned step for setting multiple scaling coefficients are set in the current frame, a single scaling coefficient may be set in the reference frame, and the opposite may be applied in the same way.

[0428] The point realignment method presented in the point alignment method setting step after the aforementioned Morton code scaling may have different current frames and reference frames.

[0429] If the reference frame applies the same downscaling as the current frame, a flag specifying this can be transmitted to the duplexer.

[0430] Step for setting the atlas search area based on the scaling factor (Step 2-1)

[0431] In the existing reference atlas navigation method, the atlas navigation range of the current frame and the atlas navigation range of the reference frame are set differently according to preset values. Generally, the atlas navigation range of the current frame is set larger than the atlas navigation range of the reference frame. However, these navigation ranges can be preset differently by the user according to their needs based on the scaling value.

[0432] If the scaling factor of the reference frame is less than or equal to a specific value N and the current atlas search range is larger than the reference atlas search range, the user can pre-set the reference atlas search range to be equal to the current frame's atlas search range. This allows securing a reference atlas search range of a certain size or larger even without significant Morton code scaling, which can be effective for generating better predictors.

[0433] If the scaling factor of the reference frame is greater than or equal to a specific value N and the current atlas search range is larger than the reference atlas search range, the user can pre-set the current frame's atlas search range to be equal to the reference frame's atlas search range. This can be effective in reducing wasted memory usage by avoiding the need to unnecessarily over-scale the atlas search range when Morton code scaling is already extensive.

[0434] FIG. 23 illustrates an atlas search within a reference frame according to embodiments.

[0435] Step 3: Selecting the atlas navigation reference point in the reference frame

[0436] When performing a reference atlas search, a reference point is required to define the search range. Since a point in the reference frame may not exist at the exact same location as a point in the current frame, a method is needed to select the reference point. Due to different alignment methods, the current frame and the reference frame may have different indices pointing to similar locations, even if they are situated in similar positions.

[0437] FIG. 23 illustrates an example of choosing the same-index reference point for atlas search in a 2D plane.

[0438] As shown in FIG. 23, it may be effective to set a point in the reference frame that has the same index as a point in the current frame as the reference point. Using the same index reduces the amount of computation required for distance calculation, which can be effective in terms of encoding / decoding speed. When global motion compensation is applied to the reference frame, the positions of the points in the reference frame become similar to those in the current frame, so it may be advantageous to select a point with the same index as the reference point.

[0439] FIG. 24 illustrates an atlas search within a reference frame according to embodiments.

[0440] FIG. 24 illustrates an example of choosing the closest reference point for atlas search in a 2D plane.

[0441] As shown in FIG. 24, it may be effective to set the point in the reference frame closest to the point in the current frame as the reference point. This can be effective in terms of the accuracy of the reference atlas search area using the closest location. Since the characteristics of points with the same index in the current frame and the reference frame may differ when global motion compensation is not applied to the reference frame, it may be advantageous to select the closer point as the reference point through distance calculation.

[0442] Search area expansion-based reference atlas search step (step 4)

[0443] When searching a reference atlas, it may be efficient to expand the reference atlas search area through the method described in the search area expansion-based atlas search step.

[0444] The atlas search area expansion factor can differ between the current frame and the reference frame.

[0445] To reduce memory usage, the atlas search area expansion factor of the reference frame can be set to a low value.

[0446] Since the accuracy of a predictor utilizing neighbor points selected from a reference frame can be high, the search area expansion factor of the reference frame can be set to a large value.

[0447] Even if the multi-region expansion factor described in the multi-region expansion factor setting step is used in the current frame, a single atlas search area expansion factor may be used in the reference frame, and vice versa.

[0448] If the reference frame uses the same atlas search area expansion factor as the current frame, this can be specified via a flag.

[0449] Since the accuracy of a predictor utilizing neighbor points selected from a reference frame can be high, the scaling factor of the reference frame can be set to a large value.

[0450] Axis removal-based reference atlas search step (Step 5)

[0451] When searching a reference atlas, the axis removal method described in the axis removal-based atlas search step can be used. It may be efficient to expand the reference atlas search area through the method described in the search area expansion-based atlas search method.

[0452] The axis to be removed can have different current frames and reference frames.

[0453] The point count-based axis removal method presented in the point count-based axis removal step can also be used when searching the reference atlas, and can be applied independently of the point count-based axis removal method applied to the current frame.

[0454] If the axis to be removed is the same as the current frame and the reference frame, this can be specified via a flag.

[0455] Steps 1 through 5 described above can be performed by an encoder such as that shown in FIG. 13. Refer to the description of FIG. 13 for an explanation of the encoder operation.

[0456] FIG. 25 shows attribute encoding according to embodiments.

[0457] The attribute decoding (inter) of FIG. 25 is very similar to the attribute decoding (intra) of FIG. 14. Refer to the explanation of FIG. 14 for common details.

[0458] The LOD generation unit can generate LODs by receiving segmented point cloud data as input, and point-specific neighbors used for lifting transformation and prediction transformation can be determined. In this case, the LOD generation unit can be executed when LOD parameters are available.

[0459] When neighbors are determined for each point in the LOD generation section, an attribute reference frame can be received as input for more efficient neighbor search, and the points of the attribute reference frame can also be utilized.

[0460] The lifting transform unit can generate transform coefficients by performing a frequency transform on attribute information through a prediction and update process for each LOD level using the generated LOD. The generated transform coefficients can be quantized and transmitted to the attribute information entropy encoder. Additionally, it can output the restored attribute information by internally performing an inverse transform.

[0461] Predictive transformation can be performed to predict the current point using neighbor points determined during the LOD generation process. The difference between the predicted attribute information and the original attribute information can be referred to as the transformation coefficient, which can be quantized and transmitted to the attribute information entropy encoding unit. Additionally, attribute information can be restored and output through inverse quantization and inverse predictive transformation.

[0462] RAHT can be performed when there are no LOD parameters. RAHT can receive segmented point cloud data as input and perform RAHT (Region Adaptive Hierarchical Transform) to transform it into the frequency domain and generate transformation coefficients. Subsequently, the transformation coefficients can be quantized and transmitted to the attribute information entropy encoding unit.

[0463] Steps 1 through 5 described above can be performed by a decoder such as that shown in FIG. 15. Refer to the description of FIG. 15 for a description of the decoder operation.

[0464] FIG. 26 illustrates attribute decoding according to embodiments.

[0465] The attribute decoding (inter) of FIG. 26 is very similar to the attribute decoding (intra) of FIG. 14. For common details, refer to the explanation of FIG. 16.

[0466] The attribute information entropy decoding unit can receive an attribute information bitstream as input, decode it, and restore the conversion coefficients.

[0467] The LOD generation unit can generate LODs by receiving restored geometry information as input, and point-by-point neighbors used for lifting transformation and prediction transformation can be determined. In this case, the LOD generation unit can be performed when LOD parameters exist.

[0468] When neighbors are determined for each point in the LOD generation section, an attribute reference frame can be received as input for more efficient neighbor search, and the points of the attribute reference frame can also be utilized.

[0469] The lifting inverse transform can restore attribute information by inversely transforming the input transformation coefficients and performing frequency inverse transform through the generated LOD and the update and prediction processes for each LOD level.

[0470] Predictive inverse transformation can restore attributes by using neighbor points determined during the LOD generation process to combine the current point's predicted value with the inverse transformed value of the input transformation coefficients.

[0471] Inverse RAHT can restore attribute information by receiving restored geometry information and transformation coefficients as input and performing the inverse process of RAHT.

[0472] Referring to FIG. 17, the encoding / decoding method according to the embodiments can generate LoD.

[0473] The Morton code assignment and alignment unit can assign a Morton code to a reference frame and then align points according to a predetermined alignment method.

[0474] The subsampling section may perform subsampling on the index list of the reference frame to distinguish reference points by LOD.

[0475] The nearest neighbor search unit can find one or more neighboring points based on geometry for each point and store the index of the corresponding point for the prediction and update process of lifting transformation for points classified by LOD. At this time, points from a subsampled reference frame may also be used to find neighboring points.

[0476] In the Morton Code Assignment and Alignment section, Morton codes can be assigned to reference points, and points can be aligned according to a predetermined alignment order.

[0477] In the Morton code assignment and alignment section, Morton codes can be assigned to reference frames through Morton code scaling (refer to the Morton code scaling-based reference atlas search method). Information regarding scaling factors or multiple scaling factors can be signaled and known through the method described in the Morton code scaling-based reference atlas search method; if a flag indicating that the same scaling as the current frame is applied is true, additional signaling is unnecessary. Morton codes can be assigned by utilizing the received information and the point coordinate values.

[0478] In the Morton code assignment and alignment section, points in a reference frame can be aligned in one of the following order: mixed order (see Morton code scaling-based reference atlas search method), Morton code order, or geometry decoder output order. Information regarding the alignment order can be signaled and known through the method described in the Morton code scaling-based reference atlas search method. Points can be aligned by utilizing the received information and the points.

[0479] FIG. 27 shows a molton code assignment and alignment unit according to embodiments.

[0480] FIG. 27 illustrates the operation of the Morton code assignment and alignment unit for the reference frame for the aforementioned inter-prediction.

[0481] The encoder and decoder according to the embodiments determine whether to perform Morton code scaling of the reference frame based on the value of inter_morton_code_scaling_enabled. They determine whether to perform the same scaling as the current frame based on the value of same_morton_scale_asintra. If inter_morton_code_scaling_enabled and same_morton_scale_asintra are true, they calculate the Morton code based on the same downscaling as the current frame and assign the Morton code to the points.

[0482] The encoder and decoder according to the embodiments determine whether to apply different scaling factors to the x-axis, y-axis, and z-axis respectively in inter-prediction-based Morton code scaling based on the inter_multi_scaling_factor_enabled value. If the inter_multi_scaling_factor_enabled value is true, Morton codes are calculated based on different downscaling for x, y, and z of the reference coordinate values, and Morton codes are assigned to the points. If the inter_multi_scaling_factor_enabled value is false, Morton codes are calculated by applying the same downscaling to the reference coordinate values, and Morton codes are assigned to the points.

[0483] The encoder and decoder according to the embodiments determine whether to use a different sorting method when inter-prediction-based Morton code scaling is performed, based on the inter_different_sorting_enabled value. If the inter_different_sorting_enabled value is false, points are sorted based on the Morton code order when the Morton codes between points are different, and points are sorted according to the geometry decoder output order when the Morton codes between points are the same.

[0484] The encoder and decoder according to the embodiments determine whether to select the same sorting method as the current frame based on the same_sorting_asintra value. If the same_sorting_asintra value is true, the points are sorted using the same sorting method as the current frame. If the same_sorting_asintra value is false, the points are sorted according to the Morton code order or according to the geometry decoder output order.

[0485] FIG. 28 shows a nearest neighbor search unit according to embodiments.

[0486] The intra-neighbor search unit can perform atlas search and total search in the current frame.

[0487] In the intra-neighbor search section, the atlas search method may vary depending on the various methods described above.

[0488] The inter-neighbor search unit can perform reference atlas search and full search in reference frames.

[0489] FIG. 29 shows an inter-neighbor search unit according to embodiments.

[0490] FIG. 29 explains the detailed operation of the inter-neighbor search unit of FIG. 28.

[0491] In the Inter Neighbor Search section, the reference atlas search area can be set by utilizing the Morton codes of each point.

[0492] In the Inter-neighbor search section, a reference atlas search is performed using the configured reference atlas search area, and if a neighbor point closer than the neighbor points found in the Intra-neighbor search section is found, the existing neighbor point can be replaced.

[0493] In the Inter-neighbor search section, even if three neighbor points are found, if a better neighbor point search is possible, a full search in the reference frame can be performed.

[0494] In the Inter-neighbor search section, the reference atlas search area can be expanded through the reference atlas search area expansion method. Information regarding the defined atlas search area expansion coefficient can be received via signaling through the method described in the search area expansion-based reference atlas search method. The atlas area can be expanded by utilizing the received information.

[0495] In the Inter-neighbor search section, an axis removal method can be utilized to exclude points within the reference atlas search area from the neighbor search target if their reference values ​​for a specific axis (one of x, y, or z) differ. Through the method described in the axis removal-based reference atlas search method, information regarding the axis to be removed and the conditions for executing the axis removal method can be received as signals. The axis removal method can be used by utilizing the received information.

[0496] Referring to FIG. 29, the encoding / decoding method according to the embodiments can set a reference atlas search area from sub-sampled points. Based on the same_index_reference_point value, a criterion for setting a reference atlas search reference point can be selected. For example, if the same_index_reference_point value is 1, a reference point having the same index as the current point in the reference frame can be selected, and if the same_index_reference_point value is 0, the nearest reference point can be selected.

[0497] The encoding / decoding method according to the embodiments can select whether to perform an inter-prediction-based reference atlas search range expansion technique based on the inter_enlarge_atlas_searchrange_enabled value.

[0498] The encoding / decoding method according to the embodiments can select whether to use the same reference atlas search area expansion factor as the current frame based on the same_atlas_enlarge_factor_astintra value. If the same_atlas_enlarge_factor_astintra value is 1, the reference search area can be expanded based on the same atlas expansion factor as the current frame.

[0499] The encoding / decoding method according to the embodiments can select whether to use multiple atlas search area expansion factors in the reference atlas search area expansion technique based on the inter_multi_atlas_enlarge_factor_enabled value. If the inter_multi_atlas_enlarge_factor_enabled value is 1, the atlas search area can be expanded based on multiple area expansion factors for x, y, and z received. If the inter_multi_atlas_enlarge_factor_enabled value is 0, the atlas search area can be expanded based on a single atlas area expansion factor.

[0500] The encoding / decoding method according to the embodiments can select whether to perform an inter-prediction-based axis removal technique based on the inter_discard_different_coordinate_neighbor_enabled value.

[0501] The encoding / decoding method according to the embodiments can select whether to apply the same axis removal method as the current frame based on the same_discard_coordinate_asintra value. If the same_discard_coordinate_asintra value is 1, the same axis removal technique as the current frame can be applied to the reference frame.

[0502] The encoding / decoding method according to the embodiments can exclude the axis from neighbor point search targets if the received reference coordinate value among the z, y, and z axes is different when inter_number of points within atlas search range > number of points for coordinate discard.

[0503] Through the process described above, the encoding / decoding method according to the embodiments can search the reference atlas and search the entire reference frame.

[0504] FIG. 30 shows the syntax of a set of attribute parameters in a bitstream according to embodiments.

[0505] Parameter information for a high-speed nearest neighbor search technique for inter-prediction-based LOD generation can be added to the Attribute Parameter Set.

[0506] The attribute parameter set ID (aps_attr_parameter_set_id) provides an APS identifier that other syntax elements can reference. The aps_attr_parameter_set_id value ranges from 0 to 15 (inclusive).

[0507] The sequence parameter set ID (aps_seq_parameter_set_id) represents the sps_seq_parameter_set_id value for the active SPS. The aps_seq_parameter_set_id value is in the range from 0 to 15 (inclusive).

[0508] aps_extension_present indicates whether the aps_extension_data syntax element is present in the APS syntax structure.

[0509] The attribute inter-prediction-enabled (attr_inter_prediction_enabled) indicates whether to use inter-prediction for attribute coding in point clouds (if 1) or not (if 0). If attr_inter_prediction_enabled is not present, it is inferred to 0.

[0510] Inter_morton_code_scaling_enabled: A flag that selects whether to perform Morton code scaling on the reference frame.

[0511] Same_index_reference_point: This is a flag that selects the criterion for setting the reference atlas search reference point. If this value is 1, it indicates selecting a reference point in the reference frame that has the same index as the current point; if it is 0, it indicates selecting the nearest reference point. (Refer to the method for selecting the atlas search reference point in a reference frame.)

[0512] same_morton_scale_asIntra: A flag that selects whether to perform scaling identical to the current frame.

[0513] Same_sorting_asIntra: A flag that selects whether to select the same sorting method as the current frame.

[0514] Same_atlas_enlarge_factor_asIntra: A flag that selects whether to use the same reference atlas search area expansion factor as the current frame.

[0515] Same_discard_coordinate_asIntra: A flag that selects whether to apply the same axis removal method as the current frame.

[0516] Prediction with distribution enable (prediction_with_distribution_enabled) indicates whether prediction coefficients are derived based on the spatial distribution of the predictor variable (if 1) or not (if 0). If prediction_with_distribution_enabled is not present, it is inferred to be 0.

[0517] Inter-enlarged atlas search range enable (inter_enlarge_atlas_searchrange_enabled): A flag that selects whether to perform the inter-predictive reference atlas search range expansion technique.

[0518] Inter-discard different_coordinate_neighbor_enabled: A flag that selects whether to perform the inter-prediction-based axis discarding technique.

[0519] FIG. 31 shows the attribute data unit header syntax within a bitstream according to embodiments.

[0520] Parameter information for a high-speed nearest neighbor search method for inter-prediction-based LOD generation can be added to the Attribute Data Unit Header.

[0521] The attribute parameter set ID (adu_attr_parameter_set_id) represents the active APS as aps_attr_parameter_set_id.

[0522] The time ID (adu_temporal_id) represents the time ID of the frame associated with the attribute data unit.

[0523] Inter-different sorting_enabled: A flag that selects whether to use different sorting methods when inter-prediction-based Morton code scaling is performed.

[0524] Inter multi-scaling factor enable (inter_multi_scaling_factor_enabled): A flag that selects whether to apply different scaling factors to the x-axis, y-axis, and z-axis respectively in inter-prediction-based Morton code scaling.

[0525] Intermorton Code Scaling Denominator: Formula( ) and formula( It is the value corresponding to d of ).

[0526] Inter-sorting method after Morton code scaling): Represents the sorting method before subsampling when performing inter-prediction-based Morton code scaling. It can be represented as 0 = sorting based on the order output from the geometry decoder, and 1 = sorting based on Morton code without applying Morton code scaling.

[0527] Inter-multi_morton_code_scaling_factor_xyz[k]): The Morton code scaling factor to apply to the k-th xyz coordinate in inter-prediction-based Morton code scaling. Represents the scaling factor to be applied to the x-coordinate when k==0, the y-coordinate when k==1, and the z-coordinate when k==2 ( Formula ( )reference)

[0528] Inter_single_morton_code_scaling_factor: Represents the Morton code scaling factor applied commonly to xyz coordinates in inter-prediction-based Morton code scaling. (Formula reference)

[0529] Inter-multi atlas expansion factor (inter_multi_atlas_enlarge_factor_xyz[k]): Refers to the multi-atlas search area expansion factors in the reference atlas search area expansion technique, and can increase the size of the minicube from (2,2,2) to (2^(inter_multi_atlas_enlarge_factor_xyz[0]), 2^(inter_multi_atlas_enlarge_factor_xyz[1]), 2^(inter_multi_atlas_enlarge_factor_xyz[2])).

[0530] Inter-single atlas enlarge factor: Refers to a single atlas search area expansion factor in the reference atlas search area expansion technique. It allows the size of a minicube to be increased from (2,2,2) to (2^(inter_single_atlas_enlarge_factor),2^(inter_single_atlas_enlarge_factor),2^(inter_single_atlas_enlarge_factor)). (See Search Area Expansion-based Reference Atlas Search Method)

[0531] Number of points for inter-axis removal (inter_number_of_points_for_coordinate_discard): Specifies the condition for performing axis removal when using the inter-prediction-based axis removal method. The axis removal method is performed only if the number of points within the reference atlas search area of ​​the reference frame is greater than inter_number_of_points_for_coordinate_discard (see Axis Removal-Based Reference Atlas Search Method).

[0532] Inter-coordinate to discard axis: Indicates the axis to be removed among the xyz axes when using the inter-prediction-based axis removal method. It can be represented as 0= x-axis, 1= y-axis, 2= z-axis.

[0533] FIG. 32 illustrates a encoding method according to embodiments.

[0534] The method according to the embodiments may include the step of encoding geometry data of point cloud data (S3200) and / or the step of encoding attribute data of point cloud data (S3210), etc.

[0535] The encoding method of FIG. 32 may include steps such as searching an atlas as an intra-prediction, searching an atlas based on Morton code scaling, applying multiple scaling factors, deriving a point alignment method after Morton code scaling, searching an atlas by expanding the search area, deriving multiple area expansion factors, searching an atlas based on axis removal, and / or removing axes based on the number of points. The encoding method of FIG. 32 includes the encoding method of FIG. 13, attribute encoding of FIG. 14, bitstream and parameter generation of FIG. 20 to 22, etc.

[0536] The encoding method of FIG. 32 may include steps such as searching an atlas as inter-prediction, searching a reference atlas based on Morton code scaling, deriving an atlas search area based on a scaling factor, deriving a reference point for searching an atlas within a reference frame, searching a reference atlas by expanding the search area, and / or searching a reference atlas based on axis removal. The encoding method of FIG. 32 may include searching an atlas in a reference frame as in FIG. 23 to 24, attribute encoding of FIG. 25, assigning a Morton code of FIG. 27, nearest neighbor search of FIG. 28, inter-neighbor search of FIG. 29, bitstream and parameter generation of FIG. 30 to 31, etc.

[0537] The encoding method of FIG. 32 can be performed by an encoding device. An encoding device according to embodiments includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data.

[0538] The embodiments further include a computer-readable storage medium for storing a bitstream generated by the method according to FIG. 32.

[0539] The embodiments further include a method comprising the steps of: acquiring a bitstream for point cloud data; generating the bitstream based on the steps of encoding geometry data of the point cloud data and encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0540] FIG. 33 illustrates a decoding method according to embodiments.

[0541] The method according to the embodiments may include the step of decoding geometry data of point cloud data in a bitstream (S3300) and / or the step of decoding attribute data of point cloud data (S3310), etc.

[0542] With respect to intra-prediction, referring together with FIGS. 16 to FIGS. 1. Attribute Decoding (LoD), the step of decoding attribute data (S3310) further includes the step of generating a Level of Detail (LoD) for point cloud data, and the step of generating the LoD may include: the step of assigning a molton code to the point cloud data; the step of aligning the point cloud data; the step of generating LoDs by subsampling the point cloud data; and the step of searching for the nearest neighbors for a point based on the LoDs.

[0543] With respect to intra-prediction, referring together with FIG. 18 'aps_morton_code_scaling_enabled', the step of assigning a Morton code includes: a step of downscaling the point cloud data based on the Morton code scaling flag (aps_morton_code_scaling_enabled'), and the same downscaling may be applied to the coordinate values ​​of the point cloud data, or different downscaling may be applied to the coordinate values ​​of the point cloud data.

[0544] With respect to intra-prediction, referring together with APS 'aps_morton_code_scaling_enabled' in FIG. 21, the bitstream may include a Morton code scaling flag (aps_morton_code_scaling_enabled) indicating whether the position for the attribute data is downscaled during the Morton code generation process.

[0545] In relation to intra-prediction, if the point alignment method after the aforementioned Morton code scaling is referenced together, point cloud data is aligned based on the downscaled Morton code order, and points having the same Morton code can be aligned based on the output order of geometry decoding.

[0546] With respect to inter-prediction, referring together with attribute decoding (LoD) of FIGS. 26 and FIGS. 27, the step of decoding attribute data (S3310) further includes the step of generating a Level of Detail (LoD) for point cloud data within a reference frame based on a reference frame for a current frame containing point cloud data, and the step of generating the LoD may include: the step of assigning a molton code to the point cloud data; the step of aligning the point cloud data; the step of generating LoDs by subsampling the point cloud data; and the step of searching for the nearest neighbors for a point based on the LoDs.

[0547] With respect to inter-prediction, referring together with FIG. 27 'inter_morton_code_scaling_enabled', the step of assigning a Morton code includes: a step of downscaling the point cloud data based on the Morton code scaling flag ('inter_morton_code_scaling_enabled'), and the same downscaling may be applied to the coordinate values ​​of the point cloud data, or different downscaling may be applied to the coordinate values ​​of the point cloud data.

[0548] Regarding inter-prediction, with reference to FIG. 30 'inter_morton_code_scaling_enabled', the bitstream may include a Morton code scaling flag indicating whether the position of attribute data within the reference frame is downscaled during the Morton code generation process.

[0549] aps_morton_code_scaling_enabled and / or inter_morton_code_scaling_enabled according to the embodiments may be referred to as 'morton_code_scaling_enabled'.

[0550] Regarding inter-prediction, when referring together to the aforementioned Morton code scaling-based reference atlas search method and FIG. 30 'same_morton_scale_asIntra', based on information indicating whether the downscaling coefficients within the bitstream are the same, the downscaling coefficient for the point cloud data within the reference frame may be the same as or different from the downscaling coefficient for the point cloud data within the current frame.

[0551] In relation to inter-prediction, if the method for selecting an atlas search reference point within a reference frame described in FIGS. 23 and 24 is referenced together, a point within the reference frame may be selected based on the index of a point within the current frame, or a point within the reference frame may be selected based on the position of a point within the current frame.

[0552] The decoding method of Fig. 33 can follow the inverse process of the encoding method of Fig. 32. The decoding method of Fig. 33 can be performed by a decoding device.

[0553] A decoding device according to the embodiments includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: decode geometry data of point cloud data within a bitstream; and decode attribute data of point cloud data.

[0554] The PCC encoding method, PCC decoding method, and signaling method of the embodiments described above can provide the following effects. Compressing the attributes of a point cloud containing a vast amount of information takes a significant amount of time. In particular, the generation of LODs performed during the attribute compression process involves searching for the nearest neighbor, which significantly slows down the encoding / decoding speed. In environments such as autonomous driving that require accurate and low-latency encoding / decoding, such low encoding / decoding speeds become a problem.

[0555] These embodiments support a high-speed nearest neighbor search method that can be utilized in the LOD generation process of point cloud attribute compression. These embodiments support methods to increase the speed and accuracy of LOD generation through the Morton code scaling method, the atlas search area expansion method, and the axis removal method.

[0556] 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.

[0557] The operation of the transmitting and receiving device according to the above-described embodiments can be explained in combination with the following point cloud compression processing process.

[0558] The PCC encoding method, PCC decoding method, and signaling method of the embodiments described above can provide the following effects.

[0559] In these embodiments, a high-speed nearest neighbor search method is supported that can be utilized in the LOD generation process of attribute compression based on point cloud inter-prediction. These embodiments support methods to increase the speed and accuracy of inter-prediction-based LOD generation through a Morton code scaling method in inter-prediction, a method to expand the reference atlas search area in inter-prediction, and a method to remove an axis in inter-prediction. Support is provided to increase or decrease the influence of the reference frame by utilizing coefficients different from those of the current frame. Additionally, a method is supported to prevent the transmission of unnecessary information by signaling a flag to specify cases where the same coefficients as the current frame are applied. Furthermore, a method for setting reference atlas search reference points is proposed, thereby supporting more effective setting and search of the reference atlas search area.

[0560] 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.

[0561] The operation of the transmitting and receiving device according to the above-described embodiments can be explained in combination with the following point cloud compression processing process.

[0562] 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.

[0563] Although the drawings have been described separately for the convenience of explanation, it is also possible to design a new embodiment by combining the embodiments described in each drawing. Furthermore, designing a computer-readable recording medium containing a program for executing the previously described embodiments, as required by a person skilled in the art, falls within the scope of the claims of the embodiments. The apparatus and method according to the embodiments are not limited to the configuration and method of the embodiments described above; rather, the embodiments may be configured by selectively combining all or part of each embodiment to allow for various modifications. Although preferred embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above. It is not only possible for a person skilled in the art to make various modifications without departing from the essence of the embodiments claimed in the claims, but such modifications should not be understood individually from the technical concept or perspective of the embodiments.

[0564] Various components of the device of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various components of the embodiments may be implemented as a single chip, for example, a single hardware circuit. Depending on the embodiments, the components according to the embodiments may each be implemented as separate chips. Depending on the embodiments, at least one of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and one or more programs may include instructions for performing or executing any one or more of the operations / methods according to the embodiments. Executable instructions for performing the methods / operations of the device according to the embodiments may be stored in non-transient CRMs or other computer program products configured to be executed by one or more processors, or may be stored in transient CRMs or other computer program products configured to be executed by one or more processors. Additionally, memory according to the embodiments may be used as a concept that includes not only volatile memory (e.g., RAM, etc.) but also non-volatile memory, flash memory, PROM, etc. In addition, it may also include implementation in the form of carrier waves, such as transmission over the Internet. Furthermore, processor-readable recording media are distributed across networked computer systems, allowing processor-readable code to be stored and executed in a distributed manner.

[0565] 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.”

[0566] 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.

[0567] 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."

[0568] 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.

[0569] 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.

[0570] 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.

[0571] As described above, the relevant details have been explained in the best mode for carrying out the embodiments.

[0572] As described above, the embodiments may be applied wholly or partially to point cloud data transmission and reception devices and systems.

[0573] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0574] The embodiments may include modifications / variations, and such modifications / variations do not exceed the scope of the claims and their equivalents.

Claims

1. A step of decoding geometry data of point cloud data within a bitstream; and A step of decoding attribute data of the above point cloud data; comprising Decryption method.

2. In Paragraph 1, The step of decoding the above attribute data is: It further includes the step of generating Level of Detail (LoD) for the above point cloud data, and The step of generating the above LoD is: A step of assigning a molton code to the above point cloud data; A step of aligning the above point cloud data; A step of generating LoDs by subsampling the above point cloud data; and A step of searching for the nearest neighbors for a point based on the above LoDs; comprising Decryption method.

3. In Paragraph 2, The step of assigning the above molton code is: The method includes the step of downscaling the point cloud data based on a Molton code scaling flag, and The same downscaling is applied to the coordinate values ​​of the above point cloud data, or different downscaling is applied to the coordinate values ​​of the above point cloud data, Decryption method.

4. In Paragraph 1, The bitstream includes a molton code scaling flag indicating whether the position for the attribute data is downscaled during the molton code generation process, Decryption method.

5. In Paragraph 3, The above point cloud data is aligned based on the downscaled Molton code sequence, and Points having the same Molton code are sorted based on the output order of geometry decoding, Decryption method.

6. In Paragraph 1, The step of decoding the above attribute data is: The method further includes the step of generating a Level of Detail (LoD) for the point cloud data within the reference frame based on a reference frame for a current frame containing the point cloud data. The step of generating the above LoD is: A step of assigning a molton code to the above point cloud data; A step of aligning the above point cloud data; A step of generating LoDs by subsampling the above point cloud data; and A step of searching for the nearest neighbors for a point based on the above LoDs; comprising Decryption method.

7. In Paragraph 2, The step of assigning the above molton code is: The method includes the step of downscaling the point cloud data based on a Molton code scaling flag, and The same downscaling is applied to the coordinate values ​​of the above point cloud data, or different downscaling is applied to the coordinate values ​​of the above point cloud data, Decryption method.

8. In Paragraph 1, The bitstream includes a molton code scaling flag indicating whether the position for attribute data within the reference frame is downscaled during the molton code generation process. Decryption method.

9. In Paragraph 7, Based on information indicating whether the downscaling factor in the bitstream is the same, the downscaling factor for the point cloud data in the reference frame is the same as or different from the downscaling factor for the point cloud data in the current frame. Decryption method.

10. In Paragraph 7, A point in the reference frame is selected based on the index of a point in the current frame, or a point in the reference frame is selected based on the position of a point in the current frame. Decryption method.

11. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Decoding geometry data of point cloud data within a bitstream; and Decoding attribute data of the above point cloud data; configured to do so, 12. A step of encoding the geometry data of the point cloud data; and A step of encoding attribute data of the above point cloud data; comprising Encoding method.

13. Memory; and At least one processor connected to the memory; comprising, wherein the at least one processor: Encoding the geometry data of the point cloud data; and Configured to encode the attribute data of the above point cloud data; Decoding device.

14. A computer-readable storage medium for storing a bitstream generated by the method according to paragraph 12.

15. Step for acquiring a bitstream for point cloud data, The bitstream is generated based on the step of encoding geometry data of the point cloud data; and the step of encoding attribute data of the point cloud data; and A method comprising the step of transmitting data including the bitstream above.

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