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

Abstract

A decoding method according to embodiments may comprise the steps of: decoding geometry data of point cloud data in a bitstream; and decoding attribute data of the point cloud data. An encoding 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] Embodiments relate to a method and apparatus for processing point cloud content.

[0002] Point cloud content is represented as a point cloud, a collection of points within a coordinate system representing 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 to hundreds of thousands of point data. Therefore, a method for efficiently processing massive amounts of point data is required.

[0003] Embodiments provide devices and methods for efficiently processing point cloud data. Embodiments provide methods and devices for processing point cloud data to address latency and encoding / decoding complexity.

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

[0005] A decoding method according to embodiments may include a step of decoding geometry data of point cloud data in a bitstream; and a step of decoding attribute data of the point cloud data. A decoding method according to embodiments may include a step of encoding geometry data of the point cloud data; and a step of encoding attribute data of the 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 services such as VR services and autonomous driving services.

[0009] The drawings are included to further understand the embodiments, and the drawings illustrate the embodiments together with the description related to the embodiments. For a better understanding of the various embodiments described below, reference should be made to the following description of the embodiments in conjunction with the following drawings, in which like reference numerals correspond to corresponding parts throughout the drawings.

[0010] Figure 1 illustrates 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 illustrates an example of a point cloud encoder according to embodiments.

[0013] Figure 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 illustrates an example of a point cloud decoder according to embodiments.

[0017] Figure 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 shows an encoder according to embodiments.

[0021] Fig. 12 shows a block diagram of an attribute information encoding unit according to embodiments.

[0022] Fig. 13 shows a decoder according to embodiments.

[0023] Figure 14 illustrates decoding of attribute data according to embodiments.

[0024] Figure 15 shows weight calculation according to embodiments.

[0025] Figure 16 shows weight calculation according to embodiments.

[0026] Figure 17 shows weight calculation according to embodiments.

[0027] Figure 18 shows weight calculation according to embodiments.

[0028] Figure 19 shows a bitstream according to embodiments.

[0029] Figure 20 shows a sequence parameter set (SPS) according to embodiments.

[0030] Figure 21 illustrates an attribute parameter set (APS) according to embodiments.

[0031] Figure 22 illustrates a tile parameter set (TPS) according to embodiments.

[0032] Figure 23 illustrates an attribute data header according to embodiments.

[0033] Figure 24 shows an encoding method according to embodiments.

[0034] Figure 25 shows a decryption method according to embodiments.

[0035] Preferred embodiments of the embodiments are described in detail, examples of which are illustrated in the accompanying drawings. The following detailed description, with reference to the accompanying drawings, is intended to illustrate preferred embodiments of the embodiments, rather than merely show embodiments that can be implemented according to the embodiments. The following detailed description includes details to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be practiced without these details.

[0036] While most of the terms used in the examples are commonly used in the field, some terms were arbitrarily selected by the applicant, and their meanings are described in detail in the following descriptions as needed. Therefore, the examples should be understood based on the intended meaning of the terms, not simply their names or meanings.

[0037] Figure 1 illustrates an example of a point cloud content provision system according to embodiments.

[0038] The point cloud content provision system illustrated in FIG. 1 may include a transmission device (10000) and a reception device (10004). The transmission device (10000) and the reception device (10004) are capable of wired and wireless communication to transmit and receive point cloud data.

[0039] A transmission device (10000) according to embodiments can secure, process, and transmit a point cloud video (or point cloud content). According to embodiments, the transmission device (10000) can include a fixed station, a base transceiver system (BTS), a network, an Artificial Intelligence (AI) device and / or system, a robot, an AR / VR / XR device and / or a server, etc. In addition, according to embodiments, the transmission device (10000) can include a device that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)), a robot, a vehicle, an AR / VR / XR device, a portable device, a home appliance, an IoT (Internet of Things) device, an AI device / server, etc.

[0040] A transmission device (10000) according to embodiments includes a point cloud video acquisition unit (Point Cloud Video Acquisition, 10001), a point cloud video encoder (Point Cloud Video Encoder, 10002), and / or a transmitter (or communication module, 10003).

[0041] A point cloud video acquisition unit (10001) according to embodiments acquires a point cloud video through a processing process such as capture, synthesis, or generation. The point cloud video is point cloud content expressed as a point cloud, which is a collection 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 embodiments may include one or more frames. One frame represents a still image / picture. Therefore, 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.

[0042] A point cloud video encoder (10002) according to embodiments encodes acquired point cloud video data. The point cloud video encoder (10002) may encode point cloud video data based on point cloud compression coding. The point cloud compression coding according to embodiments may include G-PCC (Geometry-based Point Cloud Compression) coding and / or V-PCC (Video-based Point Cloud Compression) coding or next-generation coding. In addition, the point cloud compression coding according to embodiments is not limited to the above-described embodiment. The point cloud video encoder (10002) may output a bitstream including encoded point cloud video data. The bitstream may include not only encoded point cloud video data but also signaling information related to encoding of the point cloud video data.

[0043] A transmitter (10003) according to embodiments transmits a bitstream including encoded point cloud video data. The bitstream according to embodiments is encapsulated into a file or segment (e.g., streaming segment) and transmitted through various networks such as a broadcast network and / or a broadband network. Although not shown in the drawing, the transmission device (10000) may include an encapsulation unit (or an encapsulation module) that performs an encapsulation operation. In addition, the encapsulation unit may be included in the transmitter (10003) according to embodiments. According to embodiments, the file or segment may be transmitted to a receiving device (10004) through a network or may be stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter (10003) according to embodiments may communicate with the receiving device (10004) (or receiver (10005)) via a network such as 4G, 5G, or 6G via wired / wireless communication. Additionally, the transmitter (10003) can perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). Additionally, the transmission device (10000) can transmit encapsulated data in an on-demand manner.

[0044] A receiving device (10004) according to embodiments includes a receiver (Receiver) 10005, a point cloud video decoder (Point Cloud Decoder) 10006, and / or a renderer (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 Things) device, AI device / server, etc. that performs communication with a base station and / or other wireless devices using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)).

[0045] A receiver (10005) according to embodiments receives a bitstream containing point cloud video data or a file / segment in which the bitstream is encapsulated, from a network or a storage medium. The receiver (10005) may perform data processing operations required according to a network system (e.g., a communication network system such as 4G, 5G, or 6G). The receiver (10005) according to embodiments may decapsulate the received file / segment and output a bitstream. In addition, the receiver (10005) according to embodiments may include a decapsulation unit (or decapsulation module) for performing the decapsulation operation. In addition, the decapsulation unit may be implemented as a separate element (or component) from the receiver (10005).

[0046] 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 how it is encoded (e.g., the reverse process of the operation of the 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. The point cloud decompression coding includes G-PCC coding.

[0047] The renderer (10007) renders decoded point cloud video data. The renderer (10007) can output point cloud content by rendering not only point cloud video data but also audio data. 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.

[0048] The dotted arrows in the drawing indicate the transmission path of feedback information acquired from the receiving device (10004). The feedback information is information for reflecting the interaction with the user consuming the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, etc.). In particular, when the point cloud content is content for a service requiring interaction with the user (e.g., autonomous driving service, etc.), the feedback information may be transmitted to the content transmitter (e.g., the transmitting device (10000)) and / or the service provider. Depending on the embodiments, the feedback information may be used by the receiving device (10004) as well as the transmitting device (10000), or may not be provided.

[0049] Head orientation information according to embodiments is information about the position, direction, angle, movement, etc. of the user's head. The receiving device (10004) according to embodiments can calculate viewport information based on the head orientation information. The viewport information is information about the area of ​​the point cloud video that the user is looking at. The viewpoint is the point where the user is looking at the point cloud video, and may mean the exact center point of the viewport area. In other words, the viewport is an area centered on the viewpoint, and the size, shape, etc. of the area can be determined by the FOV (Field Of View). 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 area of ​​the point cloud video that 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). The feedback information according to embodiments may be acquired during a rendering and / or display process. The feedback information according to embodiments may be acquired by one or more sensors included in the receiving device (10004). Additionally, according to embodiments, the feedback information may be acquired by a renderer (10007) or a separate external element (or device, component, etc.). The dotted line in Fig. 1 represents a transmission process of feedback information acquired by the renderer (10007). The point cloud content providing system may process (encode / decode) point cloud data based on the feedback information. Therefore, 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 point cloud video data encoder (10002)) can perform an encoding operation based on the feedback information. Therefore, 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.

[0050] According to embodiments, the transmitting device (10000) may be referred to as an encoder, a transmitting device, a transmitter, etc., and the receiving device (10004) may be referred to as a decoder, a receiving device, a receiver, etc.

[0051] Point cloud data processed (processed through a series of processes of acquisition / encoding / transmission / decoding / rendering) in the point cloud content providing system of FIG. 1 according to embodiments may be referred to as point cloud content data or point cloud video data. According to embodiments, point cloud content data may be used as a concept including metadata or signaling information related to point cloud data.

[0052] The elements of the point cloud content provision system illustrated in FIG. 1 may be implemented by hardware, software, a processor, and / or a combination thereof.

[0053] FIG. 2 is a block diagram illustrating a point cloud content provision operation according to embodiments.

[0054] 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).

[0055] A point cloud content providing system according to 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 expressed as a point cloud belonging to a coordinate system representing a three-dimensional space. The point cloud video according to embodiments can 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 can include one or more Ply files. The Ply file includes point cloud data such as the geometry and / or attributes of points. The geometry includes the positions of points. The position of each point can be expressed as parameters (e.g., values ​​of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (e.g., a coordinate system composed of XYZ axes). Attributes include attributes of points (e.g., texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.). A point has one or more attributes (or properties). For example, a point may have one attribute, color, or two attributes, 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.Additionally, 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 a point cloud video (e.g., depth information, color information, etc.).

[0056] A point cloud content providing system according to embodiments (e.g., a transmission device (10000) or a point cloud video encoder (10002)) can encode point cloud data (20001). The point cloud content providing system can encode point cloud data based on point cloud compression coding. As described above, point cloud data can include geometry and attributes of points. Therefore, the point cloud content providing system can perform geometry encoding to encode geometry and output a geometry bitstream. The point cloud content providing system can perform attribute encoding to encode attributes and output an attribute bitstream. According to embodiments, the point cloud content providing system can perform attribute encoding based on geometry encoding. The geometry bitstream and the attribute bitstream according to embodiments can be multiplexed and output as a single bitstream. A bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.

[0057] 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 expressed as a geometry bitstream and an attribute bitstream. In addition, the encoded point cloud data can be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (e.g., signaling information related to geometry encoding and attribute encoding). In addition, the point cloud content providing system can encapsulate a bitstream that transmits the encoded point cloud data and transmit it in the form of a file or segment.

[0058] A point cloud content providing system according to embodiments (e.g., a receiving device (10004) or a receiver (10005)) can receive a bitstream including encoded point cloud data. In addition, the point cloud content providing system (e.g., a receiving device (10004) or a receiver (10005)) can demultiplex the bitstream.

[0059] 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. The point cloud content providing system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. The 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 positions (geometry) of points. The point cloud content providing system can decode the attribute bitstream based on the restored geometry to restore attributes of points. A point cloud content provision system (e.g., a receiving device (10004) or a point cloud video decoder (10005)) can reconstruct a point cloud video based on positions and decoded attributes according to the reconstructed geometry.

[0060] A point cloud content providing system (e.g., a receiving device (10004) or a renderer (10007)) according to embodiments 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 the decoded geometry and attributes 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 certain minimum size centered on the vertex position, or circles centered on the vertex position. All or a portion of the rendered point cloud content is provided to a user through a display (e.g., a VR / AR display, a general display, etc.).

[0061] A point cloud content provision system according to embodiments (e.g., a receiving device (10004)) can obtain feedback information (20005). The point cloud content provision system can encode and / or decode point cloud data based on the feedback information. The feedback information and the operation of the point cloud content provision system according to embodiments are identical to the feedback information and operation described in FIG. 1, and therefore, a detailed description thereof will be omitted.

[0062] FIG. 3 illustrates an example of a point cloud encoder according to embodiments.

[0063] FIG. 3 illustrates an example of a 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 an encoding operation to adjust the quality of point cloud content (e.g., lossless, lossy, near-lossless) depending on network conditions or applications. If the total size of the point cloud content is large (e.g., point cloud content of 60 Gbps at 30 fps), the point cloud content provision system may not be able to stream the content in real time. Therefore, the point cloud content provision system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment, etc.

[0064] 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.

[0065] The point cloud encoder according to the embodiments includes 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), a LOD generation unit (Generated LOD, 30009), a lifting transformation unit (Lifting) (30010), and a coefficient quantization unit (Quantize Coefficients, 30011) and / or an arithmetic encoder (30012).

[0066] The coordinate system transformation unit (30000), the quantization unit (30001), the octree analysis unit (30002), the surface approximation analysis unit (30003), the arithmetic encoder (30004), and the geometry reconstruction unit (30005) can perform geometry encoding. The geometry encoding according to the embodiments can include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisoup geometry encoding are applied selectively or in combination. In addition, the geometry encoding is not limited to the above examples.

[0067] As illustrated in the drawing, a coordinate system conversion unit (30000) according to embodiments receives positions and converts them into coordinates. For example, the positions may be converted into location information of a three-dimensional space (e.g., a three-dimensional space expressed in an XYZ coordinate system, etc.). The location information of the three-dimensional space according to embodiments may be referred to as geometry information.

[0068] A quantization unit (30001) according to embodiments quantizes geometry. For example, the quantization unit (30001) may quantize points based on the minimum position value of all points (e.g., the minimum value on each axis for the X-axis, Y-axis, and Z-axis). The quantization unit (30001) performs a quantization operation of multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value, and then rounding down or up to find the closest integer value. Accordingly, one or more points may have the same quantized position (or position value). The quantization unit (30001) according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points. The minimum unit containing two-dimensional image / video information is a pixel, and points of point cloud content (or three-dimensional point cloud video) according to embodiments may be included in one or more voxels. A voxel is a combination of a volume and a pixel, and refers to a three-dimensional cubic space 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) may match groups of points in the three-dimensional space to voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. In addition, in order to express one voxel as one point, the position of the center of the voxel may be set based on the positions of one or more points included in one voxel. In this case, the attributes of all positions contained in one voxel can be combined and assigned to the voxel.

[0069] The octree analysis unit (30002) according to the embodiments performs octree geometry coding (or octree coding) to represent voxels in an octree structure. The octree structure represents points matched to voxels based on an octree structure.

[0070] 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 and voxelizing an area including a large number of points to efficiently provide an octree and voxelization.

[0071] An arithmetic encoder (30004) according to embodiments entropy encodes an octree and / or an approximated octree. For example, the encoding method includes an arithmetic encoding method. The encoding results in a geometry bitstream.

[0072] The color conversion unit (30006), the attribute conversion unit (30007), the RAHT conversion unit (30008), the LOD generation unit (30009), the lifting conversion unit (30010), the coefficient quantization unit (30011) and / or the arithmetic encoder (30012) perform attribute encoding. As described above, one point may have one or more attributes. Attribute encoding according to embodiments is applied equally to the attributes of one point. However, when one attribute (e.g., color) includes one or more elements, independent attribute encoding is applied to each element. Attribute encoding according to embodiments may include color transform coding, attribute transform coding, RAHT (Region Adaptive Hierarchial Transform) coding, Interpolarization-based hierarchical nearest-neighbor prediction-Prediction Transform) coding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) coding. Depending on the point cloud content, the above-described RAHT coding, prediction transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used. In addition, attribute encoding according to embodiments is not limited to the above-described examples.

[0073] The color conversion unit (30006) according to the embodiments performs color conversion coding to convert color values ​​(or textures) included in attributes. For example, the color conversion unit (30006) may 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 optionally applied depending on the color values ​​included in the attributes.

[0074] 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 a reconstructed geometry (or restored geometry).

[0075] The attribute conversion unit (30007) according to the embodiments performs attribute conversion that converts attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit (30007) can convert the attributes based on the reconstructed geometry information. For example, the attribute conversion unit (30007) can convert the attribute of a point at a position based on the position value of the point included in the 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 the voxel, the attribute conversion unit (30007) converts the attributes of one or more points. When try-soup geometry encoding is performed, the attribute conversion unit (30007) can convert attributes based on the try-soup geometry encoding.

[0076] The attribute transformation unit (30007) can perform attribute transformation by calculating the average value of the attributes or attribute values ​​(e.g., the color or reflectance of each point) of neighboring points within a specific position / radius from the position (or position value) of the center point of each voxel. The attribute transformation unit (30007) can apply a weight according to the distance from the center point to each point when calculating the average value. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

[0077] The attribute transformation unit (30007) can search for neighboring points within a specific position / radius from the position of the center point of each voxel based on the KD tree or the Moulton code. The KD tree is a binary search tree that supports a data structure that can manage points based on their positions to enable fast nearest neighbor search (NNS). The Moulton code represents the coordinate values ​​(e.g. (x, y, z)) indicating the 3D positions of all points as bit values ​​and is generated by mixing the bits. For example, if the coordinate values ​​indicating the position of a point are (5, 9, 1), the bit values ​​of the coordinate values ​​are (0101, 1001, 0001). If the bit values ​​are mixed in the order of z, y, and x according to the bit index, it is 010001000111. If this value is expressed in decimal, it becomes 1095. That is, the Moulton code value of the point with coordinate values ​​(5, 9, 1) is 1095. The attribute transformation unit (30007) can sort points based on the Moulton code value and perform nearest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, if nearest neighbor search (NNS) is also required in other transformation processes for attribute coding, a KD tree or Moulton code is utilized.

[0078] As shown in the drawing, the converted attributes are input to the RAHT conversion unit (30008) and / or the LOD generation unit (30009).

[0079] The RAHT transform unit (30008) according to the embodiments performs RAHT coding to predict attribute information based on reconstructed geometry information. For example, the RAHT transform unit (30008) can predict attribute information of a node at an upper level of an octree based on attribute information associated with a node at a lower level of the octree.

[0080] The LOD generation unit (30009) according to the embodiments generates a LOD (Level of Detail) to perform predictive transformation coding. The LOD according to the embodiments represents the level of detail of point cloud content. A smaller LOD value indicates lower detail of point cloud content, and a larger LOD value indicates higher detail of point cloud content. Points can be classified according to LOD.

[0081] The lifting transformation unit (30010) according to the embodiments performs lifting transformation coding that transforms attributes of a point cloud based on weights. As described above, lifting transformation coding may be applied selectively.

[0082] The coefficient quantization unit (30011) according to the embodiments quantizes attribute-coded attributes based on coefficients.

[0083] An arithmetic encoder (30012) according to embodiments encodes quantized attributes based on arithmetic coding.

[0084] The elements of the point cloud encoder of FIG. 3 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud encoder of FIG. 3 described above. Furthermore, the 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. The one or more memories according to embodiments may include high-speed random access memory, or 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).

[0085] Figure 4 illustrates examples of octree and occupancy codes according to embodiments.

[0086] As described in FIGS. 1 to 3, the point cloud content provision system (point cloud video encoder (10002)) or point cloud encoder (e.g., octree analysis unit (30002)) performs octree geometry coding (or octree coding) based on an octree structure to efficiently manage the area and / or position of a voxel.

[0087] The top of Fig. 4 shows the octree structure. The three-dimensional space of the point cloud content according to the embodiments is expressed 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 ) is generated by recursively subdividing the cubical axis-aligned bounding box defined by . 2d can be set to a value that constitutes the smallest bounding box that encloses all points of the point cloud content (or point cloud video). d represents the depth of the octree. The value of d is determined by 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.

[0088] d=Ceil(Log2(Max(x int n ,y int n ,z int n ,n=1,…,N)+1))

[0089] As shown in the middle of the upper part of Fig. 4, the entire three-dimensional space can be divided into eight spaces according to the division. Each divided space is expressed as a cube with six faces. As shown in the upper right of Fig. 4, each of the eight spaces is again divided based on the axes of the coordinate system (e.g., X-axis, Y-axis, Z-axis). Therefore, each space is again divided into eight smaller spaces. The divided smaller spaces are also expressed as cubes with six faces. This division method is applied until the leaf nodes of the octree become voxels.

[0090] The bottom of Fig. 4 shows the occupancy code of the octree. The occupancy code of the octree is generated to indicate whether each of the eight partitioned spaces generated by partitioning one space contains at least one point. Therefore, one occupancy code is expressed by eight child nodes. Each child node represents the occupancy of the partitioned space, and each child node has a value of 1 bit. Therefore, the occupancy code is expressed 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 illustrated in Fig. 4 is 00100001, it indicates that the spaces corresponding to the third and eighth child nodes among the eight child nodes each contain at least one point. As shown in the drawing, the third child node and the eighth child node each have eight child nodes, and each child node is expressed by an 8-bit occupancy code. The drawing shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30004)) can entropy encode the occupancy code. In addition, the point cloud encoder can intra / inter code the occupancy code to increase compression efficiency. A receiving device according to embodiments (e.g., a receiving device (10004) or a point cloud video decoder (10006)) reconstructs an octree based on the occupancy code.

[0091] A point cloud encoder according to 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 points. However, points within a 3D space are not always evenly distributed, and thus, there may be specific areas where there are not many points. Therefore, performing voxelization on the entire 3D space is inefficient. For example, if there are few points in a specific area, there is no need to perform voxelization up to that area.

[0092] Therefore, the point cloud encoder according to the embodiments can perform direct coding that directly codes the positions of points included in the specific region (or nodes excluding leaf nodes of the octree) without performing voxelization for the specific region described above. The coordinates of the direct coded points according to the embodiments are referred to as a direct coding mode (DCM). In addition, the point cloud encoder according to the embodiments can perform trisoup geometry encoding that reconstructs the positions of points within the specific region (or node) on a voxel basis based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes. Therefore, the point cloud decoder can generate a point cloud from the mesh surface. Direct coding and trisoup geometry encoding according to the embodiments can be selectively performed. Additionally, direct coding and tri-subtractive geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).

[0093] In order to perform direct coding, the option to use direct mode for applying direct coding must be activated, the node to which direct coding is to be applied must not be a leaf node, and there must be points below a threshold within a specific node. 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 arithmetic encoder (30004)) according to the embodiments can entropy code the positions (or position values) of the points.

[0094] A point cloud encoder according to embodiments (e.g., surface approximation analysis unit (30003)) can determine a specific level of an octree (when the level is smaller than the depth d of the octree) and, starting from that level, perform tri-subject geometry encoding to reconstruct the positions of points within a node region on a voxel basis using a surface model (tri-subject mode). A point cloud encoder according to embodiments can specify a level to which tri-subject geometry encoding is to be applied. For example, when the specified level is equal to the depth of the octree, the point cloud encoder does not operate in tri-subject mode. That is, a point cloud encoder according to embodiments can operate in tri-subject mode only when the specified level is smaller than the depth value of the octree. A three-dimensional cubic area of ​​nodes at a specified level according to embodiments is called a block. One block may include one or more voxels. A block or a voxel may correspond to a brick. Within each block, geometry is represented by a surface. According to embodiments, a surface may intersect each edge of the block at most once.

[0095] Since one block has 12 edges, there are at least 12 intersections within one block. Each intersection is called a vertex. A vertex existing along an edge is detected if there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. An occupied voxel according to 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 the edge among all blocks sharing the edge.

[0096] When a vertex is detected, the point cloud encoder according to the embodiments can entropy code the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When the tri-substructure geometry encoding is applied, the point cloud encoder according to the embodiments (e.g., the geometry reconstruction unit (30005)) can perform triangle reconstruction, up-sampling, and voxelization processes to generate restored geometry (reconstructed geometry).

[0097] The vertices located at the edge of a block determine the surface passing through the block. According to the embodiments, the surface is a non-planar polygon. The triangle reconstruction process reconstructs the surface represented by a triangle based on the starting point of the edge, the direction vector of the edge, and the position value of the vertex. The triangle reconstruction process is as follows. ① Calculate the centroid value of each vertex, ② Subtract the centroid value from each vertex value, and ③ Square the values, and then add up all the values ​​to obtain the value.

[0098]

[0099] The minimum of the added values ​​is found, and the projection process is performed according to the axis with the minimum value. For example, if the x element is minimum, each vertex is projected to the x-axis based on the center of the block, and then projected onto the (y, z) plane. If the value output when projected onto the (y, z) plane is (ai, bi), the θ value is found through atan2(bi, ai), and the vertices are sorted based on the θ value. The table below shows the combination of vertices to create a triangle depending on the number of vertices. The vertices are sorted in order from 1 to n. The table below shows that for four vertices, two triangles can be formed depending on the combination of the vertices. The first triangle can be formed by the 1st, 2nd, and 3rd vertices among the sorted vertices, and the second triangle can be formed by the 3rd, 4th, and 1st vertices among the sorted vertices.

[0100] Table 2-1. Triangles formed from vertices ordered 1,… ,n

[0101] n triangles

[0102] 3 (1,2,3)

[0103] 4 (1,2,3), (3,4,1)

[0104] 5 (1,2,3), (3,4,5), (5,1,3)

[0105] 6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

[0106] 7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

[0107] 8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

[0108] 9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

[0109] 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)

[0110] 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)

[0111] 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)

[0112] The upsampling process is performed to voxelize the triangle by adding points in the middle along the edges. Additional points are generated based on the upsampling factor and the width of the block. The additional points are called refined vertices. A point cloud encoder according to embodiments can voxelize the refined vertices. The point cloud encoder can also perform attribute encoding based on the voxelized positions (or position values).

[0113] Figure 5 shows an example of a point configuration by LOD according to embodiments.

[0114] 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 direct-coded points (e.g., placing the direct-coded points at the front of the point cloud data). When trysoup geometry encoding is applied, the geometry reconstruction process includes triangle reconstruction, upsampling, and voxelization. Since attributes depend on the geometry, attribute encoding is performed based on the reconstructed geometry.

[0115] A point cloud encoder (e.g., LOD generation unit (30009)) can reorganize points by LOD. The drawing shows point cloud content corresponding to LOD. The left side of the drawing shows the original point cloud content. The second figure from the left in the drawing shows the distribution of points of the lowest LOD, and the rightmost figure in the drawing shows the distribution of points of the highest LOD. That is, points of the lowest LOD are sparsely distributed, and points of the highest LOD are densely distributed. That is, as LOD increases in the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between points becomes shorter.

[0116] Figure 6 shows an example of a point configuration by LOD according to embodiments.

[0117] 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 a LOD generation unit (30009)) can generate a 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 not only in the point cloud encoder but also in the point cloud decoder.

[0118] The upper part of Fig. 6 shows examples of points (P0 to P9) of point cloud content distributed in 3D space. The original order in Fig. 6 represents the order of points P0 to P9 before LOD generation. The LOD-based order in Fig. 6 represents the order of points according to LOD generation. The points are rearranged by LOD. Additionally, a higher LOD includes points belonging to a lower LOD. As shown in Fig. 6, LOD0 includes P0, P5, P4, and P2. LOD1 includes points of LOD0 and P1, P6, and P3. LOD2 includes points of LOD0, points of LOD1, and P9, P8, and P7.

[0119] As described in FIG. 3, the point cloud encoder according to the embodiments can selectively or in combination perform predictive transform coding, lifting transform coding, and RAHT transform coding.

[0120] A point cloud encoder according to embodiments can perform predictive transformation coding to generate a predictor for points and set a predicted attribute (or predicted attribute value) for each point. That is, N predictors can be generated for N points. The predictor according to embodiments can calculate a weight (= 1 / distance) value based on the LOD value of each point, indexing information for neighboring points existing within a distance set for each LOD, and distance values ​​to the neighboring points.

[0121] According to the embodiments, the predicted attribute (or attribute value) is set as the average value of the product of the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point and the weight (or weight value) calculated based on the distance to each neighboring point. The point cloud encoder according to the embodiments (for example, the 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.

[0122] Attribute prediction residuals quantization pseudo codeint PCCQuantization(int value, int quantStep) {if( value >=0) {return floor(value / quantStep + 1.0 / 3.0);} else {return -floor(-value / quantStep + 1.0 / 3.0);}}

[0123] Attribute prediction residuals inverse quantization pseudo codeint PCCInverseQuantization(int value, int quantStep) {if( quantStep ==0) {return value;} else {return value * quantStep;}}

[0124] A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the quantized and dequantized residuals as described above when there are neighboring points to the predictor of each point. A point cloud encoder according to embodiments (e.g., an arithmetic encoder (30012)) can entropy code the attributes of the point without performing the above-described process when there are no neighboring points to the predictor of each point.

[0125] A point cloud encoder according to embodiments (e.g., 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 according to the distance to the neighboring points. Lifting transformation coding according to embodiments is similar to the above-described predictive transformation coding, but differs in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to attribute values ​​according to embodiments is as follows.

[0126] 1) Create an array QW (QuantizationWieght) that stores the weight values ​​of each point. The initial value of all elements in QW is 1.0. Add the value obtained by multiplying the weight of the current point's predictor by the QW value of the predictor index of the neighboring node registered in the predictor.

[0127] 2) Lift prediction process: To calculate the predicted attribute value, the weighted value of the point's attribute value is multiplied and subtracted from the existing attribute value.

[0128] 3) Create temporary arrays called updateweight and update and initialize them to 0.

[0129] 4) For each predictor, the calculated weights are multiplied by the weights stored in the QW corresponding to the predictor index, and the resulting weights are cumulatively added to the update weight array as the index of the neighboring node. The update array accumulates the values ​​obtained by multiplying the calculated weights by the attribute values ​​of the indexes of the neighboring nodes.

[0130] 5) Lift update process: For each predictor, the attribute values ​​in the update array are divided by the weight values ​​in the update weight array of the predictor index, and the existing attribute values ​​are added to the divided value.

[0131] 6) For all predictors, the predicted attribute values ​​are calculated by additionally multiplying the updated attribute values ​​through the lift update process by the weights (stored in QW) updated through the lift prediction process. The point cloud encoder according to the embodiments (e.g., coefficient quantization unit (30011)) quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., arithmetic encoder (30012)) entropy-codes the quantized attribute values.

[0132] A point cloud encoder according to embodiments (e.g., RAHT transform unit (30008)) can perform RAHT transform coding that predicts attributes of upper-level nodes using attributes associated with nodes at lower levels of an octree. RAHT transform coding is an example of attribute intra coding through octree backward scan. A point cloud encoder according to embodiments scans from a voxel to the entire area, and repeats the merging process up to a root node while merging voxels into larger blocks at each step. The merging process according to embodiments is performed only for occupied nodes. The merging process is not performed for empty nodes, and the merging process is performed for the node immediately above the empty node.

[0133] The equation below represents the RAHT transformation matrix. is the level It represents the average attribute value of voxels in . Is and can be calculated from. and The weight of class am.

[0134]

[0135] is a low-pass value, which is used in the merging process at the next higher level. are high-pass coefficients, and the high-pass coefficients at each step are quantized and entropy coded (e.g., encoding of an arithmetic encoder (300012)). The weights are is calculated as . The root node is the last class It is generated as follows:

[0136]

[0137] The gDC values ​​are also quantized and entropy coded, like the high-pass coefficients.

[0138] Fig. 7 illustrates an example of a point cloud decoder according to embodiments.

[0139] 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 FIGS. 1 to 6.

[0140] As described in Figure 1, the point cloud decoder can perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.

[0141] A point cloud decoder according to embodiments includes an arithmetic decoder (7000), an octree synthesizer (7001), a surface approximation synthesizer (7002), a geometry reconstructor (7003), an inverse transform coordinates (7004), an arithmetic decoder (7005), an inverse quantize (7006), a RAHT transform (7007), a LOD generator (7008), an inverse lifting (7009), and / or an inverse transform colors (7010).

[0142] The arithmetic decoder (7000), the octree synthesis unit (7001), the surface oproximation synthesis unit (7002), the geometry reconstruction unit (7003), and the coordinate system inversion unit (7004) can perform geometry decoding. Geometry decoding according to embodiments can include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are applied selectively. In addition, geometry decoding is not limited to the above examples, and is performed by the reverse process of the geometry encoding described in FIGS. 1 to 6.

[0143] An arithmetic decoder (7000) according to embodiments decodes a received geometry bitstream based on arithmetic coding. The operation of the arithmetic decoder (7000) corresponds to the reverse process of the arithmetic encoder (30004).

[0144] 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 about the geometry obtained as a result of decoding). A specific description of the occupancy code is as described in FIGS. 1 to 6.

[0145] The surface off-axis synthesis unit (7002) according to the embodiments can synthesize a surface based on the decoded geometry and / or the generated octree when the tri-sub geometry encoding is applied.

[0146] The geometry reconstruction unit (7003) according to the embodiments can regenerate geometry based on the surface and / or decoded geometry. As described in FIGS. 1 to 6, direct coding and try-soup geometry encoding are selectively applied. Therefore, the geometry reconstruction unit (7003) directly retrieves and adds position information of points to which direct coding is applied. In addition, when try-soup geometry encoding is applied, the geometry reconstruction unit (7003) can restore geometry by performing a reconstruction operation of the geometry reconstruction unit (30005), such as triangle reconstruction, up-sampling, and voxelization operations. The specific details are the same as described in FIG. 4 and are therefore omitted. The restored geometry may include a point cloud picture or frame that does not include attributes.

[0147] The coordinate system inverse transformation unit (7004) according to the embodiments can obtain the positions of points by transforming the coordinate system based on the restored geometry.

[0148] The arithmetic decoder (7005), the inverse quantization unit (7006), the RAHT transform unit (7007), the LOD generation unit (7008), the inverse lifting unit (7009), and / or the color inverse transform unit (7010) can perform the attribute decoding described in FIG. 10. The attribute decoding according to embodiments can include RAHT (Region Adaptive Hierarchial Transform) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform) decoding, and lifting transform (interpolation-based hierarchical nearest-neighbor prediction with an update / lifting step (Lifting Transform)) decoding. The three decodings described above can be used selectively, or a combination of one or more decodings can be used. Additionally, attribute decoding according to embodiments is not limited to the examples described above.

[0149] An arithmetic decoder (7005) according to embodiments decodes an attribute bitstream using arithmetic coding.

[0150] The inverse quantization unit (7006) according to the embodiments inverse quantizes information about the decoded attribute bitstream or the attributes obtained as a result of the decoding and outputs the inverse quantized attributes (or attribute values). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

[0151] According to embodiments, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may process the reconstructed geometry and the inverse quantized attributes. As described above, the RAHT transform unit (7007), the LOD generator (7008), and / or the inverse lifting unit (7009) may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.

[0152] The color inverse transform unit (7010) according to the embodiments performs inverse transform coding to inversely transform the color values ​​(or textures) included in the decoded attributes. The operation of the color inverse transform unit (7010) may be selectively performed based on the operation of the color transform unit (30006) of the point cloud encoder.

[0153] The elements of the point cloud decoder of FIG. 7 may be implemented by hardware, software, firmware, or a combination thereof, including one or more processors or integrated circuits configured to communicate with one or more memories included in a point cloud providing device, although not shown in the drawing. The one or more processors may perform at least one or more of the operations and / or functions of the elements of the point cloud decoder of FIG. 7 described above. Furthermore, the 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.

[0154] Figure 8 is an example of a transmission device according to embodiments.

[0155] 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 or more of the same or similar operations and encoding methods as the operations and encoding methods of the point cloud encoder described in FIGS. 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 a property conversion processing unit) (8009), a prediction / lifting / RAHT conversion processing unit (8010), an arithmetic coder (8011), and / or a transmission processing unit (8012).

[0156] The data input unit (8000) according to the embodiments receives or acquires point cloud data. The data input unit (8000) may perform operations and / or acquisition methods identical or similar to those of the point cloud video acquisition unit (10001) (or the acquisition process (20000) described in FIG. 2).

[0157] 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 the same or similar to the geometry encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0158] The quantization processing unit (8001) according to the embodiments quantizes geometry (e.g., position values ​​of points or position values). The operation and / or quantization of the quantization processing unit (8001) is identical to 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 FIGS. 1 to 6.

[0159] The voxelization processing unit (8002) according to the embodiments voxels the position values ​​of quantized points. The voxelization processing unit (80002) may perform operations and / or processes identical or similar to the operations and / or voxelization processes of the quantization unit (30001) described in FIG. 3. Specific descriptions are identical to those described in FIGS. 1 to 6.

[0160] The octree occupancy code generation unit (8003) according to the embodiments performs octree coding on the positions of voxelized points based on the 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 those 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 to 6.

[0161] The surface model processing unit (8004) according to the embodiments can perform tri-subject geometry encoding to reconstruct the positions of points within a specific area (or node) on a voxel basis based on the surface model. The surface model processing unit (8004) can perform operations and / or methods identical or similar to those 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 with reference to FIGS. 1 to 6.

[0162] The intra / inter coding processing unit (8005) according to embodiments can intra / inter code point cloud data. The intra / inter coding processing unit (8005) can perform coding identical to or similar to the intra / inter coding described in FIG. 7. The specific description is identical to that described in FIG. 7. According to embodiments, the intra / inter coding processing unit (8005) can be included in an arithmetic coder (8006).

[0163] An arithmetic coder (8006) according to embodiments entropy encodes an octree and / or an approximated octree of point cloud data. For example, the encoding method includes an arithmetic encoding method. The arithmetic coder (8006) performs operations and / or methods identical or similar to those of the arithmetic encoder (30004).

[0164] The metadata processing unit (8007) according to the embodiments processes metadata regarding point cloud data, such as setting values, and provides the metadata to a necessary processing step, such as geometry encoding and / or attribute encoding. In addition, the 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 and processed separately from geometry encoding and / or attribute encoding. In addition, the signaling information according to the embodiments may be interleaved.

[0165] The color conversion processing unit (8008), the attribute conversion processing unit (8009), the prediction / lifting / RAHT conversion processing unit (8010), and the arithmetic coder (8011) perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 6, a detailed description thereof will be omitted.

[0166] The color conversion processing unit (8008) according to the embodiments performs color conversion coding to convert the color values ​​included in the attributes. The color conversion processing unit (8008) can perform color conversion coding based on the reconstructed geometry. The description of the reconstructed geometry is the same as that described with reference to FIGS. 1 to 6. In addition, the color conversion processing unit (8008) performs the same or similar operation and / or method as that of the color conversion unit (30006) described with reference to FIG. 3. A detailed description thereof will be omitted.

[0167] The attribute transformation processing unit (8009) according to embodiments performs attribute transformation to transform attributes based on positions for which geometry encoding has not been performed and / or reconstructed geometry. The attribute transformation processing unit (8009) performs operations and / or methods that are the same as or similar to those of the attribute transformation unit (30007) described in FIG. 3. A detailed description thereof will be omitted. The prediction / lifting / RAHT transformation processing unit (8010) according to embodiments can code transformed attributes by using any one or a combination of RAHT coding, prediction transformation coding, and lifting transformation coding. The prediction / lifting / RAHT transformation processing unit (8010) performs at least one or more of operations that are the same as or similar to those of the RAHT transformation unit (30008), LOD generation unit (30009), and lifting transformation unit (30010) described in FIG. 3. In addition, the description of the prediction transformation coding, lifting transformation coding, and RAHT transformation coding is the same as that described in FIGS. 1 to 6, so a detailed description is omitted.

[0168] An arithmetic coder (8011) according to embodiments can encode coded attributes based on arithmetic coding. The arithmetic coder (8011) performs operations and / or methods identical or similar to those of the arithmetic encoder (300012).

[0169] The transmission processing unit (8012) according to embodiments may transmit each bitstream including encoded geometry and / or encoded attribute, metadata information, or may transmit the encoded geometry and / or encoded attribute, and metadata information as one bitstream. When the encoded geometry and / or encoded attribute, and metadata information according to embodiments are configured as one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to embodiments may include signaling information including a Sequence Parameter Set (SPS) for sequence-level signaling, a Geometry Parameter Set (GPS) for signaling geometry information coding, an Attribute Parameter Set (APS) for signaling attribute information coding, and a Tile Parameter Set (TPS) for tile-level signaling, and slice data. The slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream (Geom0). 0 ) and one or more attribute bitstreams (Attr0 0 , Attr1 0 ) may be included.

[0170] A slice is a series of syntax elements that represent all or part of a coded point cloud frame.

[0171] A TPS according to embodiments may include information about each tile (e.g., coordinate value information of a bounding box and height / size information, etc.) for one or more tiles. A geometry bitstream may include a header and a payload. The header of a geometry bitstream according to embodiments may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload. As described above, a metadata processing unit (8007) according to embodiments may generate and / or process signaling information and transmit it to a transmission processing unit (8012). According to embodiments, elements that perform geometry encoding and elements that perform attribute encoding may share data / information with each other as indicated by a dotted line. The transmission processing unit (8012) according to the embodiments may perform operations and / or transmission methods identical or similar to those of the transmitter (10003). A detailed description thereof is omitted as it is the same as that described in FIGS. 1 and 2.

[0172] Fig. 9 is an example of a receiving device according to embodiments.

[0173] The receiving device illustrated in FIG. 9 is an example of the receiving device (10004) of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11). The receiving device illustrated in FIG. 9 can perform at least one or more of the same or similar operations and decoding methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.

[0174] A receiving device according to 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 transform processing unit (9009), a color inverse transform processing unit (9010), and / or a renderer (9011). Each component of the decoding according to embodiments may perform the reverse process of the component of the encoding according to embodiments.

[0175] The receiving unit (9000) according to the embodiments receives point cloud data. The receiving unit (9000) may perform operations and / or receiving methods identical or similar to those of the receiver (10005) of FIG. 1. A detailed description thereof will be omitted.

[0176] The 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) can be included in the receiving unit (9000).

[0177] The arithmetic 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 the same or similar to the geometry decoding described in FIGS. 1 to 10, a detailed description thereof will be omitted.

[0178] An arithmetic decoder (9002) according to embodiments can decode a geometry bitstream based on arithmetic coding. The arithmetic decoder (9002) performs operations and / or coding identical to or similar to those of the arithmetic decoder (7000).

[0179] The occupancy code-based octree reconstruction processing unit (9003) according to embodiments can reconstruct an octree by obtaining an occupancy code from a decoded geometry bitstream (or information about the 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 those of the octree synthesis unit (7001) and / or the octree generation method. The surface model processing unit (9004) according to embodiments can perform tri-sub geometry decoding and related geometry reconstructing (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method when tri-sub geometry encoding is applied. The surface model processing unit (9004) performs the same or similar operations as those of the surface off-ratio synthesis unit (7002) and / or the geometry reconstructing unit (7003).

[0180] The inverse quantization processing unit (9005) according to the embodiments can inverse quantize the decoded geometry.

[0181] The metadata parser (9006) according to the embodiments can parse metadata, such as setting values, contained in the received point cloud data. The metadata parser (9006) can pass the metadata to geometry decoding and / or attribute decoding. A detailed description of the metadata is omitted as it is the same as described in FIG. 8.

[0182] The arithmetic decoder (9007), the inverse quantization processing unit (9008), the prediction / lifting / RAHT inverse transform processing unit (9009), and the color inverse transform processing unit (9010) perform attribute decoding. Since attribute decoding is the same or similar to the attribute decoding described in FIGS. 1 to 10, a detailed description thereof will be omitted.

[0183] An arithmetic decoder (9007) according to embodiments can decode an attribute bitstream using arithmetic coding. The arithmetic decoder (9007) can decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder (9007) performs operations and / or coding identical or similar to those of the arithmetic decoder (7005).

[0184] The inverse quantization processing unit (9008) according to the embodiments can inverse quantize the decoded attribute bitstream. The inverse quantization processing unit (9008) performs operations and / or methods identical or similar to the operations and / or inverse quantization methods of the inverse quantization unit (7006).

[0185] The prediction / lifting / RAHT inverse transform processing unit (9009) according to embodiments can process reconstructed geometry and inverse quantized attributes. The prediction / lifting / RAHT inverse transform processing unit (9009) performs at least one or more of operations and / or decodings that are identical or similar to the operations and / or decodings of the RAHT transform unit (7007), the LOD generation unit (7008), and / or the inverse lifting unit (7009). The color inverse transform processing unit (9010) according to embodiments performs inverse transform coding for inverse transforming the color value (or texture) included in the decoded attributes. The color inverse transform processing unit (9010) performs operations and / or inverse transform coding that are identical or similar to the operations and / or inverse transform coding of the color inverse transform unit (7010). A renderer (9011) according to embodiments can render point cloud data.

[0186] Fig. 10 shows an example of a structure that can be linked with a point cloud data transmission / reception method / device according to embodiments.

[0187] 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. In addition, the XR device (1030) may correspond to or be linked with a point cloud data (PCC) device according to embodiments.

[0188] A cloud network (1000) may refer to a network that constitutes part of a 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.

[0189] 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) through a cloud network (1000), and can assist in at least part of the processing of the connected devices (1010 to 1070).

[0190] The HMD (Head-Mount Display) (1070) represents one of the types in which the XR device and / or the PCC device according to the embodiments can be implemented. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory unit, an I / O unit, a sensor unit, and a power supply unit.

[0191] Below, various embodiments of devices (1010 to 1050) to which the above-described technology is applied are described. Here, the devices (1010 to 1050) illustrated in FIG. 10 can be linked / combined with point cloud data transmission / reception devices according to the above-described embodiments.

[0192] <PCC+XR>

[0193] The XR / PCC device (1030) may be implemented as a HMD (Head-Mount Display), a HUD (Head-Up Display) equipped in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a fixed robot, a mobile robot, etc., by applying PCC and / or XR (AR+VR) technology.

[0194] The XR / PCC device (1030) can obtain information about surrounding space or real objects by analyzing 3D point cloud data or image data acquired through various sensors or from external devices to generate location 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 including additional information about a recognized object in correspondence with the recognized object.

[0195] <PCC+XR+모바일폰>

[0196] The XR / PCC device (1030) can be implemented as a mobile phone (1040) or the like by applying PCC technology.

[0197] The mobile phone (1040) can decode and display point cloud content based on PCC technology.

[0198] <PCC+자율주행+XR>

[0199] Autonomous vehicles (1020) can be implemented as mobile robots, vehicles, unmanned aerial vehicles, etc. by applying PCC technology and XR technology.

[0200] An autonomous vehicle (1020) to which XR / PCC technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images, or an autonomous vehicle that is the subject of control / interaction within an XR image. In particular, an autonomous vehicle (1020) that is the subject of control / interaction within an XR image is distinct from an XR device (1030) and can be linked with each other.

[0201] An autonomous vehicle (1020) equipped with a means for providing XR / PCC images can obtain sensor information from sensors including cameras and output XR / PCC images generated based on the obtained sensor information. For example, the autonomous vehicle (1020) can be equipped with a HUD to output XR / PCC images, thereby providing passengers with XR / PCC objects corresponding to real objects or objects on a screen.

[0202] At this time, when the XR / PCC object is output to the HUD, at least a part of the XR / PCC object may be output so as to overlap with an actual object toward which the passenger's gaze is directed. On the other hand, when the XR / PCC object is output to a display provided inside the autonomous vehicle, at least a part of the XR / PCC object may be output so as to overlap with an object on the screen. For example, the autonomous vehicle (1220) may output XR / PCC objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, etc.

[0203] 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.

[0204] In other words, VR technology is a display technology that provides only CG images of objects or backgrounds in the real world. On the other hand, AR technology refers to a technology that shows a virtually created CG image on top of an image of an actual object. Furthermore, MR technology is similar to the aforementioned AR technology in that it mixes and combines virtual objects in the real world. However, in AR technology, the distinction between real objects and virtual objects created with CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are considered to have the same characteristics as real objects. A more specific example is the hologram service, which is an application of the aforementioned MR technology.

[0205] However, recently, rather than clearly distinguishing between VR, AR, and MR technologies, they are often referred to as XR (extended reality) technologies. Therefore, embodiments of the present invention are applicable to all VR, AR, MR, and XR technologies. These technologies can be applied to encoding / decoding based on PCC, V-PCC, and G-PCC technologies.

[0206] The PCC method / device according to the embodiments can be applied to a vehicle providing an autonomous driving service.

[0207] Vehicles providing autonomous driving services are connected to PCC devices to enable wired / wireless communication.

[0208] A point cloud data (PCC) transmission and reception device according to embodiments, when connected to a vehicle to enable wired / wireless communication, can receive / process content data related to AR / VR / PCC services that can be provided together with autonomous driving services and transmit the same to the vehicle. In addition, when the point cloud data transmission and reception device is mounted on a vehicle, the point cloud transmission and reception device can receive / process content data related to AR / VR / PCC services and provide the same to a user according to a user input signal input through a user interface device. A vehicle or a user interface device according to embodiments can receive a user input signal. The user input signal according to embodiments can include a signal instructing an autonomous driving service.

[0209] The encoding method and device according to the embodiments may include and perform the transmission device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the encoders of FIGS. 11 to 12, the encoders of FIGS. 15 to 18, the bitstream and syntax generation of FIGS. 19 to 23, and the encoding method of FIG. 24.

[0210] The decoding method and device according to the embodiments may include and perform the 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. 10 and FIG. 11, receiving device of FIG. 13, device of FIG. 14, decoder of FIG. 13 to FIG. 14, decoder of FIG. 15 to FIG. 18, bitstream and syntax parsing of FIG. 19 to FIG. 23, decoding method of FIG. 25, etc.

[0211] The encoding / decoding method according to the embodiments includes a lifting weight calculation method and a signaling method.

[0212] Embodiments include a method for calculating weight values ​​used in a lifting transformation for LiDAR data to increase compression efficiency of attributes of Geometry-based Point Cloud Compression (G-PCC) of a point cloud frame.

[0213] Examples include methods for calculating lifting weights, signaling methods, etc.

[0214] The embodiments include a method for improving the compression efficiency of Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. Hereinafter, the terms encoder, encoder, and decoder may be used interchangeably, respectively.

[0215] A point cloud consists of a collection of points, each of which can have both geometric and attribute information. Geometric information includes three-dimensional positional (XYZ) information, while attribute information includes color (RGB, YUV, etc.) and / or reflectance values.

[0216] The G-PCC encoding process can be comprised of dividing a point cloud into tiles by region, dividing each tile into slices for parallel processing, compressing geometry for each slice, and compressing attribute information based on the reconstructed geometry (decoded geometry) based on the positional information changed through compression.

[0217] The G-PCC decoding process can be configured to receive an encoded slice unit geometry bitstream and attribute bitstream, decode the geometry, and decode attribute information based on the geometry reconstructed through the decoding process.

[0218] Octree-based, predictive tree-based, or trisoup-based compression techniques can be used to compress geometry information.

[0219] For attribute information compression, compression techniques based on predicting transform, lifting transform, or RAHT (Region-Adaptive Hierarchical Transform) transform can be used.

[0220] In the prediction transformation technique and the lifting transformation technique according to the embodiments, points can be divided and grouped by level of detail (LOD).

[0221] Each point can have a single predictor. The predictor predicts attributes from neighboring points registered with the predictor. The predictor can perform predictions using the set of neighboring points through inverse distance weighting (IDW). That is, the weights can be calculated using the distance values ​​between each neighboring point and the current point.

[0222] For example, the predictor of node P3 can use the set of neighboring points (P2 P4 P6) to calculate weights based on the distance values ​​of each current point and its neighboring points. The weight of each neighboring point is (1 / (P2-P3) 2 , 1 / (P4-P3) 2 , 1 / (P6-P3) 2 ) can be calculated.

[0223] To reiterate, in one embodiment, the distance between the current point and its neighboring point can be calculated as the L2 distance, which is the square of the distance between the two points. The lifting weight of each neighboring point can then be calculated as the reciprocal of the L2 distance between the two points.

[0224] Once the set of neighboring points of the predictor is set, 3) the weights of each neighboring point can be normalized by the sum of the weights of the neighboring points.

[0225] For example, add the weights of all neighboring points within the set of neighboring points of node P3, and total weight (total_weight) = 1 / (P2-P3) 2 +1 / (P4-P3) 2 +1 / (P6-P3) 2 , and then divide that value again by the weight of each neighboring point ({1 / (P2-P3) 2} / total_weight, {1 / (P4-P3) 2} / total_weight, {1 / (P6-P3) 2} / total_weight) Normalizes the weight values.

[0226] To reiterate, the weight of each neighboring point is (1 / (P2-P3) 2 , 1 / (P4-P3) 2 , 1 / (P6-P3) 2 ) is the total sum of the weights of neighboring points (total_weight) = 1 / (P2-P3) 2 +1 / (P4-P3) 2 +1 / (P6-P3) 2 Divide by ({1 / (P2-P3) 2} / total_weight, {1 / (P4-P3) 2} / total_weight, {1 / (P6-P3) 2} / total_weight) can be normalized.

[0227] If cross-attribute prediction is used, an additional weight recalculation process may be included. Cross-attribute prediction is a method of calculating weights using already restored attribute values. In other words, it is a technique that can be used for mixed data of LiDAR and RGB. Therefore, after RGB is compressed, the weights for LiDAR can be calculated using the RGB values ​​and the distance between the current point and neighboring points. Conversely, after LiDAR is first encoded / decoded, the weights for RGB can be calculated using the LiDAR values ​​and the distance between the current point and neighboring points. Afterwards, normalization can be performed in the same manner as the normalization method described above.

[0228] 4) Attributes can be predicted using a predictor. The predicted result can be the average of the weighted values ​​of the attributes of registered neighboring points, or a specific point can be used. The method used can be determined by pre-calculating the compressed result values ​​and then selecting the method that produces the smallest stream.

[0229] 5) The residual between the attribute values ​​of the point and the attribute values ​​predicted by the predictor of the point can be encoded and signaled to the receiver together with the method selected by the predictor.

[0230] In the decoder, the same process as steps 1) to 3) is performed, and in step 4), the transmitted prediction method is decoded to predict the attribute value according to that method. In step 5), the transmitted residual value is decoded and the attribute value can be restored by adding the value predicted through step 4).

[0231] In the embodiments, the prediction of lifting transformation is performed using lifting weights suitable for lidar content, thereby improving encoding efficiency for point cloud attribute compression through high prediction performance.

[0232] The location information of LiDAR content contains a lot of noise. Predicting using the locations of points already containing noise reduces prediction performance. To address this, the embodiments utilize the characteristics of the LiDAR content acquisition device. LiDAR acquisition devices typically generate points using a structure that transmits and receives laser signals. Since only one point can be generated from a single laser signal, multiple points cannot be generated at the same azimuth. In other words, while LiDAR content is 3D data, it resembles 2D data when projected onto a plane. Therefore, points are more likely to exist in a plane or direction, such as at the same radius and azimuth, or at the same elevation and radius, or at the same elevation and azimuth. By leveraging this characteristic, points on the same plane or direction are given relatively high weights because they are noise-free, while points on the diagonal are more likely to contain noise and are given relatively low weights. This allows IDW to be performed to achieve high prediction performance.

[0233] Variations and combinations between embodiments are possible. Terms used in the embodiments can be understood based on their intended meaning within the scope widely used in the relevant field.

[0234] Cross-attribute weight generation can be performed and applied in both PCC encoders / decoders.

[0235] (1) Distance-based neighbor point search

[0236] Neighboring point groups can be determined using the distance and distribution between the current point and surrounding points. In this case, points with close distances can be selected. Furthermore, the distribution can be determined to include neighboring points in groups with different directions to prevent neighboring points from being concentrated in one direction among the selected multiple neighboring points.

[0237] (2) Lifting weight calculation method

[0238] A neighboring point group can be determined for each point using the method described in (1). A lifting weight can be calculated for the determined neighboring point group.

[0239] As an example, the L1 distance, which is the sum of the absolute values, can be used. The distance value D of the Inverse Distance Weighting (IDW) can use the weighted sum of the L1 values ​​of the point locations and attribute values. For example, the L1 distances D0, D1, and D2 of the neighboring point groups P0, P1, and P2 for the point P can be calculated as D0=|P[0]-P0[0]| + |P[1]-P0[1]| + |P[2]-P1[2]|, D1=|P[0]-P1[0]| + |P[1]-P1[1]| + |P[2]-P1[2]|, D2=|P[0]-P2[0]| + |P[1]-P2[1]| + |P[2]-P2[2]|, respectively.

[0240] To reiterate, according to one embodiment, the distance of each neighboring point to a point can be calculated using the L1 distance. The L1 distance can be calculated as the sum of the absolute values ​​of the distances between two points for each axis. For example, the L1 distance D0 of a neighboring point P0 to a point P can be calculated by summing the absolute values ​​of the distances between the two points for the first axis (|P[0]-P0[0]|), the absolute values ​​of the distances between the two points for the second axis (|P[1]-P0[1]|), and the absolute values ​​of the distances between the two points for the third axis (|P[2]-P1[2]|).

[0241] As another example, the L2 distance, which is the sum of squares, can be used. For example, the L2 distances D0, D1, and D2 of the neighboring point groups P0, P1, and P2 for point P are D0=(P[0]-P0[0]) 2 + (P[1]-P0[1]) 2 + (P[2]-P1[2]) 2 , D1=(P[0]-P1[0]) 2 + (P[1]-P1[1]) 2 + (P[2]-P1[2]) 2 , D2=(P[0]-P2[0]) 2 + (P[1]-P2[1]) 2 + (P[2]-P2[2]) 2 can be calculated as follows.

[0242] To reiterate, in one embodiment, the distance between each neighboring point to a point can be calculated using the L2 distance. The L2 distance can be calculated as the sum of the squares of the distances between two points for each axis. For example, the L2 distance D0 of a neighboring point P0 to a point P is the square of the distance between the two points for the first axis (P[0]-P0[0]). 2, , the square of the distance between two points on the second axis (P[1]-P0[1]) 2 , and the square of the distance between two points on the third axis (P[2]-P1[2]) 2 can be calculated by adding them together.

[0243] As another example, the squared L1 distance, which is the sum of the absolute values, can be used. For example, the squared L1 distances D0, D1, and D2 of the neighboring point groups P0, P1, and P2 for a point P are each D0=(|P[0]-P0[0]| + |P[1]-P0[1]| + |P[2]-P1[2]|). 2 , D1=(|P[0]-P1[0]| + |P[1]-P1[1]| + |P[2]-P1[2]|) 2 , D2=(|P[0]-P2[0]| + |P[1]-P2[1]| + |P[2]-P2[2]|) 2 can be calculated as follows.

[0244] To reiterate, according to one embodiment, the distance of each neighboring point to a point can be calculated using the squared L1 distance. The squared L1 distance can be calculated as the square of the sum of the absolute values ​​of the distances between two points for each axis. Also, the squared L1 distance and the L1 squared distance can be used as terms referring to the same calculation method. For example, the squared L1 distance D0 of a neighboring point P0 to a point P can be calculated as the square of the sum of the absolute values ​​of the distances between the two points for the first axis (|P[0]-P0[0]|), the absolute values ​​of the distances between the two points for the second axis (|P[1]-P0[1]|), and the absolute values ​​of the distances between the two points for the third axis (|P[2]-P1[2]|).

[0245] As another example, the L2 distance and the squared L1 distance can be used adaptively for attributes. For example, if the attribute currently being encoded / decoded is color, the L2 distance can be used. Conversely, if the attribute currently being encoded / decoded is a reflection coefficient, the L1 distance can be used. In this case, information indicating whether the current attribute value is RGB or a reflection coefficient can be transmitted from the encoder to the decoder.

[0246] As another embodiment, one of the methods for calculating L1, L2, or squared L1 distances may optionally be used.

[0247] For example, one of three distances may be used in the encoder via a user input parameter. This information may be transmitted from the encoder to the decoder as part of the bitstream.

[0248] Alternatively, the reconstructed geometric information from the encoder / decoder can be analyzed to select one of three distances. For example, if the distribution of the difference vector between the current point and its neighboring points is examined and points are densely located along a specific plane or axis, the squared L1 distance can be selected. Conversely, if points are distributed in various directions and not concentrated in a specific direction, the L2 distance can be selected.

[0249] Alternatively, the distance can be selected using the relationship between the current point and its neighboring points. In this case, the relationship quantizes the distance between the current point and its neighboring points, and one of three distances can be selected based on the quantized values. Different distances can be mapped depending on the quantized values. For example, if the distance between the neighboring point and the current point is quantized to 10 and all quantized values ​​have the same value, the L2 distance can be used. Conversely, if one quantized value is relatively large, the L1 distance can be used. Alternatively, if the three quantized values ​​are equally spaced, the squared L1 distance can be used.

[0250] Alternatively, different distances can be used depending on the LOD level. For example, the L2 distance can be used when the LOD is lower than a certain level, and the L1 square distance can be used otherwise. Alternatively, the L1 square distance can be used when the LOD is lower than a certain level, and the L2 distance can be used otherwise. In this case, the specific LOD value can be transmitted from the encoder to the decoder. The encoder can receive the specific LOD value from the user. Alternatively, it can be determined based on the distribution of already restored points.

[0251] To enhance the effectiveness of the proposed method, a process of transforming points acquired from the same sensor of the lidar into the same plane, for example, converting Cartesian coordinates into spherical coordinates, may be performed first. In the embodiments, distance-based weights were calculated for point cloud data distributed on spherical coordinates, but more generally, this can be extended by a method such as assigning relatively high weights to points acquired by the same lidar sensor and relatively low weights to points acquired by different lidar sensors.

[0252] The method according to the embodiments may include: 1) a distance-based neighbor point search step; and / or 2) a cross-attribute weight calculation step.

[0253] 1) Distance-based neighbor point search step:

[0254] Methods according to embodiments can determine neighboring point groups using the distance and distribution between a current point and surrounding points. In this case, points with close distances can be selected. Furthermore, the distribution can be determined to include neighboring points in groups in different directions to prevent neighboring points from being concentrated in one direction among the selected plurality of neighboring points.

[0255] 2) Cross-attribute weight calculation step:

[0256] Using the aforementioned 1) distance-based neighbor point search method, a neighboring point group can be determined for each point. For each determined neighboring point group, weights can be calculated using other previously restored attribute values.

[0257] For example, the lifting weights of LiDAR can be calculated using the RGB values ​​that have already been encoded / decoded. For example, the lifting weights of RGB can be calculated using the reflectance coefficients of LiDAR that have already been encoded / decoded.

[0258] For example, the L1 distance, which is the sum of the absolute values, can be used. The distance value D of IDW can be a weighted sum of the L1 values ​​of the point location and the attribute value. In this case, the Lagrangian multiplier can be used to match the scale of the two values, and the Lagrangian multiplier can have different values ​​depending on the maximum and minimum values ​​of the attribute value and QP.

[0259] For example, the square of the L1 distance, which is the sum of the absolute values, can be used. The distance value D of IDW can be a weighted sum of the squares of the L1 values ​​of the point location and the attribute value. At this time, the Lagrangian multiplier can be used to match the scale of the two values, and the Lagrangian multiplier can have different values ​​depending on the maximum and minimum values ​​of the attribute value and QP.

[0260] For example, the L2 distance, which is the sum of squares, can be used. The distance value D of IDW can be a weighted sum of the L2 distances of the point location and the attribute value. At this time, the Lagrangian multiplier can be used to match the scale of the two values, and the Lagrangian multiplier can have different values ​​depending on the maximum and minimum values ​​of the attribute value and QP.

[0261] For example, a table for Gaussian functions can be used to calculate distance values. Gaussian functions typically have different distributions depending on their mean and standard deviation. Therefore, the mean is 0, and values ​​for various standard deviations can be stored and utilized in a table. Here, different tables may be used because the Gaussian distribution for spatial distances and the Gaussian distribution for attribute values ​​may differ. Furthermore, point clouds represent a very large space and can have significantly large position values. Therefore, the spatial Gaussian distribution table may only store some values ​​for which spatial distances are quantized. Here, the standard deviation of the spatial Gaussian distribution may vary depending on the LOD index. Alternatively, it may vary depending on the DIST2 value, which is the distance between representative points obtained from the encoder along with the LOD index. Alternatively, it may vary depending on the geometry QP value. Furthermore, the standard deviation of the attribute Gaussian distribution may vary depending on the attribute QP value.

[0262] We describe how to select a set of predictors and generate prediction weights.

[0263] The encoding / decoding method according to the embodiments may include or perform a method for selecting a set of predictors and generating prediction weights.

[0264] After the predictor search for a refinement point is completed, the set of predictors is selected, a weight is calculated for each eligible predictor, and the predictors are sorted according to the weight.

[0265] If necessary, the size of the predictor set is limited to pred_set_size_minus1 + 1 elements by discarding the furthest predictors.

[0266] PredCnt[PtIdx] = Min(pred_set_size_minus1 + 1, PredCnt[PtIdx])

[0267] When cross_attr_prediction_enabled_this_type is 0, the predictor weights are computed using the biased squared distance between each predictor and the current point.

[0268] for (ni = 0; ni < PredCnt[PtIdx]; ni++)

[0269] dist[ni] = BiasedNorm2(PtIdx, PredPtIdx[PtIdx][ni], 0, PredPtRef[PtIdx][ni])

[0270] + PredPtRef[PtIdx][ni] ? 1:0

[0271] If the first predictor spatially matches the current point, all other predictors are discarded.

[0272] if (dist[0] == 0)

[0273] PredCnt[PtIdx] = 1

[0274] When lod scalability is enabled (lod_scalability_enabled) is 1, predictors with an unbiased squared distance greater than the threshold are discarded.

[0275] if (lod_scalability_enabled) {

[0276] threshold = 3 × (pred_max_range_minus1 + 1) << 2 × Lvl

[0277] for (ni = 1; ni < PredCnt[PtIdx]; ni++)

[0278] if (Norm2(ptIdx, PredPtIdx[PtIdx][ni], 0, 0) > threshold) {

[0279] PredCnt[PtIdx] = ni

[0280] break

[0281] }

[0282] }

[0283] When cross-attribute prediction enabled dis type (cross_attr_prediction_enabled_this_type) is 1, the predictor weights are calculated using the overall distance between each predictor and the current point.

[0284] for (ni = 0; ni < PredCnt[PtIdx]; ni++

[0285] dist[ni] = overAllDist[ni]

[0286] The overall distance is a weighted combination of the geometric distance and the attribute distance, stored in the array overAllDist[ni], where ni = 0 .. PredCnt[ PtIdx ] - 1. The geometric distance is defined as the spatial distance, which is computed by a biased l^1 norm weighted by the PredBias. The attribute distance is defined as the sum of the absolute differences in the attribute values ​​for each component.

[0287] overAllDist[ni] = geomDis + attrWeight * attrDis

[0288] where

[0289] geomDis = BiasedNorm1(PtIdx, PredPtIdx[PtIdx][ni], 0, 0)

[0290] attrDis = 0

[0291] for(i = 0; i < attr_components_minus1[refAttrIdx] + 1; i++)

[0292] attrDis += Abs(RecCloudAttr[ptIdx][refAttrIdx][i] -

[0293] RecCloudAttr[PredPtIdx[PtIdx][ni]][refAttrIdx][i])

[0294] The attribute weight (attrWeight), defined as the weight of the attribute distance, is determined as follows:

[0295] attrWeight = lambda * maxGeom / maxAttr

[0296] where

[0297] lambda = attr_label[refAttrIdx] ? (4011 - 67 * (attr_primary_qp_minus4 + 4)) >> 15:

[0298] (1939 - 33 * (attr_primary_qp_minus4 + 4)) >> 12

[0299] for (k = 0; k < 3; j++)

[0300] maxGeom += RefCloudAttrBboxSize[k]

[0301] maxAttr = (attr_components_minus1[refAttrIdx] + 1) *

[0302] 1 << (attr_bitdepth_minus1[refAttrIdx] + 1)

[0303] Here, maxGeom represents the sum of the length, width, and height of the slice bounding box, and maxAttr represents the maximum possible value of the encoded attribute used to decode the current property.

[0304] If the first predictor is equal to the current point, all other predictors are discarded.

[0305] if (dist[0] == 0)

[0306] PredCnt[PtIdx] = 1

[0307] If cross_attr_prediction_enabled_this_type is 1, the predictors are sorted in ascending order by their overall distance to the current point. If cross_attr_prediction_enabled_this_type is 0, the predictors are reordered by their biased squared distance to the current point.

[0308] The array order is dist[order[ i]], for i = 0 .. PredCnt[PtIdx] - 1, which is an ascending stable sort of the array dist.

[0309] The members of the predictor set and the dist array are arranged according to the elements of the array order.

[0310] Predictor distances are normalized to the smallest distance to generate initial weights.

[0311] n = Max(0, IntLog2(dist[0] - 8))

[0312] for (ni = 0; ni < PredCnt[PtIdx]; ni++)

[0313] weight[ni] = DivExp2Up(dist[ni], n)

[0314] Predictors with weights greater than or equal to 256 times the smallest weight are discarded.

[0315] if (PredCnt[PtIdx] == 3 && weight[2] ≥ 256 × weight[0])

[0316] PredCnt[PtIdx] = 2

[0317] if (PredCnt[PtIdx] == 2 && weight[1] ≥ 256 × weight[0])

[0318] PredCnt[PtIdx] = 1

[0319] 마지막 가중치는 다음과 같이 도출된다.

[0320] if (PredCnt[PtIdx] == 1)

[0321] PredWeight[PtIdx][0] = 256

[0322] if (PredCnt[PtIdx] == 2) {

[0323] PredWeight[PtIdx][1] = Div(weight[0], weight[0] + weight[1], 8)

[0324] PredWeight[PtIdx][0] = 256 - PredWeight[PtIdx][1]

[0325] }

[0326] if (PredCnt[PtIdx] == 3) {

[0327] d1d2 = weight[1] × weight[2]

[0328] d0d2 = weight[0] × weight[2]

[0329] d0d1 = weight[0] × weight[1]

[0330] sum = d1d2 + d0d2 + d0d1

[0331] PredWeight[PtIdx][2] = Div(d0d1, sum, 8)

[0332] PredWeight[PtIdx][1] = Div(d0d2, sum, 8)

[0333] PredWeight[PtIdx][0] = 256 - PredWeight[PtIdx][1] - PredWeight[PtIdx][2]

[0334] }

[0335] The pred_dist_bias_minus1_xyz[k] plus 1 specifies a factor used to weight the kth XYZ component of the distance vector between two point positions used to compute the inter-point distance in the predictor search for a single refinement point. The expression PredBias[k] specifies a factor for the kth STV component.

[0336] PredBias[k] := pred_dist_bias_minus1_xyz[StvToXyz[k]] + 1

[0337] Prediction distance bias minus 1 (pred_dist_bias_minus1_xyz[k]) can be applied when attribute coding type (attr_coding_type) is 1 or 2.

[0338] Fig. 11 shows an encoder according to embodiments.

[0339] The encoding method and device according to the embodiments (the transmission device (10000), the point cloud video encoder (10002), the transmitter (10003) of FIG. 1, the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the encoders of FIGS. 11 to 12, the encoders of FIGS. 15 to 18, the bitstream and syntax generation of FIGS. 19 to 23, and the encoding method of FIG. 24) can encode PCC data according to the embodiments of FIG. 11.

[0340] Figure 11 is a block diagram of a PCC data encoder according to embodiments. The embodiments can be applied not only to the encoder described in the embodiments below, but also to the compression of data acquired based on LiDAR. Each component can correspond to hardware, software, a processor, and / or a combination thereof. PCC data can be input to the encoder and encoded to output a geometry information bitstream and an attribute information bitstream. Detailed components according to the embodiments can refer to blocks indicated by hatching.

[0341] The data input section can read and set input data (e.g., ply, configuration file, etc.).

[0342] The coordinate system transformation unit can support coordinate system changes, such as changing the xyz axes or transforming from the xyz rectangular coordinate system to the spherical coordinate system.

[0343] The geometry information transformation quantization processing unit can adjust the scale by multiplying the x, y, and z values ​​of the geometry location of the point cloud point by the scale (scale = geometry quantization value) setting.

[0344] The spatial partition can be divided into tiles or slices for area-specific access or parallel processing of content.

[0345] The geometry encoding unit can encode spatially divided geometry information to generate a geometry information bitstream.

[0346] The color conversion processor can support attribute type conversion, such as changing RGB color to YUV.

[0347] The color rescaling unit can predict attribute values ​​appropriate for the changed location when the geometry is scaled and the location information values ​​are changed.

[0348] The attribute information encoding unit can receive color-resized original attribute information, restored geometry information, and a reference frame as input and perform encoding to generate an attribute information bitstream.

[0349] The reference frame generation unit can store restored geometry and restored attribute information in a reference frame buffer and transfer reference frame data from the reference frame to other modules.

[0350] Fig. 12 shows a block diagram of an attribute information encoding unit according to embodiments.

[0351] Fig. 12 shows a block diagram of an attribute information encoding unit included in the encoder of Fig. 11.

[0352] The embodiments can be applied to attribute compression for data acquired based on lidar, as well as the attribute encoder described in the examples below. Each component may correspond to hardware, software, a processor, and / or a combination thereof. Detailed components according to the embodiments may refer to blocks indicated by hatching.

[0353] The LOD generation unit can input segmented point cloud data, sample points, and generate point groups for each LOD. The generated point groups for each LOD can be passed to the nearest point group determination unit.

[0354] The nearest point group determination unit can use the points included in a point group of a higher LOD than the current LOD to determine the nearest point group for points within the point group of the current LOD using the received point group for each LOD. At this time, whether to include the point in the nearest point group can be determined based on the distance between the current point and a point included in the higher LOD point group. The determined nearest point group can be transmitted to the weight calculation unit.

[0355] The weight calculation unit can calculate weights used in lifting transformation or prediction transformation using points within the received closest point group.

[0356] The lifting transform unit can generate transform coefficients by performing frequency transformation on attribute information through LOD level-specific prediction and update processes using the generated LOD. The generated transform coefficients can be quantized and transmitted to the attribute information entropy encoding unit. Additionally, an internal inverse transformation can be performed to output restored attribute information.

[0357] Predictive transformation can be performed to predict the current point using neighboring points determined during the LOD generation process. The predicted attribute information and the original attribute information can be converted into differential values ​​called transform coefficients, which can be quantized and passed to the attribute information entropy encoding unit. Furthermore, attribute information can be restored and output through inverse quantization and predictive inverse transformation.

[0358] RAHT can be performed without LOD parameters. It receives segmented point cloud data and performs Region Adaptive Hierarchical Transform (RAHT) to transform it into the frequency domain, generating transform coefficients. These transform coefficients are then quantized and passed to the attribute information entropy encoding unit.

[0359] Fig. 13 shows a decoder according to embodiments.

[0360] The decoder of Fig. 13 can follow the reverse process of the encoder of Fig. 11. Fig. 13 can represent a block diagram of a PCC data decoder.

[0361] The decoding method and device 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. 10, FIG. 11, receiving device of FIG. 13, device of FIG. 14, decoder of FIG. 13 to FIG. 14, decoder of FIG. 15 to FIG. 18, bitstream and syntax parsing of FIG. 19 to FIG. 23, decoding method of FIG. 25) can decode PCC data according to the embodiments of FIG. 13.

[0362] The embodiments can be applied to decoding bitstreams generated based on lidar, as well as the decoder described in the embodiments below. Each component can correspond to hardware, software, a processor, and / or a combination thereof. The decoder inputs encoded geometry information bitstreams and attribute information bitstreams, and decoded and restored PCC data can be output. Detailed components according to the embodiments can refer to blocks indicated by hatching.

[0363] The geometry information decoding unit can receive a geometry information bitstream and decode it to restore the geometry information.

[0364] The coordinate system inversion unit can restore the changed xyz axes or inversely transform the transformed coordinate system into the xyz orthogonal coordinate system.

[0365] The geometry information conversion dequantization processing unit can restore the signaled scale (scale = geometry quantization value) and apply it to the geometry location x, y, and z values ​​of the restored point.

[0366] Attribute residual information entropy decoding can entropy decode attribute bitstreams.

[0367] The attribute information decryption unit can receive an attribute information bitstream and decrypt it to restore the attribute information.

[0368] The color inversion processing unit can restore the converted attributes to RGB colors.

[0369] The reference frame generation unit can store restored geometry and restored attribute information in a reference frame buffer and transfer reference frame data from the reference frame to other modules.

[0370] Figure 14 illustrates decoding of attribute data according to embodiments.

[0371] Fig. 14 shows a block diagram of an attribute information decoding unit included in the decoder of Fig. 13.

[0372] The embodiments can be applied to attribute decoding of bitstreams generated based on LiDAR, as well as the attribute decoder described in the examples below. Each component may correspond to hardware, software, a processor, and / or a combination thereof. (See the hatched blocks for detailed components according to the embodiments.)

[0373] The attribute information entropy decoding unit can receive an attribute information bitstream, decode it, and restore the transform coefficients.

[0374] The LOD generation unit can input segmented point cloud data, sample points, and generate point groups by LOD. The generated point groups by LOD can be passed to the nearest point group determination unit. The LOD generation unit can be performed when LOD parameters are present.

[0375] The nearest point group determination unit can use the points included in a point group of a higher LOD than the current LOD to determine the nearest point group for points within the point group of the current LOD using the received point group for each LOD. At this time, whether to include the point in the nearest point group can be determined based on the distance between the current point and a point included in the higher LOD point group. The determined nearest point group can be transmitted to the weight calculation unit.

[0376] The weight calculation unit can calculate weights used in lifting transformation or prediction transformation using points within the received closest point group.

[0377] The lifting inverse transform can restore attribute information by inversely transforming the input transform coefficients and performing frequency inverse transform through the generated LOD and LOD level-by-LOD level update and prediction process.

[0378] Predictive inverse transformation can restore attributes by combining the current point with the predicted value and the inverse transformation value of the input transformation coefficient using the neighboring points determined during the LOD generation process.

[0379] Inverse RAHT can restore attribute information by performing the reverse process of RAHT by inputting restored geometry information and transformation coefficients.

[0380] Figure 15 shows weight calculation according to embodiments.

[0381] The encoder and decoder of FIGS. 11 to 14 can further perform a method of calculating weights as in FIG. 15 for attribute encoding and decoding.

[0382] According to one embodiment, FIG. 15 may represent a flowchart of the weight calculation unit of FIGS. 12 and 14. Detailed components according to the embodiments may refer to blocks indicated by hatching.

[0383] The weight calculation unit receives the closest point group, checks whether the current attribute is a reflection coefficient, and L2 distance weight calculation or L1 square distance weight calculation can be performed.

[0384] If the current attribute is a reflection coefficient, L1 squared distance weighting calculation can be performed.

[0385] If the current attribute is a color (RGB), L2 squared distance weighting calculation can be performed.

[0386] Alternatively, if the current attribute is a color (RGB), L2 distance weighting calculation can be performed.

[0387] Figure 16 shows weight calculation according to embodiments.

[0388] The encoder and decoder of FIGS. 11 to 14 can further perform a method of calculating weights as in FIG. 16 for attribute encoding and decoding.

[0389] According to one embodiment, FIG. 16 may represent a flowchart of the weight calculation unit of FIGS. 12 and 14.

[0390] Detailed components according to the embodiments can be referenced by blocks indicated by hatching.

[0391] The weight calculation unit can receive a group of nearest points and perform L2 distance weight calculation or L1 square distance weight calculation.

[0392] First, we can check the flag indicating whether a weight calculation method using multiple distances was used.

[0393] If a single weight calculation method is used, L2 distance weight calculation can be performed.

[0394] If multiple weighting methods were used, you can additionally check whether the current attribute is a reflection coefficient or a color.

[0395] If the current attribute is a reflection coefficient, L1 squared distance weighting calculation can be performed.

[0396] If the current attribute is a color (RGB), L2 squared distance weighting calculation can be performed.

[0397] Alternatively, if the current attribute is a color (RGB), L2 distance weighting calculation can be performed.

[0398] Figure 17 shows weight calculation according to embodiments.

[0399] The encoder and decoder of FIGS. 11 to 14 can further perform a method of calculating weights as in FIG. 17 for attribute encoding and decoding.

[0400] According to one embodiment, FIG. 17 may represent a flowchart of the weight calculation unit of FIGS. 12 and 14. Detailed components according to the embodiments may refer to blocks indicated by hatching.

[0401] The weight calculation unit can receive a group of nearest points and perform L2 distance weight calculation, L1 square distance weight calculation, or L1 distance weight calculation.

[0402] First, we can check the flag indicating whether a weight calculation method using multiple distances was used.

[0403] If a single weight calculation method is used, L2 distance weight calculation can be performed.

[0404] If multiple weight calculation methods were used, the distance type can be checked.

[0405] If the distance type is L1 square distance, L1 square distance weight calculation can be performed.

[0406] If the distance type is L1 distance, L1 distance weight calculation can be performed.

[0407] If the distance type is L2 distance, L2 distance weight calculation can be performed.

[0408] To reiterate, the bitstream may include flag information indicating whether the distance between two points uses multiple distance calculation methods. The weight calculation unit may check the flag information.

[0409] For example, if the flag indicates a first value, the distance between two points can be calculated using a single method. In one embodiment, the distance between two points can be calculated using the L2 distance, and a weight can be calculated based on the L2 distance.

[0410] Additionally, if the flag indicates a second value, the distance between two points can be calculated using multiple distance calculation methods. In this case, the distance type information can be checked and the distance between the two points can be calculated based on the value indicated by the distance type information. The distance type information can indicate one of the following: L1 distance, L1 square distance, or L2 distance.

[0411] Figure 18 shows weight calculation according to embodiments.

[0412] The encoder and decoder of FIGS. 11 to 14 can further perform a method of calculating weights as in FIG. 18 for attribute encoding and decoding.

[0413] According to one embodiment, FIG. 18 may represent a flowchart of the weight calculation unit of FIGS. 12 and 14. Detailed components according to the embodiments may refer to blocks indicated by hatching.

[0414] The weight calculation unit can receive a group of nearest points and perform L2 distance weight calculation or L1 square distance weight calculation.

[0415] You can compare the current LOD and threshold LOD values.

[0416] Here, the threshold LOD value can be a value transmitted from the encoder, or a value determined by mutual agreement between the encoder and decoder.

[0417] If the current LOD is less than the threshold LOD, L2 distance weighting calculation can be performed.

[0418] In the opposite case, L1 square distance weighting calculation can be performed.

[0419] To explain again, the weight calculation unit can calculate the distance between two points and calculate the weight by comparing the LOD associated with the current point with a preset threshold LOD value and applying different distance calculation methods depending on whether the LOD associated with the current point is smaller than the preset threshold LOD.

[0420] Figure 19 shows a bitstream according to embodiments.

[0421] The encoding method and device according to the embodiments (the transmission device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the encoders of FIGS. 11 to 12, the encoders of FIGS. 15 to 18, the bitstream and syntax generation of FIGS. 19 to 23, and the encoding method of FIG. 24) can encode point cloud data, generate parameter information (which may be referred to as signaling information or syntax elements), and generate and transmit the bitstream of FIG. 19 including the encoded point cloud data and the parameter information.

[0422] The decoding method and device 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. 10, FIG. 11, receiving device of FIG. 13, device of FIG. 14, decoder of FIG. 13 to FIG. 14, decoder of FIG. 15 to FIG. 18, bitstream and syntax parsing of FIG. 19 to FIG. 23, decoding method of FIG. 25, etc.) can receive the bitstream of FIG. 19 and decode point cloud data based on parameter information.

[0423] In order to add / perform embodiments, related information can be signaled within the bitstream as in Fig. 19. Signaling information according to embodiments can be used at a transmitter or receiver, etc.

[0424] The encoded point cloud configuration is as follows. A point cloud data encoder that performs geometry encoding and / or attribute encoding processes can generate the encoded point cloud (or bitstream including the point cloud) as follows. In addition, signaling information regarding the point cloud data can be generated and processed by the metadata processing unit of the point cloud data transmission device and included in the point cloud as follows.

[0425] Each abbreviation stands for the following. Each abbreviation may be referred to by other terms within the scope of equivalent meaning.

[0426] SPS: Sequence Parameter Set

[0427] GPS: Geometry Parameter Set

[0428] APS: Attribute Parameter Set

[0429] TPS: Tile Parameter Set

[0430] Geom: Geometry bitstream = geometry slice header + [geometry PU header + geometry PU data] | geometry slice data

[0431] Attr: Attribute bitstream = attribute data unit header + [attribute PU header + attribute PU data] | attribute data unit data

[0432] Optional information related to lifting weight calculation can be added to SPS and APS and signaled.

[0433] Information regarding lifting weight calculation options can be signaled by adding them to the Tile Parameter Set (TPS) or the Attribute header for each slice.

[0434] Tiles or slices are provided to allow point clouds to be divided and processed by region.

[0435] When dividing by region, you can set different options for generating neighboring point sets for each region, providing a choice between low complexity and somewhat lower reliability, or conversely, high complexity but high reliability. These settings can vary depending on the receiver's processing capacity.

[0436] Therefore, when a point cloud is divided into tiles, different options can be applied to each tile.

[0437] When a point cloud is divided into slices, different options can be applied to each slice.

[0438] Figure 20 shows a sequence parameter set (SPS) according to embodiments.

[0439] Figure 20 illustrates the syntax of SPS in the bitstream of Figure 19.

[0440] Optional information related to lifting weight calculation can be added to the Sequence Parameter Set and signaled. By combining the signaling information in the highlighted rows of Figure 20, efficient signaling can be achieved to support inter-prediction compression. The names of the signaling information can be understood within the scope of their meaning and function.

[0441] Profile (profile_idc): Indicates that the bitstream conforms to a profile.

[0442] Profile Compatibility Flags (profile_compatibility_flags): If this value is 1, it indicates that the bitstream complies with the profile whose profile_idc is j.

[0443] Number of attribute sets (sps_num_attribute_sets): Indicates the number of coded attributes in the bitstream. The sps_num_attribute_sets value ranges from 0 to 63.

[0444] Attribute dimension (attribute_dimension[ i ]): Indicates the number of components of the i-th attribute.

[0445] Attribute instance ID (attribute_instance_id[ i ]): Represents the instance ID of the i-th attribute.

[0446] Attribute Multiple Distance Lifting Weight Enabled (sps_attr_multiple_distance_lifting_weight_enabled): Indicates whether to apply lifting weight calculation using multiple distances to the sequence.

[0447] To reiterate, sps_attr_multiple_distance_lifting_weight_enabled can indicate whether lifting weights are calculated using multiple distance calculation methods to calculate the distance between two points in a sequence. For example, if sps_attr_multiple_distance_lifting_weight_enabled is 0, lifting weights can be calculated using a single distance calculation method. Alternatively, if sps_attr_multiple_distance_lifting_weight_enabled is 1, lifting weights can be calculated using multiple distance calculation methods.

[0448] Attribute Distance Type (sps_attr_distance_type): Indicates the type of distance used to calculate the lifting weight for a sequence. For example, sps_attr_distance_type 0 indicates the L2 distance, sps_attr_distance_type 1 indicates the L1 square distance, and sps_attr_distance_type 2 indicates the L1 distance.

[0449] Figure 21 illustrates an attribute parameter set (APS) according to embodiments.

[0450] Figure 21 illustrates the syntax of APS in the bitstream of Figure 19.

[0451] During the attribute information encoding / decoding process, optional information related to lifting weight calculation can be added to the Attribute Parameter Set and signaled. By combining the signaling information in the highlighted rows of Figure 21, inter prediction can be efficiently signaled. The name of the signaling information can be understood within the scope of its meaning and function.

[0452] Attribute Parameter Set ID (aps_attr_parameter_set_id): Provides an APS identifier that other syntax elements can reference.

[0453] Sequence Parameter Set ID (aps_seq_parameter_set_id): Indicates the sps_seq_parameter_set_id value for the active SPS.

[0454] Attribute coding type (attr_coding_type): Indicates how the attribute is coded. Valid values ​​are 0 to 3. For example, an attr_coding_type value of 0 can indicate RAHT, an attr_coding_type value of 1 can indicate a predictive transform, an attr_coding_type value of 2 can indicate a lifting transform, and an attr_coding_type value of 3 can indicate raw attribute data. Other values ​​are reserved for future use in ISO / IEC. Decoders conforming to this version of this document must ignore (remove from the bitstream and discard) attribute data units coded with reserved values ​​of attr_coding_type.

[0455] Attribute multiple distance lifting weight enable (aps_attr_multiple_distance_lifting_weight_enabled): Indicates whether lifting weight calculations using multiple distances are applied to the frame.

[0456] To reiterate, aps_attr_multiple_distance_lifting_weight_enabled can indicate whether lifting weight calculations are applied using multiple distance calculation methods to calculate the distance between two points in the frame. For example, if aps_attr_multiple_distance_lifting_weight_enabled is 0, lifting weights can be calculated using a single distance calculation method. Also, if aps_attr_multiple_distance_lifting_weight_enabled is 1, lifting weights can be calculated using multiple distance calculation methods.

[0457] Attribute Distance Type (aps_attr_distance_type): Indicates the type of distance used to calculate lifting weights for a frame. For example, aps_attr_distance_type 0 indicates the L2 distance, aps_attr_distance_type 1 indicates the squared L1 distance, and aps_attr_distance_type 2 indicates the L1 distance.

[0458] Figure 22 illustrates a tile parameter set (TPS) according to embodiments.

[0459] Figure 22 illustrates the syntax of TPS in the bitstream of Figure 19.

[0460] Optional information related to lifting weight calculation can be added to the tile parameter set and signaled. By combining the signaling information in the highlighted rows of Figure 22, it can be efficiently signaled to support reference frame buffers. The names of the signaling information can be understood within the scope of the meaning and function of the signaling information.

[0461] Number of tiles (num_tiles): Indicates the number of tiles signaled in the bitstream. If this value is missing, num_tiles is assumed to be 0.

[0462] Tile bounding box offset x[i] (tile_bounding_box_offset_x[ i ]): Indicates the x-offset of the ith tile in Cartesian coordinates. If this value is missing, the value of tile_bounding_box_offset_x

[0000] is inferred to be sps_bounding_box_offset_x.

[0463] Tile bounding box offset y[i] (tile_bounding_box_offset_y[ i ]): Indicates the y-offset of the ith tile in Cartesian coordinates. If this value is missing, the value of tile_bounding_box_offset_y

[0000] is inferred to be sps_bounding_box_offset_y.

[0464] Tile bounding box offset z[i] (tile_bounding_box_offset_z[ i ]): Indicates the z-offset of the ith tile in Cartesian coordinates. If this value is missing, the value of tile_bounding_box_offset_z

[0000] is inferred to be sps_bounding_box_offset_z.

[0465] Tile Attribute Multiple Distance Lifting Weight Enabled (tile_attr_multiple_distance_lifting_weight_enabled): Indicates whether lifting weight calculations using multiple distances are applied to tiles.

[0466] Tile Attribute Distance Type (tile_attr_distance_type): Indicates the type of distance used to calculate lifting weights for tiles. For example, if tile_attr_distance_type is 0, it can indicate the L2 distance, if tile_attr_distance_type is 1, it can indicate the squared L1 distance, and if tile_attr_distance_type is 2, it can indicate the L1 distance.

[0467] Figure 23 illustrates an attribute data header according to embodiments.

[0468] Figure 23 illustrates the syntax of an attribute slice header in the bitstream of Figure 19.

[0469] Optional information related to lifting weight calculation can be signaled by adding it to the Attribute Slice Header. By combining the signaling information in the highlighted rows of Figure 23, it can be efficiently signaled to support reflectivity attribute compression. The name of the signaling information can be understood within the scope of its meaning and function.

[0470] Attribute parameter set ID (abh_attr_parameter_set_id): Indicates the aps_attr_parameter_set_id value of the active APS.

[0471] Attribute Sequence Parameter Set Attribute Index (abh_attr_sps_attr_idx): Indicates the attribute set of the active SPS. The abh_attr_sps_attr_idx value ranges from 0 to sps_num_attribute_sets in the active SPS.

[0472] Attribute Geometry Slice ID (abh_attr_geom_slice_id): Indicates the gsh_slice_id value of the active geometry slice header.

[0473] Attribute multiple distance lifting weight enable (abh_attr_multiple_distance_lifting_weight_enabled): Specifies whether to apply lifting weight calculation using multiple distance to the slice.

[0474] To reiterate, abh_attr_multiple_distance_lifting_weight_enabled can indicate whether lifting weight calculations are applied using multiple distance calculation methods to calculate the distance between two points in a slice. For example, if abh_attr_multiple_distance_lifting_weight_enabled is 0, lifting weights can be calculated using a single distance calculation method. Also, if abh_attr_multiple_distance_lifting_weight_enabled is 1, lifting weights can be calculated using multiple distance calculation methods.

[0475] Attribute Distance Type (abh_attr_distance_type): Specifies the type of distance used to calculate the lifting weight for a slice. For example, abh_attr_distance_type 0 represents the L2 distance, abh_attr_distance_type 1 represents the squared L1 distance, and abh_attr_distance_type 2 represents the L1 distance.

[0476] Figure 24 shows an encoding method according to embodiments.

[0477] The encoding method according to the embodiments may include a step of encoding geometry data of point cloud data (S2410); and / or a step of encoding attribute data of point cloud data (S2420).

[0478] The step of encoding geometry data (S2410) may include an encoding operation of geometry data described in the transmission device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the encoder of FIGS. 11 to 12, the encoder of FIGS. 15 to 18, the bitstream and syntax generation of FIGS. 19 to 23, and the encoding method of FIG. 24.

[0479] The step of encoding attribute data (S2420) may include encoding operations of attribute data described in the transmission device (10000) of FIG. 1, the point cloud video encoder (10002), the transmitter (10003), the acquisition-encoding-transmission (20000-20001-20002) of FIG. 2, the encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the encoders of FIGS. 11 to 12, the encoders of FIGS. 15 to 18, the bitstream and syntax generation of FIGS. 19 to 23, and the encoding method of FIG. 24.

[0480] Referring further to FIG. 12, the step of encoding attribute data (S2420) may include a step of creating a point group by LoD (level of Detail) based on geometry data, a step of searching for a neighboring point group for a point based on the point group by LoD, and a step of calculating a weight based on the distance between the point and the neighboring point group.

[0481] Referring further to FIGS. 20 to 23, the encoding method can generate a bitstream including multiple distance information indicating whether the distance between a point and a neighboring point group is calculated based on multiple distance calculation methods.

[0482] And the bitstream may further include distance type information used to calculate the weight. At this time, the distance type information may indicate at least one of the L1 distance, the L1 squared distance, and the L2 distance, where the L1 distance may indicate the sum of the absolute values ​​of the distances between two points for each axis, the L1 squared distance may indicate the square of the sum of the absolute values ​​of the distances between two points for each axis, and the L2 distance may indicate the sum of the squares of the distances between two points for each axis.

[0483] And referring to FIGS. 16 and 17, the step of calculating the weight may include a step of calculating the weight using a single distance calculation method based on the plurality of distance information indicating a first value, and a step of calculating the weight based on the plurality of distance calculation methods based on the plurality of distance information indicating a second value.

[0484] In one embodiment, a single distance calculation method may apply an L2 distance calculation method. In addition, multiple distance calculation methods may include calculating a distance by applying an L1, L2, or L1 square distance depending on the type of attribute data, applying a distance calculation method depending on a value indicated by distance type information, or calculating a distance depending on whether an LOD value is greater than a set LOD value. In other words, a single distance calculation method may apply an L2 distance, and multiple distance calculation methods may include applying different distance calculation methods depending on set conditions. In this case, the set conditions may include a type of attribute data, distance type information, an LOD value, etc.

[0485] Also, according to one embodiment, referring to FIG. 15, the step of calculating the weight may include the step of checking the type of attribute data, the step of calculating the weight using the L2 distance based on the type of the attribute data being the first attribute, and the step of calculating the weight using the L1 square distance based on the type of the attribute data being the second attribute.

[0486] Also, according to one embodiment, referring to FIG. 18, the step of calculating the weight may include the step of determining whether the LOD associated with the point is greater than the set LOD, the step of calculating the weight using the L2 distance based on the LOD associated with the point being greater than the set LOD, and the step of calculating the weight using the L1 square distance based on the LOD associated with the point being equal to or less than the set LOD.

[0487] Additionally, the weights can be used in the lifting inverse transform or the predictive inverse transform to restore the attribute information of the points.

[0488] Meanwhile, according to one embodiment, the step of encoding attribute data (S2420) may include a step of generating a LoD (Level of Detail) for point cloud data, and a step of predicting the attribute data based on the LoD.

[0489] And the weights used for prediction of attribute data can be calculated based on the location of the point cloud data, the distance related to the attribute data, and the weights related to the attribute data.

[0490] Additionally, the distance related to the attribute data may be calculated based on the difference between the attribute data for the first point and the attribute data for the predicted point for the first point, and the weight may be calculated based on the square of the distance related to the attribute data.

[0491] And the weights used for prediction of attribute data can be calculated by adding the square of the geometric distance to the first point and the square of the distance related to the attribute data with the weight applied to the attribute data.

[0492] Additionally, the bitstream may include at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, or an attribute data unit for point cloud data, and at least one of the sequence parameter set, the attribute parameter set, the tile parameter set, or the attribute data unit may include information indicating whether cross-lifting weights are used to predict the attribute data.

[0493] The encoding method of FIG. 24 may be performed by an encoding device (encoder). The encoding device includes a memory; and at least one processor connected to the memory; and the at least one processor may be configured to: encode geometry data of point cloud data; and encode attribute data of point cloud data.

[0494] The embodiments further include a computer-readable storage medium storing a bitstream generated by the method according to FIG. 24.

[0495] Embodiments further include a method comprising: obtaining a bitstream for point cloud data, the bitstream being generated based on a step of encoding geometry data of the point cloud data; and a step of encoding attribute data of the point cloud data; and transmitting data including the bitstream.

[0496] Figure 25 shows a decryption method according to embodiments.

[0497] The decoding method according to the embodiments may include a step of decoding geometry data of point cloud data in a bitstream (S2510); and / or a step of decoding attribute data of point cloud data (S2520).

[0498] The step of decoding geometry data (S2510) may include a decoding operation of geometry data described in the 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. 10, FIG. 11, receiving device of FIG. 13, device of FIG. 14, decoder of FIG. 13 to FIG. 14, decoder of FIG. 15 to FIG. 18, bitstream and syntax parsing of FIG. 19 to FIG. 23, decoding method of FIG. 25, etc.

[0499] The step of decoding attribute data (S2520) may include the decoding operation of attribute data described in the 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. 10, FIG. 11, receiving device of FIG. 13, device of FIG. 14, decoder of FIG. 13 to FIG. 14, decoder of FIG. 15 to FIG. 18, bitstream and syntax parsing of FIG. 19 to FIG. 23, decoding method of FIG. 25, etc.

[0500] Referring to FIG. 14, the step of decoding attribute data (S2520) may include a step of creating a point group by LoD (level of Detail) based on geometry data, a step of searching for a neighboring point group for a point based on the point group by LoD, and a step of calculating a weight based on the distance between the point and the neighboring point group.

[0501] Referring to FIGS. 20 to 23, the bitstream may include multiple distance information indicating whether the distance between a point and a neighboring point group is calculated based on multiple distance calculation methods.

[0502] And the bitstream may further include distance type information used to calculate the weight. At this time, the distance type information may indicate at least one of the L1 distance, the L1 squared distance, and the L2 distance, where the L1 distance may indicate the sum of the absolute values ​​of the distances between two points for each axis, the L1 squared distance may indicate the square of the sum of the absolute values ​​of the distances between two points for each axis, and the L2 distance may indicate the sum of the squares of the distances between two points for each axis.

[0503] And referring to FIGS. 16 and 17, the step of calculating the weight may include a step of calculating the weight using a single distance calculation method based on the plurality of distance information indicating a first value, and a step of calculating the weight based on the plurality of distance calculation methods based on the plurality of distance information indicating a second value.

[0504] In one embodiment, a single distance calculation method may apply an L2 distance calculation method. In addition, multiple distance calculation methods may include calculating a distance by applying an L1, L2, or L1 square distance depending on the type of attribute data, applying a distance calculation method depending on a value indicated by distance type information, or calculating a distance depending on whether an LOD value is greater than a set LOD value. In other words, a single distance calculation method may apply an L2 distance, and multiple distance calculation methods may include applying different distance calculation methods depending on set conditions. In this case, the set conditions may include a type of attribute data, distance type information, an LOD value, etc.

[0505] Also, according to one embodiment, referring to FIG. 15, the step of calculating the weight may include the step of checking the type of attribute data, the step of calculating the weight using the L2 distance based on the type of the attribute data being the first attribute, and the step of calculating the weight using the L1 square distance based on the type of the attribute data being the second attribute.

[0506] Also, according to one embodiment, referring to FIG. 18, the step of calculating the weight may include the step of determining whether the LOD associated with the point is greater than the set LOD, the step of calculating the weight using the L2 distance based on the LOD associated with the point being greater than the set LOD, and the step of calculating the weight using the L1 square distance based on the LOD associated with the point being equal to or less than the set LOD.

[0507] Additionally, the weights can be used in the lifting inverse transform or the predictive inverse transform to restore the attribute information of the points.

[0508] Meanwhile, according to one embodiment, the step of decoding attribute data (S2520) may include a step of generating a LoD (Level of Detail) for point cloud data, and a step of predicting the attribute data based on the LoD.

[0509] And the weights used for prediction of attribute data can be calculated based on the location of the point cloud data, the distance related to the attribute data, and the weights related to the attribute data.

[0510] Additionally, the distance related to the attribute data may be calculated based on the difference between the attribute data for the first point and the attribute data for the predicted point for the first point, and the weight may be calculated based on the square of the distance related to the attribute data.

[0511] And the weights used for prediction of attribute data can be calculated by adding the square of the geometric distance to the first point and the square of the distance related to the attribute data with the weight applied to the attribute data.

[0512] Additionally, the bitstream may include at least one of a sequence parameter set, an attribute parameter set, a tile parameter set, or an attribute data unit for point cloud data, and at least one of the sequence parameter set, the attribute parameter set, the tile parameter set, or the attribute data unit may include information indicating whether cross-lifting weights are used to predict the attribute data.

[0513] The decoding method of FIG. 25 may be performed by a decoding device (decoder). The decoding device may include 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 in a bitstream; and decode attribute data of the point cloud data.

[0514] The method and device according to the embodiments provide the following technical effects.

[0515] When encoding / decoding LIDAR values, the similarity between points located in the same direction or plane can be utilized to perform a lifting transformation, thereby improving prediction. High prediction performance can have the effect of reducing the size of the residual value, which ultimately reduces the size of the bitstream. Furthermore, high prediction performance in the lifting transformation exhibits a higher energy compression phenomenon, which also has the effect of reducing quantization errors, thereby improving the objective image quality of the restored attribute values.

[0516] Accordingly, the transmission method / device according to the embodiments can efficiently compress point cloud data to transmit the data, and by transmitting signaling information for this, the reception method / device according to the embodiments can also efficiently decode / restore point cloud data.

[0517] The operation of the transmitting and receiving device according to the above-described embodiments can be explained in combination with the point cloud compression processing process below.

[0518] The embodiments have been described in terms of methods and / or devices, and the descriptions of methods and devices may be applied complementarily.

[0519] For the convenience of explanation, each drawing has been described separately, but it is also possible to design a new embodiment by combining the embodiments described in each drawing. In addition, designing a computer-readable recording medium having a program recorded thereon for executing the previously described embodiments, as needed by a person skilled in the art, also falls within the scope of the embodiments. The devices and methods according to the embodiments are not limited to the configurations and methods of the embodiments described above, but the embodiments may be configured by selectively combining all or part of the embodiments so that various modifications can be made. Although preferred embodiments of the embodiments have been illustrated and described, the embodiments are not limited to the specific embodiments described above, and various modifications can be made by a person skilled in the art to which the present invention pertains without departing from the gist of the embodiments claimed in the claims, and such modifications should not be understood individually from the technical idea or prospect of the embodiments.

[0520] The various components of the devices of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. The various components of the embodiments may be implemented by a single chip, for example, a single hardware circuit. According to embodiments, the components according to the embodiments may be implemented by separate chips. According to embodiments, at least one of the components of the devices of the embodiments may be configured with one or more processors capable of executing one or more programs, and the one or more programs may perform, or include instructions for performing, one or more of the operations / methods according to the embodiments. The executable instructions for performing the methods / operations of the devices of the embodiments may be stored in non-transitory CRMs or other computer program products configured to be executed by one or more processors, or may be stored in temporary CRMs or other computer program products configured to be executed by one or more processors. In addition, the memory according to the embodiments may be used as a concept including not only volatile memory (e.g., RAM, etc.), but also non-volatile memory, flash memory, PROM, etc. Additionally, it may be implemented in the form of a carrier wave, such as transmission via the Internet. Furthermore, the processor-readable recording medium may be distributed across network-connected computer systems, allowing the processor-readable code to be stored and executed in a distributed manner.

[0521] 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, “or” in this document is interpreted as “and / or”. For example, “A or B” can mean 1) “A” only, 2) “B” only, or 3) “A and B”. In other words, “or” in this document can mean “additionally or alternatively”.

[0522] Terms such as first, second, etc. may be used to describe various components of the embodiments. However, the various components according to the embodiments should not be interpreted as limited by the above terms. These terms are merely used to distinguish one component from another. For example, a first user input signal may be referred to as a second user input signal. Similarly, a second user input signal may be referred to as a first user input signal. The use of these terms should be interpreted as not departing from the scope of the various embodiments. Although a first user input signal and a second user input signal are both user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.

[0523] The terminology used to describe the embodiments is for the purpose of describing particular embodiments and is not intended to be limiting of the embodiments. As used in the description of the embodiments and in the claims, the singular is intended to include the plural unless the context clearly dictates otherwise. The expressions “and / or” are used to mean all possible combinations of terms. The expression “includes” describes the presence of features, numbers, steps, elements, and / or components, but does not mean that additional features, numbers, steps, elements, and / or components are not included. Conditional expressions such as “if” or “when” used to describe the embodiments are not intended to be limited to only optional cases. When a specific condition is satisfied, a related action is performed in response to a specific condition, or a related definition is intended to be interpreted.

[0524] Additionally, the operations according to the embodiments described in this document may be performed by a transceiver device including a memory and / or a processor according to the embodiments. The memory may store programs for processing / controlling the 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. The operations according to 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 the memory.

[0525] Meanwhile, the operations according to the embodiments described above may be performed by a transmitting device and / or a receiving device according to the embodiments. The transmitting / receiving device may include a transmitting / 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 / receiving device.

[0526] The processor may be referred to as a controller or the like, and may correspond to, for example, hardware, software, and / or a combination thereof. The operations according to the above-described embodiments may be performed by the processor. Furthermore, the processor may be implemented as an encoder / decoder or the like for the operations of the above-described embodiments.

[0527]

[0528] As described above, the relevant contents have been described in the best form for carrying out the embodiments.

[0529]

[0530] As described above, the embodiments may be applied in whole or in part to a point cloud data transmission and reception device and system.

[0531] Those skilled in the art may make various changes or modifications to the embodiments within the scope of the embodiments.

[0532] Embodiments may include modifications / changes, which do not depart from the scope of the claims and their equivalents.

Claims

1. A step of decoding geometry data of point cloud data in a bitstream; and A step of decoding attribute data of the above point cloud data; comprising; How to decrypt.

2. In paragraph 1, The steps to decode the above attribute data are: A step of generating a point group by LoD (level of Detail) based on the above geometry data; A step of searching for a neighboring point group for a point based on the above LoD-specific point group; and Comprising a step of calculating a weight based on the distance between the point and the neighboring point group, How to decrypt.

3. In paragraph 2, The bitstream includes multiple distance information indicating whether the distance between the point and the neighboring point group is calculated based on multiple distance calculation methods. How to decrypt.

4. In paragraph 3, The steps to calculate the above weights are: A step of calculating the weight using a single distance calculation method based on the multiple distance information indicating the first value; and A step of calculating the weight based on the plurality of distance calculation methods, based on the plurality of distance information indicating the second value, How to decrypt.

5. In paragraph 4, The above bitstream further includes distance type information used to calculate the weight, The above distance type information represents at least one of L1 distance, L1 square distance, and L2 distance, The above L1 distance represents the sum of the absolute values ​​of the distances between two points for each axis, The above L1 square distance represents the square of the sum of the absolute values ​​of the distances between two points for each axis, The above L2 distance represents the sum of the squares of the distances between two points for each axis. How to decrypt.

6. In paragraph 2, The steps to calculate the above weights are: A step of checking the type of the above attribute data; A step of calculating the weight using the L2 distance based on the type of the attribute data being the first attribute; and A step of calculating the weight using the L1 square distance based on the type of the attribute data being the second attribute, How to decrypt.

7. In paragraph 2, The steps to calculate the above weights are: A step of checking whether the LOD associated with the above point is greater than the set LOD; A step of calculating the weight using the L2 distance based on the LOD associated with the above point being greater than the set LOD; and Comprising a step of calculating the weight using the L1 square distance based on whether the LOD associated with the above point is equal to or less than the set LOD. How to decrypt.

8. In paragraph 2, The above weights are used in lifting inverse transformation or prediction inverse transformation to restore attribute information of the above point. How to decrypt.

9. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Decoding geometry data of point cloud data in the bitstream; and Decoding attribute data of the above point cloud data; configured to do so, Decryption device.

10. 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.

11. In paragraph 10, The steps to encode attribute data are: A step of generating a point group by LoD (level of Detail) based on the above geometry data; A step of searching for a neighboring point group for a point based on the above LoD-specific point group; and Comprising a step of calculating a weight based on the distance between the point and the neighboring point group, Encoding method.

12. In paragraph 11, The distance between the above point and the neighboring point group is calculated based on multiple distance calculation methods. Encoding method.

13. Memory; and At least one processor connected to the memory; wherein the at least one processor comprises: Encoding the geometry data of point cloud data; and Encoding attribute data of the above point cloud data; configured to do so; Decryption device.

14. A computer-readable storage medium storing a bitstream generated by the method according to Article 10.

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

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