WO2021194065A1 - Appareil et procédé de traitement de données de nuage de points - Google Patents

Appareil et procédé de traitement de données de nuage de points Download PDF

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Publication number
WO2021194065A1
WO2021194065A1 PCT/KR2021/000368 KR2021000368W WO2021194065A1 WO 2021194065 A1 WO2021194065 A1 WO 2021194065A1 KR 2021000368 W KR2021000368 W KR 2021000368W WO 2021194065 A1 WO2021194065 A1 WO 2021194065A1
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Prior art keywords
point cloud
attribute
value
cloud data
point
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PCT/KR2021/000368
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English (en)
Korean (ko)
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한재신
허혜정
오세진
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엘지전자 주식회사
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/597—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding specially adapted for multi-view video sequence encoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/70—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by syntax aspects related to video coding, e.g. related to compression standards
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/80—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation
    • H04N19/82—Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation involving filtering within a prediction loop
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/85—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using pre-processing or post-processing specially adapted for video compression

Definitions

  • Embodiments provide point cloud content to provide users with various services such as VR (Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality), and autonomous driving service. provide a way
  • the point cloud content is content expressed as a point cloud, which is a set of points (points) belonging to a coordinate system representing a three-dimensional space.
  • Point cloud content can express three-dimensional media, and provides various services such as VR (Virtual Reality), AR (Augmented Reality), MR (Mixed Reality), and autonomous driving service. used to provide However, tens of thousands to hundreds of thousands of point data are needed to express point cloud content. Therefore, a method for efficiently processing a large amount of point data is required.
  • Embodiments provide an apparatus and method for efficiently processing point cloud data.
  • Embodiments provide a point cloud data processing method and apparatus for solving latency and encoding/decoding complexity.
  • a method for transmitting point cloud data includes encoding the point cloud data and transmitting a bitstream including the encoded point cloud data.
  • a method for receiving point cloud data includes receiving a bitstream including point cloud data and decoding the point cloud data.
  • the apparatus and method according to the embodiments may process point cloud data with high efficiency.
  • the apparatus and method according to the embodiments may provide a high quality point cloud service.
  • the apparatus and method according to the embodiments may provide point cloud content for providing universal services such as a VR service and an autonomous driving service.
  • FIG. 1 shows an example of a point cloud content providing system according to embodiments.
  • FIG. 2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIG. 4 shows an example of a point cloud encoder according to embodiments.
  • FIG. 5 illustrates an example of a voxel according to embodiments.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • FIG. 7 shows an example of a neighbor node pattern according to embodiments.
  • FIG. 10 shows an example of a point cloud decoder according to embodiments.
  • FIG. 11 shows an example of a point cloud decoder according to embodiments.
  • FIG. 13 is an example of a receiving apparatus according to embodiments.
  • FIG. 14 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • 15 is a block diagram illustrating an apparatus for transmitting point cloud data according to embodiments.
  • 16 is a block diagram illustrating an example of a lifting transformation process according to embodiments.
  • 17 is a flowchart of a method for transmitting point cloud data according to embodiments.
  • FIG. 18 shows a structure of a bitstream according to embodiments.
  • FIG. 19 illustrates a structure of an Attribute Parameter Set (APS) of point cloud data according to embodiments.
  • APS Attribute Parameter Set
  • 20 is a block diagram illustrating an apparatus for receiving point cloud data according to embodiments.
  • 21 is a block diagram illustrating an example of an inverse lifting process according to embodiments.
  • 22 is a flowchart of a method for receiving point cloud data according to embodiments.
  • FIG. 23 is an example of a flowchart illustrating a method of transmitting point cloud data according to embodiments.
  • FIG. 24 is an example of a flowchart illustrating a method of receiving point cloud data according to embodiments.
  • FIG. 1 shows an example of a point cloud content providing system according to embodiments.
  • the point cloud content providing system shown in FIG. 1 may include a transmission device 10000 and a reception device 10004 .
  • the transmitting device 10000 and the receiving device 10004 are capable of wired/wireless communication in order to transmit/receive point cloud data.
  • the transmission device 10000 may secure, process, and transmit a point cloud video (or point cloud content).
  • the transmission device 10000 may be a fixed station, a base transceiver system (BTS), a network, an Ariticial Intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or a server and the like.
  • BTS base transceiver system
  • AI Ariticial Intelligence
  • robot an AR/VR/XR device and/or a server and the like.
  • the transmission device 10000 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to perform communication with a base station and/or other wireless devices; It may include robots, vehicles, AR/VR/XR devices, mobile devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.
  • a radio access technology eg, 5G NR (New RAT), LTE (Long Term Evolution)
  • 5G NR New RAT
  • LTE Long Term Evolution
  • IoT Internet of Things
  • Transmission device 10000 is 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 (Transmitter (or Communication module), 10003) ) contains
  • the point cloud video acquisition unit 10001 acquires the point cloud video through processing such as capturing, synthesizing, or generating.
  • the point cloud video is point cloud content expressed as a point cloud that is a set of points located in a three-dimensional space, and may be referred to as point cloud video data or the like.
  • a point cloud video according to embodiments may include one or more frames. One frame represents a still image/picture. Accordingly, the point cloud video may include a point cloud image/frame/picture, and may be referred to as any one of a point cloud image, a frame, and a picture.
  • the point cloud video encoder 10002 encodes the obtained point cloud video data.
  • the point cloud video encoder 10002 may encode point cloud video data based on point cloud compression coding.
  • Point cloud compression coding may include Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding or next-generation coding.
  • G-PCC Geometry-based Point Cloud Compression
  • V-PCC Video based Point Cloud Compression
  • the point cloud video encoder 10002 may output a bitstream including encoded point cloud video data.
  • the bitstream may include not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data.
  • the transmitter 10003 transmits a bitstream including encoded point cloud video data.
  • the bitstream according to the embodiments is encapsulated into a file or segment (eg, a streaming segment) and transmitted through various networks such as a broadcasting network and/or a broadband network.
  • the transmission device 10000 may include an encapsulation unit (or encapsulation module) that performs an encapsulation operation.
  • the encapsulation unit may be included in the transmitter 10003 .
  • the file or segment may be transmitted to the receiving device 10004 through a network or stored in a digital storage medium (eg, USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.).
  • the transmitter 10003 may communicate with the receiving device 10004 (or a receiver 10005) through wired/wireless communication through networks such as 4G, 5G, and 6G. Also, the transmitter 10003 may perform a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, or 6G). Also, the transmission device 10000 may transmit encapsulated data according to an on demand method.
  • a network system eg, a communication network system such as 4G, 5G, or 6G.
  • the transmission device 10000 may transmit encapsulated data according to an on demand method.
  • the receiving apparatus 10004 includes a receiver (Receiver, 10005), a point cloud video decoder (Point Cloud Decoder, 10006), and/or a renderer (Renderer, 10007).
  • the receiving device 10004 uses a radio access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a device, a robot , vehicles, AR/VR/XR devices, mobile devices, home appliances, Internet of Things (IoT) devices, AI devices/servers, and the like.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • the receiver 10005 receives a bitstream including 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 a necessary data processing operation according to a network system (eg, a communication network system such as 4G, 5G, or 6G).
  • the receiver 10005 may output a bitstream by decapsulating the received file/segment.
  • the receiver 10005 may include a decapsulation unit (or a decapsulation module) for performing a decapsulation operation.
  • the decapsulation unit may be implemented as an element (or component) separate from the receiver 10005 .
  • the point cloud video decoder 10006 decodes a bitstream including point cloud video data.
  • the point cloud video decoder 10006 may decode the point cloud video data according to an encoded manner (eg, a reverse process of the operation of the point cloud video encoder 10002 ). Accordingly, the point cloud video decoder 10006 may decode the point cloud video data by performing point cloud decompression coding, which is a reverse process of the point cloud compression.
  • Point cloud decompression coding includes G-PCC coding.
  • the renderer 10007 renders the decoded point cloud video data.
  • the renderer 10007 may output point cloud content by rendering audio data as well as point cloud video data.
  • the renderer 10007 may include a display for displaying the point cloud content.
  • the display may not be included in the renderer 10007 and may be implemented as a separate device or component.
  • the feedback information is information for reflecting the interactivity with the user who consumes the point cloud content, and includes user information (eg, head orientation information, viewport information, etc.).
  • user information eg, head orientation information, viewport information, etc.
  • the feedback information is provided by the content transmitting side (eg, the transmission device 10000) and/or the service provider can be passed on to According to embodiments, the feedback information may be used by the receiving device 10004 as well as the transmitting device 10000 or may not be provided.
  • the head orientation information is information about the user's head position, direction, angle, movement, and the like.
  • the receiving apparatus 10004 may calculate viewport information based on head orientation information.
  • the viewport information is information about the area of the point cloud video that the user is looking at.
  • a viewpoint is a point at which a user is watching a point cloud video, and may mean a central point of the viewport area. That is, the viewport is an area centered on a viewpoint, and the size and shape of the area may be determined by a Field Of View (FOV).
  • FOV Field Of View
  • the reception device 10004 may extract viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information.
  • the receiving device 10004 checks the user's point cloud consumption method, the point cloud video area the user gazes at, the gaze time, and the like by performing a gaze analysis or the like.
  • the receiving device 10004 may transmit feedback information including the result of the gaze analysis to the transmitting device 10000 .
  • Feedback information may be obtained during rendering and/or display.
  • Feedback information may be secured by one or more sensors included in the receiving device 10004 .
  • the feedback information may be secured by the renderer 10007 or a separate external element (or device, component, etc.).
  • a dotted line in FIG. 1 shows a process of transferring feedback information secured by the renderer 10007 .
  • the point cloud content providing system may process (encode/decode) the point cloud data based on the feedback information. Accordingly, the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information. Also, the receiving device 10004 may transmit feedback information to the transmitting device 10000 . The transmitting device 10000 (or the point cloud video data encoder 10002 ) may perform an encoding operation based on the feedback information. Therefore, the point cloud content providing system does not process (encode / decode) all point cloud data, but efficiently processes necessary data (for example, point cloud data corresponding to the user's head position) based on the feedback information, and the user can provide point cloud content to
  • the transmitting apparatus 10000 may be referred to as an encoder, a transmitting device, a transmitter, or the like, and the receiving apparatus 10004 may be referred to as a decoder, a receiving device, a receiver, or the like.
  • Point cloud data (processed in a series of acquisition/encoding/transmission/decoding/rendering) processed 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.
  • the point cloud content data may be used as a concept including metadata or signaling information related to the point cloud data.
  • the elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, a processor and/or a combination thereof.
  • FIG. 2 is a block diagram illustrating an operation of providing point cloud content according to embodiments.
  • the block diagram of FIG. 2 shows the operation of the point cloud content providing system described in FIG. 1 .
  • the point cloud content providing system may process point cloud data based on point cloud compression coding (eg, G-PCC).
  • point cloud compression coding eg, G-PCC
  • the point cloud content providing system may acquire a point cloud video (20000).
  • a point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space.
  • a point cloud video according to embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file.
  • the acquired point cloud video may include one or more Ply files.
  • the Ply file contains point cloud data such as the point's geometry and/or attributes. Geometry includes positions of points.
  • the position of each point may be expressed by parameters (eg, values of each of the X-axis, Y-axis, and Z-axis) representing a three-dimensional coordinate system (eg, a coordinate system including XYZ axes).
  • the attribute includes attributes of points (eg, texture information of each point, color (YCbCr or RGB), reflectance (r), transparency, etc.).
  • a point has one or more attributes (or properties).
  • one point may have one attribute of color, or two attributes of color and reflectance.
  • the geometry may be referred to as positions, geometry information, geometry data, and the like, and the attribute may be referred to as attributes, attribute information, attribute data, and the like.
  • the point cloud content providing system receives points from information (eg, depth information, color information, etc.) related to the point cloud video acquisition process. Cloud data can be obtained.
  • the point cloud content providing system may encode the point cloud data (20001).
  • the point cloud content providing system may encode point cloud data based on point cloud compression coding.
  • the point cloud data may include the geometry and attributes of the point.
  • the point cloud content providing system may output a geometry bitstream by performing geometry encoding for encoding the geometry.
  • the point cloud content providing system may output an attribute bitstream by performing attribute encoding for encoding the attribute.
  • the point cloud content providing system may perform attribute encoding based on geometry encoding.
  • the geometry bitstream and the attribute bitstream according to the embodiments may be multiplexed and output as one bitstream.
  • the bitstream according to embodiments may further include signaling information related to geometry encoding and attribute encoding.
  • the point cloud content providing system may transmit the encoded point cloud data (20002).
  • the encoded point cloud data may be expressed as a geometry bitstream and an attribute bitstream.
  • the encoded point cloud data may be transmitted in the form of a bitstream together with signaling information related to encoding of the point cloud data (eg, signaling information related to geometry encoding and attribute encoding).
  • the point cloud content providing system may encapsulate the bitstream for transmitting the encoded point cloud data and transmit it in the form of a file or segment.
  • the point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) according to the embodiments may receive a bitstream including the encoded point cloud data. Also, the point cloud content providing system (eg, the receiving device 10004 or the receiver 10005) may demultiplex the bitstream.
  • the point cloud content providing system may decode the encoded point cloud data (for example, a geometry bitstream, an attribute bitstream) transmitted as a bitstream. have.
  • the point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may decode the point cloud video data based on signaling information related to encoding of the point cloud video data included in the bitstream. have.
  • the point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may decode the geometry bitstream to restore positions (geometry) of the points.
  • the point cloud content providing system may restore attributes of points by decoding an attribute bitstream based on the restored geometry.
  • the point cloud content providing system (eg, the receiving device 10004 or the point cloud video decoder 10005) may reconstruct the point cloud video based on positions and decoded attributes according to the reconstructed geometry.
  • the point cloud content providing system may render the decoded point cloud data (20004).
  • the point cloud content providing system may render the geometry and attributes decoded through the decoding process according to various rendering methods according to the rendering method. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered at the vertex position, or a circle centered at the vertex position. All or part of the rendered point cloud content is provided to the user through a display (eg, VR/AR display, general display, etc.).
  • a display eg, VR/AR display, general display, etc.
  • the point cloud content providing system (eg, the receiving device 10004) according to embodiments may secure feedback information (20005).
  • the point cloud content providing system may encode and/or decode the point cloud data based on the feedback information. Since the operation of the feedback information and point cloud content providing system according to the embodiments is the same as the feedback information and operation described with reference to FIG. 1 , a detailed description thereof will be omitted.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIG. 3 shows an example of a point cloud video capture process of the point cloud content providing system described with reference to FIGS. 1 to 2 .
  • the point cloud content is an object located in various three-dimensional spaces (eg, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video representing the environment (images and/or videos) are included.
  • one or more cameras eg, an infrared camera capable of securing depth information, color information corresponding to depth information
  • Point cloud video can be captured using an RGB camera that can extract
  • the point cloud content providing system according to the embodiments may extract a shape of a geometry composed of points in a three-dimensional space from depth information, and extract an attribute of each point from color information to secure point cloud data.
  • An image and/or an image according to embodiments may be captured based on at least one of an inward-facing method and an outward-facing method.
  • the left side of FIG. 3 shows an inward-pacing scheme.
  • the inward-pacing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the central object.
  • the inward-facing method provides a 360-degree image of a point cloud content that provides a 360-degree image of a core object to the user (for example, a 360-degree image of an object (e.g., a core object such as a character, player, object, actor, etc.) to the user.
  • VR/AR content for example, a 360-degree image of an object (e.g., a core object such as a character, player, object, actor, etc.)
  • the right side of FIG. 3 shows an outward-pacing scheme.
  • the outward-pacing method refers to a method in which one or more cameras (or camera sensors) positioned surrounding the central object capture the environment of the central object rather than the central object.
  • the outward-pacing method may be used to generate point cloud content (eg, content representing an external environment that may be provided to a user of an autonomous vehicle) for providing a surrounding environment that appears from the user's point of view.
  • point cloud content eg, content representing an external environment that may be provided to a user of an autonomous vehicle
  • the point cloud content may be generated based on a capture operation of one or more cameras.
  • the point cloud content providing system may perform calibration of one or more cameras in order to set a global coordinate system before a capture operation.
  • the point cloud content providing system may generate the point cloud content by synthesizing the image and/or image captured by the above-described capture method and an arbitrary image and/or image.
  • the point cloud content providing system may not perform the capture operation described with reference to FIG. 3 when generating point cloud content representing a virtual space.
  • the point cloud content providing system according to the embodiments may perform post-processing on the captured image and/or the image. That is, the point cloud content providing system removes an unwanted area (for example, a background), recognizes a space where captured images and/or images are connected, and fills in a spatial hole if there is one. can
  • the point cloud content providing system may generate one point cloud content by performing coordinate system transformation on points of the point cloud video obtained from each camera.
  • the point cloud content providing system may perform coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing one wide range and may generate point cloud content having a high density of points.
  • FIG. 4 shows an example of a point cloud encoder according to embodiments.
  • the point cloud encoder controls point cloud data (eg, positions of points and/or attributes) and perform an encoding operation.
  • point cloud data e.g, positions of points and/or attributes
  • the point cloud content providing system may not be able to stream the corresponding content in real time. Accordingly, the point cloud content providing system may reconfigure the point cloud content based on a maximum target bitrate in order to provide it according to a network environment.
  • the point cloud encoder may perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.
  • the point cloud encoder may include a coordinate system transformation unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface appropriation analysis unit ( Analyze Surface Approximation (40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Color Transformer (Transform Colors, 40006), Attribute Transformer (Transfer Attributes, 40007), RAHT Transform a unit 40008, an LOD generator (Generated LOD, 40009), a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011) and/or an arithmetic encoder (Arithmetic Encode, 40012).
  • a coordinate system transformation unit Transformation Coordinates, 40000
  • a quantization unit Quantization and Remove Points (Voxelize)
  • the coordinate system transformation unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface approximation analysis unit 40003, the arithmetic encoder 40004, and the geometry reconstruction unit 40005 perform geometry encoding. can do.
  • Geometry encoding according to embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. Direct coding and trisup geometry encoding are applied selectively or in combination. Also, the geometry encoding is not limited to the above example.
  • the coordinate system conversion unit 40000 receives the positions and converts them into a coordinate system.
  • the positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space expressed in an XYZ coordinate system, etc.).
  • Location information in a 3D space according to embodiments may be referred to as geometry information.
  • the quantizer 40001 quantizes the geometry. For example, the quantizer 40001 may quantize the points based on the minimum position values of all points (eg, the minimum values on each axis with respect to the X-axis, Y-axis, and Z-axis). The quantization unit 40001 multiplies the difference between the minimum position value and the position value of each point by a preset quatization scale value, and then performs a quantization operation to find the nearest integer value by rounding or rounding it down. Accordingly, one or more points may have the same quantized position (or position value). The quantizer 40001 according to embodiments performs voxelization based on quantized positions to reconstruct quantized points.
  • the quantizer 40001 performs voxelization based on quantized positions to reconstruct quantized points.
  • a minimum unit including 2D image/video information is a pixel, and points of point cloud content (or 3D point cloud video) according to embodiments may be included in one or more voxels.
  • the quantizer 40001 may match groups of points in a 3D space to voxels.
  • one voxel may include only one point.
  • one voxel may include one or more points.
  • a position of a ceter of a corresponding voxel may be set based on positions of one or more points included in one voxel.
  • attributes of all positions included in one voxel may be combined and assigned to a corresponding voxel.
  • the octree analyzer 40002 performs octree geometry coding (or octree coding) to represent voxels in an octree structure.
  • the octree structure represents points matched to voxels based on the octal tree structure.
  • the surface approximation analyzer 40003 may analyze and approximate the octree.
  • Octree analysis and approximation is a process of analyzing to voxelize a region including a plurality of points in order to efficiently provide octree and voxelization.
  • the arithmetic encoder 40004 entropy encodes the octree and/or the approximated octree.
  • the encoding method includes an arithmetic encoding method.
  • the encoding results in a geometry bitstream.
  • Color transform unit 40006, attribute transform unit 40007, RAHT transform unit 40008, LOD generating unit 40009, lifting transform unit 40010, coefficient quantization unit 40011 and/or arithmetic encoder 40012 performs attribute encoding.
  • one point may have one or more attributes. Attribute encoding according to embodiments is equally applied to attributes of one point. However, when one attribute (eg, color) includes one or more elements, independent attribute encoding is applied to each element.
  • Attribute encoding may include color transform coding, attribute transform coding, region adaptive hierarchical transform (RAHT) coding, interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, and interpolation-based hierarchical nearest -neighbor prediction with an update/lifting step (Lifting Transform)) may include coding.
  • RAHT region adaptive hierarchical transform
  • coding interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding
  • Lifting Transform interpolation-based hierarchical nearest -neighbor prediction with an update/lifting step
  • attribute encoding is not limited to the above-described example.
  • the color conversion unit 40006 performs color conversion coding for converting color values (or textures) included in attributes.
  • the color conversion unit 40006 may convert the format of color information (eg, convert from RGB to YCbCr).
  • the operation of the color converter 40006 according to embodiments may be optionally applied according to color values included in the attributes.
  • the geometry reconstruction unit 40005 reconstructs (decompresses) an octree and/or an approximated octree.
  • the geometry reconstruction unit 40005 reconstructs an octree/voxel based on a result of analyzing the distribution of points.
  • the reconstructed octree/voxel may be referred to as a reconstructed geometry (or a reconstructed geometry).
  • the attribute transform unit 40007 performs an attribute transform that transforms attributes based on positions where geometry encoding has not been performed and/or a reconstructed geometry. As described above, since the attributes are dependent on the geometry, the attribute conversion unit 40007 may transform the attributes based on the reconstructed geometry information. For example, the attribute conversion unit 40007 may convert an attribute of a point at the 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 corresponding voxel is set based on the positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of the one or more points. When the tri-soup geometry encoding is performed, the attribute conversion unit 40007 may convert the attributes based on the tri-soup geometry encoding.
  • the attribute conversion unit 40007 is an average value of attributes or attribute values (eg, 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. can be calculated to perform attribute transformation.
  • the attribute conversion unit 40007 may apply a weight according to the distance from the center point to each point when calculating the average value.
  • each voxel has a position and a computed attribute (or attribute value).
  • the attribute transform unit 40007 may search for neighboring points existing within a specific position/radius from the position of the center point of each voxel based on the K-D tree or the Morton code.
  • the K-D tree is a binary search tree and supports a data structure that can manage points based on location so that Nearest Neighbor Search-NNS is possible quickly.
  • the Molton code represents a coordinate value (eg (x, y, z)) representing the three-dimensional position of all points as a bit value, and is generated by mixing the bits. For example, if the coordinate value indicating the position of the point is (5, 9, 1), the bit value of the coordinate value is (0101, 1001, 0001).
  • the attribute transform unit 40007 may align the points based on the Molton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transform operation, when the nearest neighbor search (NNS) is required in another transform process for attribute coding, a K-D tree or a Molton code is used.
  • NSS shortest neighbor search
  • the converted attributes are input to the RAHT conversion unit 40008 and/or the LOD generation unit 40009.
  • the RAHT converter 40008 performs RAHT coding for predicting attribute information based on the reconstructed geometry information.
  • the RAHT transform unit 40008 may predict attribute information of a node at an upper level of the octree based on attribute information associated with a node at a lower level of the octree.
  • the LOD generator 40009 generates a level of detail (LOD) to perform predictive transform coding.
  • LOD level of detail
  • the LOD according to the embodiments indicates the detail of the point cloud content, and the smaller the LOD value, the lower the detail of the point cloud content, and the larger the LOD value, the higher the detail of the point cloud content. Points may be classified according to LOD.
  • the lifting transform unit 40010 performs lifting transform coding that transforms the attributes of the point cloud based on weights. As described above, lifting transform coding may be selectively applied.
  • the coefficient quantizer 40011 quantizes the attribute-coded attributes based on the coefficients.
  • the arithmetic encoder 40012 encodes the quantized attributes based on arithmetic coding.
  • the elements of the point cloud encoder of FIG. 4 are hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device. , software, firmware, or a combination thereof.
  • the one or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 4 described above.
  • the one or more processors may also 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. 4 .
  • One or more memories may include high speed random access memory, non-volatile memory (eg, one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state memory). memory devices (such as solid-state memory devices).
  • FIG. 5 shows an example of a voxel according to embodiments.
  • voxel 5 is generated through an octree structure that recursively subdivides a bounding box (cubical axis-aligned bounding box) defined by two poles (0,0,0) and (2d, 2d, 2d)
  • a voxel is shown.
  • One voxel includes at least one or more points.
  • a voxel may estimate spatial coordinates from a positional relationship with a voxel group.
  • voxels have attributes (such as color or reflectance) like pixels of a 2D image/image. A detailed description of the voxel is the same as that described with reference to FIG. 4 and thus will be omitted.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • the point cloud content providing system (point cloud video encoder 10002) or point cloud encoder (eg, octree analysis unit 40002) efficiently manages the area and/or position of voxels.
  • point cloud video encoder 10002 or point cloud encoder eg, octree analysis unit 40002
  • octree geometry coding or octree coding based on octree structure is performed.
  • FIG. 6 shows the octree structure.
  • the three-dimensional space of the point cloud content according to the embodiments is expressed by axes (eg, X-axis, Y-axis, and Z-axis) of the coordinate system.
  • the octree structure is created by recursive subdividing a cubic axis-aligned bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ). . 2d may be set as a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video).
  • d represents the depth of the octree.
  • the value of d is determined according to the following equation. In the following equation (x int n , y int n , z int n ) represents positions (or position values) of quantized points.
  • the entire 3D space may be divided into eight spaces according to the division.
  • Each divided space is represented by a cube with six faces.
  • each of the eight spaces is again divided based on the axes of the coordinate system (eg, X-axis, Y-axis, and Z-axis). Therefore, each space is further divided into 8 small spaces.
  • the divided small space is also expressed as a cube with six faces. This division method is applied until a leaf node of the octree becomes a voxel.
  • the lower part of FIG. 6 shows the occupanci code of the octree.
  • An occupancy code of an octree is generated to indicate whether each of eight divided spaces generated by dividing one space includes at least one point. Accordingly, one occupanci code is expressed by eight child nodes. Each child node represents the occupancies of the divided space, and each child node has a value of 1 bit. Therefore, the occupanci code is expressed as an 8-bit code. That is, if at least one point is included in the space corresponding to the child node, the corresponding 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 occupanci code shown in FIG.
  • a point cloud encoder (eg, arithmetic encoder 40004 ) according to embodiments may entropy encode the occupanci code. In addition, to increase the compression efficiency, the point cloud encoder can intra/inter-code the occupanci code.
  • the receiving apparatus (eg, the receiving apparatus 10004 or the point cloud video decoder 10006) according to embodiments reconstructs an octree based on the occupanci code.
  • the point cloud encoder (eg, the point cloud encoder of FIG. 4 , or the octree analyzer 40002) according to embodiments may perform voxelization and octree coding to store positions of points.
  • the points in the 3D space are not always evenly distributed, there may be a specific area where there are not many points. Therefore, it is inefficient to perform voxelization on the entire 3D space. For example, if there are few points in a specific area, it is not necessary to perform voxelization up to the corresponding area.
  • the point cloud encoder does not perform voxelization on the above-described specific region (or a node other than a leaf node of an octree), but directly codes positions of points included in the specific region. ) can be done. Coordinates of direct coding points according to embodiments are called direct coding mode (DCM).
  • DCM direct coding mode
  • the point cloud encoder according to the embodiments may perform trisoup geometry encoding for reconstructing positions of points in a specific region (or node) based on a voxel based on a surface model.
  • Tri-Soop geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes.
  • the point cloud decoder can generate a point cloud from the mesh surface.
  • Direct coding and trisup geometry encoding according to embodiments may be selectively performed. Also, direct coding and trisup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).
  • the option to use a direct mode for applying direct coding must be activated, and a node to which direct coding is to be applied is not a leaf node, but is below a threshold within a specific node. points must exist. In addition, the number of whole points to be subjected to direct coding should not exceed a preset limit value. If the above condition is satisfied, the point cloud encoder (or the arithmetic encoder 40004 ) according to embodiments may entropy-code positions (or position values) of points.
  • the point cloud encoder (for example, the surface appropriation analyzer 40003) according to the embodiments determines a specific level of the octree (when the level is smaller than the depth d of the octree), and from that level, a node using the surface model It is possible to perform tri-soup geometry encoding that reconstructs the position of a point in a region based on voxels (tri-soup mode).
  • the point cloud encoder according to the embodiments may designate a level to which tri-soup geometry encoding is to be applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder will not operate in trichop mode.
  • the point cloud encoder may operate in the tri-soup mode only when the designated level is smaller than the depth value of the octree.
  • a three-dimensional cube region of nodes of a specified level according to embodiments is called a block.
  • One block may include one or more voxels.
  • a block or voxel may correspond to a brick.
  • the geometry is represented as a surface.
  • a surface according to embodiments may intersect each edge of the block at most once.
  • a vertex existing along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge.
  • An ocupided voxel means a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels of all voxels adjacent to the edge among all blocks sharing the edge.
  • the point cloud encoder When a vertex is detected, the point cloud encoder according to the embodiments entropy-codes the starting point (x, y, z) of the edge, the direction vectors ( ⁇ x, ⁇ y, ⁇ z) of the edge, and the vertex position values (relative position values within the edge).
  • the point cloud encoder eg, the geometry reconstruction unit 40005
  • the point cloud encoder performs triangle reconstruction, up-sampling, and voxelization processes. to create a reconstructed geometry (reconstructed geometry).
  • Vertices located at the edge of a block determine the surface that passes through the block.
  • the surface according to embodiments 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 perform a square on the values obtained by subtracting the center value from each vertex value, and obtain the sum of all the values.
  • the minimum value of the added values is obtained, and the projection process is performed along the axis with the minimum value. For example, if the x element is the minimum, each vertex is projected on the x-axis with respect to the center of the block and projected on the (y, z) plane. If the projected value on the (y, z) plane is (ai, bi), the ⁇ value is obtained through atan2(bi, ai), and the vertices are aligned based on the ⁇ value.
  • the table below shows combinations of vertices for generating a triangle according to the number of vertices. Vertices are sorted in order from 1 to n.
  • the table below shows that for four vertices, two triangles can be formed according to a combination of vertices.
  • the first triangle may be composed of 1st, 2nd, and 3rd vertices among aligned vertices
  • the second triangle may be composed of 3rd, 4th, and 1st vertices among aligned vertices.
  • the upsampling process is performed to voxelize the triangle by adding points along the edge of the triangle. Create additional points based on the upsampling factor and the width of the block. The additional points are called refined vertices.
  • the point cloud encoder may voxel the refined vertices. Also, the point cloud encoder may perform attribute encoding based on the voxelized position (or position value).
  • FIG. 7 shows an example of a neighbor node pattern according to embodiments.
  • the point cloud encoder may perform entropy coding based on context adaptive arithmetic coding.
  • the point cloud content providing system or the point cloud encoder directly transmits the occupanci code.
  • Entropy coding is possible.
  • the point cloud content providing system or point cloud encoder performs entropy encoding (intra-encoding) based on the occupancies of the current node and the occupancies of neighboring nodes, or entropy encoding (inter-encoding) based on the occupancies of the previous frame. ) can be done.
  • a frame according to embodiments means a set of point cloud videos generated at the same time.
  • a point cloud encoder determines occupancy of neighboring nodes of each node of an octree and obtains a value of a neighbor pattern.
  • the neighbor node pattern is used to infer the occupancies pattern of the corresponding node.
  • the left side of FIG. 7 shows a cube corresponding to a node (a cube located in the center) and six cubes (neighbor nodes) sharing at least one face with the cube.
  • the nodes shown in the figure are nodes of the same depth (depth).
  • the numbers shown in the figure represent the weights (1, 2, 4, 8, 16, 32, etc.) associated with each of the six nodes. Each weight is sequentially assigned according to the positions of neighboring nodes.
  • the right side of FIG. 7 shows the neighboring node pattern values.
  • the neighbor node pattern value is the sum of values multiplied by the weights of the ocupided neighbor nodes (neighbor nodes with points). Therefore, the neighbor node pattern values range from 0 to 63. When the value of the neighbor node pattern is 0, it indicates that there is no node (ocupid node) having a point among the neighboring nodes of the corresponding node. When the neighbor node pattern value is 63, it indicates that all of the neighbor nodes are ocupid nodes. As shown in the figure, since neighboring nodes to which weights 1, 2, 4, and 8 are assigned are ocupided nodes, the neighboring node pattern value is 15, which is the sum of 1, 2, 4, and 8.
  • the point cloud encoder may perform coding according to the value of the neighboring node pattern (for example, if the value of the neighboring node pattern is 63, 64 types of coding are performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing the neighbor node pattern value (eg, based on a table that changes 64 to 10 or 6).
  • the encoded geometry is reconstructed (decompressed).
  • the geometry reconstruction operation may include changing the arrangement of the direct coded points (eg, placing the direct coded points in front of the point cloud data).
  • the geometry reconstruction process is triangular reconstruction, upsampling, and voxelization. Since the attribute is dependent on the geometry, the attribute encoding is performed based on the reconstructed geometry.
  • the point cloud encoder may reorganize the points by LOD.
  • the figure shows the point cloud content corresponding to the LOD.
  • the left side of the drawing shows the original point cloud content.
  • the second figure from the left of the figure shows the distribution of the points of the lowest LOD, and the rightmost figure of the figure shows the distribution of the points of the highest LOD. That is, the points of the lowest LOD are sparsely distributed, and the points of the highest LOD are densely distributed. That is, as the LOD increases according to the direction of the arrow indicated at the bottom of the drawing, the interval (or distance) between the points becomes shorter.
  • the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder 10002, the point cloud encoder of FIG. 4, or the LOD generator 40009) generates an LOD. can do.
  • the LOD is created by reorganizing the points into a set of refinement levels according to a set LOD distance value (or set of Euclidean Distance).
  • the LOD generation process is performed not only in the point cloud encoder but also in the point cloud decoder.
  • FIG. 9 shows examples (P0 to P9) of points of point cloud content distributed in a three-dimensional space.
  • the original order of FIG. 9 indicates the order of points P0 to P9 before LOD generation.
  • the LOD based order of FIG. 9 indicates the order of points according to the LOD generation. Points are rearranged by LOD. Also, the high LOD includes points belonging to the low LOD.
  • LOD0 includes P0, P5, P4 and P2.
  • LOD1 includes the points of LOD0 and P1, P6 and P3.
  • LOD2 includes points of LOD0, points of LOD1, and P9, P8 and P7.
  • the point cloud encoder may perform predictive transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.
  • a point cloud encoder may generate predictors for points and perform predictive transform coding to set a predictive attribute (or predictive attribute value) of each point. That is, N predictors may be generated for N points.
  • the prediction attribute (or attribute value) is a weight calculated based on the distance to each neighboring point in the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point (or a weight value) is set as the average value of the multiplied value.
  • the point cloud encoder for example, the coefficient quantization unit 40011 according to the embodiments subtracts the predicted attribute (attribute value) from the attribute (attribute value) of each point (residuals, residual attribute, residual attribute value, attribute quantization and inverse quantization can be performed on the prediction residual value, etc.
  • the quantization process is shown in the following table.
  • the point cloud encoder (eg, the arithmetic encoder 40012 ) according to embodiments may entropy-code the quantized and dequantized residual values as described above when there are neighboring points to the predictor of each point.
  • the point cloud encoder (eg, the arithmetic encoder 40012 ) according to embodiments may entropy-code attributes of a corresponding point without performing the above-described process if there are no neighboring points in the predictor of each point.
  • the point cloud encoder (for example, the lifting transform unit 40010) according to the embodiments generates a predictor of each point, sets the LOD calculated in the predictor and registers neighboring points, and according to the distance to the neighboring points
  • Lifting transform coding can be performed by setting weights.
  • Lifting transform coding according to embodiments is similar to the aforementioned predictive transform coding, but is different in that a weight is accumulated and applied to an attribute value.
  • the weights calculated for all predictors are additionally multiplied by the weights stored in the QW corresponding to the predictor index, and the calculated weights are cumulatively added to the update weight array as the indexes of neighboring nodes.
  • the value obtained by multiplying the calculated weight by the attribute value of the index of the neighbor node is accumulated and summed.
  • predictive attribute values are calculated by additionally multiplying the attribute values updated through the lift update process by the weights updated through the lift prediction process (stored in QW).
  • a point cloud encoder eg, the coefficient quantization unit 40011
  • a point cloud encoder eg, arithmetic encoder 40012
  • entropy codes the quantized attribute values.
  • the point cloud encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding for predicting the attributes of the nodes of the higher level by using the attributes associated with the nodes at the lower level of the octree.
  • RAHT transform coding is an example of attribute intra coding with octree backward scan.
  • the point cloud encoder according to the embodiments scans the entire area from the voxel, and repeats the merging process up to the root node while merging the voxels into a larger block at each step.
  • the merging process according to the embodiments is performed only for the ocupid node. A merging process is not performed on an empty node, and a merging process is performed on a node immediately above the empty node.
  • the following equation represents the RAHT transformation matrix. denotes the average attribute value of voxels in level l. Is Wow can be calculated from Wow the weight of class am.
  • the root node is the last class is created as follows,
  • the gDC value is also quantized and entropy-coded like the high-pass coefficient.
  • FIG. 10 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder shown in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1 , and may perform the same or similar operations to the operation of the point cloud video decoder 10006 described in FIG. 1 .
  • the point cloud decoder may receive a geometry bitstream and an attribute bitstream included in one or more bitstreams.
  • the point cloud decoder includes a geometry decoder and an attribute decoder.
  • the geometry decoder outputs decoded geometry by performing geometry decoding on the geometry bitstream.
  • the attribute decoder outputs decoded attributes by performing attribute decoding based on the decoded geometry and the attribute bitstream.
  • the decoded geometry and decoded attributes are used to reconstruct the point cloud content (decoded point cloud).
  • FIG. 11 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder shown in FIG. 11 is an example of the point cloud decoder described with reference to FIG. 10 , and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud encoder described with reference to FIGS. 1 to 9 .
  • the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed before attribute decoding.
  • a point cloud decoder may include an arithmetic decoder 11000, a synthesize octree 11001, a synthesize surface approximation 11002, and a reconstruct geometry , 11003), inverse transform coordinates (11004), arithmetic decoder (11005), inverse quantize (11006), RAHT transform unit (11007), LOD generator (generate LOD, 11008) ), an inverse lifting unit (Inverse lifting, 11009), and / or color inverse transform unit (inverse transform colors, 11010).
  • the arithmetic decoder 11000 , the octree synthesizer 11001 , the surface opproximation synthesizer 11002 , the geometry reconstruction unit 11003 , and the coordinate system inverse transformation unit 11004 may perform geometry decoding.
  • Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisup geometry decoding are optionally applied. Also, the geometry decoding is not limited to the above example, and is performed as a reverse process of the geometry encoding described with reference to FIGS. 1 to 9 .
  • the arithmetic decoder 11000 decodes the received geometry bitstream based on arithmetic coding.
  • the operation of the arithmetic decoder 11000 corresponds to the reverse process of the arithmetic encoder 40004 .
  • the octree synthesizing unit 11001 may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding).
  • a detailed description of the occupanci code is the same as described with reference to FIGS. 1 to 9 .
  • the surface op-proximation synthesizing unit 11002 may synthesize a surface based on the decoded geometry and/or the generated octree when the tri-top geometry encoding is applied.
  • the geometry reconstruction unit 11003 may reconstruct a geometry based on the surface and/or the decoded geometry. As described with reference to FIGS. 1 to 9 , direct coding and tri-soup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly brings and adds position information of points to which direct coding is applied. In addition, when tri-soup geometry encoding is applied, the geometry reconstruction unit 11003 may perform a reconstruction operation of the geometry reconstruction unit 40005, for example, triangle reconstruction, up-sampling, and voxelization to restore the geometry. have. Specific details are the same as those described with reference to FIG. 6 and thus will be omitted.
  • the reconstructed geometry may include a point cloud picture or frame that does not include attributes.
  • the coordinate system inverse transform unit 11004 may obtain positions of points by transforming the coordinate system based on the restored geometry.
  • the arithmetic decoder 11005, the inverse quantization unit 11006, the RAHT transform unit 11007, the LOD generation unit 11008, the inverse lifting unit 11009, and/or the color inverse transform unit 11010 are the attributes described with reference to FIG. decoding can be performed.
  • Attribute decoding according to embodiments includes Region Adaptive Hierarchical Transform (RAHT) decoding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding may be included.
  • RAHT Region Adaptive Hierarchical Transform
  • Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform decoding
  • interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (Lifting Transform)) decoding may be included.
  • the arithmetic decoder 11005 decodes an attribute bitstream by arithmetic coding.
  • the inverse quantization unit 11006 inverse quantizes the decoded attribute bitstream or information about the attribute secured as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on attribute encoding of the point cloud encoder.
  • the RAHT transformation unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may process the reconstructed geometry and dequantized attributes. As described above, the RAHT converting unit 11007, the LOD generating unit 11008 and/or the inverse lifting unit 11009 may selectively perform a corresponding decoding operation according to the encoding of the point cloud encoder.
  • the color inverse transform unit 11010 performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes.
  • the operation of the color inverse transform unit 11010 may be selectively performed based on the operation of the color transform unit 40006 of the point cloud encoder.
  • the elements of the point cloud decoder of FIG. 11 are not shown in the figure, hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing apparatus , software, firmware, or a combination thereof.
  • One or more processors may perform at least any one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above.
  • the one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of the elements of the point cloud decoder of FIG. 11 .
  • the transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or the point cloud encoder of FIG. 4 ).
  • the transmitting apparatus shown in FIG. 12 may perform at least any one or more of the same or similar operations and methods to the operations and encoding methods of the point cloud encoder described with reference to FIGS. 1 to 9 .
  • the transmission apparatus includes a data input unit 12000 , a quantization processing unit 12001 , a voxelization processing unit 12002 , an occupancy code generation unit 12003 , a surface model processing unit 12004 , and an intra/ Inter-coding processing unit 12005, arithmetic coder 12006, metadata processing unit 12007, color conversion processing unit 12008, attribute conversion processing unit (or attribute conversion processing unit) 12009, prediction/lifting/RAHT conversion It may include a processing unit 12010 , an arithmetic coder 12011 , and/or a transmission processing unit 12012 .
  • the data input unit 12000 receives or acquires point cloud data.
  • the data input unit 12000 may perform the same or similar operation and/or acquisition method to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described in FIG. 2 ).
  • the coder 12006 performs geometry encoding. Since the geometry encoding according to the embodiments is the same as or similar to the geometry encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.
  • the quantization processing unit 12001 quantizes a geometry (eg, a position value of points or a position value).
  • the operation and/or quantization of the quantization processing unit 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described with reference to FIG. 4 .
  • a detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the voxelization processing unit 12002 voxelizes position values of quantized points.
  • the voxelization processing unit 12002 may perform the same or similar operations and/or processes as those of the quantization unit 40001 described with reference to FIG. 4 and/or the voxelization process. A detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the octree occupancy code generator 12003 performs octree coding on the positions of voxelized points based on the octree structure.
  • the octree occupancy code generator 12003 may generate an occupanci code.
  • the octree occupancy code generator 12003 may perform the same or similar operations and/or methods to those of the point cloud encoder (or the octree analyzer 40002) described with reference to FIGS. 4 and 6 . A detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the surface model processing unit 12004 may perform tri-supply geometry encoding for reconstructing positions of points in a specific region (or node) based on a voxel based on a surface model.
  • the fore surface model processing unit 12004 may perform the same or similar operations and/or methods to those of the point cloud encoder (eg, the surface appropriation analyzer 40003) described with reference to FIG. 4 .
  • a detailed description is the same as that described with reference to FIGS. 1 to 9 .
  • the intra/inter coding processing unit 12005 may perform intra/inter coding of point cloud data.
  • the intra/inter coding processing unit 12005 may perform the same or similar coding to the intra/inter coding described with reference to FIG. 7 . A detailed description is the same as that described with reference to FIG. 7 .
  • the intra/inter coding processing unit 12005 may be included in the arithmetic coder 12006 .
  • the arithmetic coder 12006 entropy encodes an octree and/or an approximated octree of point cloud data.
  • the encoding method includes an arithmetic encoding method.
  • the arithmetic coder 12006 performs the same or similar operations and/or methods as the operations and/or methods of the arithmetic encoder 40004 .
  • the metadata processing unit 12007 processes metadata related to point cloud data, for example, a setting value, and provides it to necessary processing such as geometry encoding and/or attribute encoding. Also, the metadata processing unit 12007 according to embodiments may generate and/or process signaling information related to geometry encoding and/or attribute encoding. Signaling information according to embodiments may be encoded separately from geometry encoding and/or attribute encoding. Also, signaling information according to embodiments may be interleaved.
  • the color conversion processing unit 12008, the attribute conversion processing unit 12009, the prediction/lifting/RAHT conversion processing unit 12010, and the arithmetic coder 12011 perform attribute encoding. Since the attribute encoding according to the embodiments is the same as or similar to the attribute encoding described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.
  • the color conversion processing unit 12008 performs color conversion coding for converting color values included in the attributes.
  • the color conversion processing unit 12008 may 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 9 .
  • the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described with reference to FIG. 4 is performed. A detailed description will be omitted.
  • the attribute transformation processing unit 12009 performs an attribute transformation for transforming attributes based on positions where geometry encoding has not been performed and/or a reconstructed geometry.
  • the attribute transformation processing unit 12009 performs the same or similar operations and/or methods as those of the attribute transformation unit 40007 described in FIG. 4 . A detailed description will be omitted.
  • the prediction/lifting/RAHT transform processing unit 12010 may code the transformed attributes by any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding.
  • the prediction/lifting/RAHT transformation processing unit 12010 performs at least one or more of the same or similar operations to the operations of the RAHT transformation unit 40008, the LOD generation unit 40009, and the lifting transformation unit 40010 described with reference to FIG. 4 . do.
  • the descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described with reference to FIGS. 1 to 9 , a detailed description thereof will be omitted.
  • the arithmetic coder 12011 may encode coded attributes based on arithmetic coding.
  • the arithmetic coder 12011 performs the same or similar operations and/or methods to the operations and/or methods of the arithmetic encoder 400012 .
  • the transmission processing unit 12012 transmits each bitstream including the encoded geometry and/or the encoded attribute and metadata information, or converts the encoded geometry and/or the encoded attribute and metadata information into one It can be transmitted by composing it as a bitstream.
  • the bitstream may include one or more sub-bitstreams.
  • the bitstream according to the embodiments is a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of a geometry information coding, an attribute parameter set (APS) for signaling of an attribute information coding, a tile Signaling information including a Tile Parameter Set (TPS) for level signaling and slice data may be included.
  • SPS sequence parameter set
  • GPS geometry parameter set
  • APS attribute parameter set
  • TPS Tile Parameter Set
  • 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 ).
  • a slice refers to a series of syntax elements representing all or a part of a coded point cloud frame.
  • the TPS may include information about each tile (eg, coordinate value information and height/size information of a bounding box, etc.) for one or more tiles.
  • a geometry bitstream may include a header and a payload.
  • the header of the geometry bitstream according to the embodiments may include identification information (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id) of a parameter set included in GPS, and information about data included in a payload, etc.
  • the metadata processing unit 12007 may generate and/or process signaling information and transmit it to the transmission processing unit 12012 .
  • elements performing geometry encoding and elements performing attribute encoding may share data/information with each other as dotted lines are processed.
  • the transmission processing unit 12012 may perform the same or similar operation and/or transmission method to the operation and/or transmission method of the transmitter 10003 . Since the detailed description is the same as that described with reference to FIGS. 1 to 2 , a detailed description thereof will be omitted.
  • FIG. 13 is an example of a receiving apparatus according to embodiments.
  • the reception apparatus shown in FIG. 13 is an example of the reception apparatus 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11 ).
  • the receiving apparatus shown in FIG. 13 may perform at least any one or more of the same or similar operations and methods to the operations and decoding methods of the point cloud decoder described with reference to FIGS. 1 to 11 .
  • the reception apparatus includes a reception unit 13000 , a reception processing unit 13001 , an arithmetic decoder 13002 , an Occupancy code-based octree reconstruction processing unit 13003 , and a surface model processing unit (triangle reconstruction). , up-sampling, voxelization) 13004, inverse quantization processing unit 13005, metadata parser 13006, arithmetic decoder 13007, inverse quantization processing unit 13008, prediction It may include a /lifting/RAHT inverse transformation processing unit 13009 , an inverse color transformation processing unit 13010 , and/or a renderer 13011 .
  • Each component of decoding according to embodiments may perform a reverse process of a component of encoding according to embodiments.
  • the receiver 13000 receives point cloud data.
  • the receiver 13000 may perform the same or similar operation and/or reception method as the operation and/or reception method of the receiver 10005 of FIG. 1 . A detailed description will be omitted.
  • the reception processing unit 13001 may acquire a geometry bitstream and/or an attribute bitstream from the received data.
  • the reception processing unit 13001 may be included in the reception unit 13000 .
  • the arithmetic decoder 13002, the occupancy code-based octree reconstruction processing unit 13003, the surface model processing unit 13004, and the inverse quantization processing unit 13005 may perform geometry decoding. Since the geometry decoding according to the embodiments is the same as or similar to the geometry decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.
  • the arithmetic decoder 13002 may decode a geometry bitstream based on arithmetic coding.
  • the arithmetic decoder 13002 performs the same or similar operations and/or coding to the operations and/or coding of the arithmetic decoder 11000 .
  • the ocupancy code-based octree reconstruction processing unit 13003 may reconstruct the octopus by obtaining an occupanci code from a decoded geometry bitstream (or information about a geometry secured as a result of decoding).
  • the occupancy code-based octree reconstruction processing unit 13003 performs the same or similar operations and/or methods as those of the octree synthesis unit 11001 and/or the octree generation method.
  • the surface model processing unit 13004 may decode a trichop geometry based on the surface model method and reconstruct the geometry related thereto (eg, triangle reconstruction, up-sampling, voxelization) based on the surface model method when trisuple geometry encoding is applied. can be performed.
  • the surface model processing unit 13004 performs the same or similar operations to the operations of the surface op-proximation synthesis unit 11002 and/or the geometry reconstruction unit 11003 .
  • the inverse quantization processing unit 13005 may inverse quantize the decoded geometry.
  • the metadata parser 13006 may parse metadata included in the received point cloud data, for example, a setting value.
  • the metadata parser 13006 may pass the metadata to geometry decoding and/or attribute decoding. A detailed description of the metadata is the same as that described with reference to FIG. 12 , and thus will be omitted.
  • the arithmetic decoder 13007 , the inverse quantization processing unit 13008 , the prediction/lifting/RAHT inverse transformation processing unit 13009 , and the color inverse transformation processing unit 13010 perform attribute decoding. Since the attribute decoding is the same as or similar to the attribute decoding described with reference to FIGS. 1 to 10 , a detailed description thereof will be omitted.
  • the arithmetic decoder 13007 may decode the attribute bitstream by arithmetic coding.
  • the arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry.
  • the arithmetic decoder 13007 performs the same or similar operation and/or coding to the operation and/or coding of the arithmetic decoder 11005 .
  • the inverse quantization processing unit 13008 may inverse quantize the decoded attribute bitstream.
  • the inverse quantization processing unit 13008 performs the same or similar operations and/or methods to those of the inverse quantization unit 11006 and/or the inverse quantization method.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 may process the reconstructed geometry and inverse quantized attributes.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 performs the same or similar operations and/or decodings as the operations and/or decodings of the RAHT transform unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009 and/or At least any one or more of the decodings are performed.
  • the color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming color values (or textures) included in decoded attributes.
  • the color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding to the operation and/or inverse transform coding of the color inverse transform unit 11010 .
  • the renderer 13011 may render point cloud data.
  • FIG. 14 illustrates an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • the structure of FIG. 14 includes at least one or more of a server 1460 , a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 , and/or an HMD 1470 .
  • a configuration connected to the cloud network 1410 is shown.
  • the robot 1410 , the autonomous vehicle 1420 , the XR device 1430 , the smart phone 1440 , or the home appliance 1450 are referred to as devices.
  • the XR device 1430 may correspond to a point cloud data (PCC) device according to embodiments or may be linked with the PCC device.
  • PCC point cloud data
  • the cloud network 1400 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure.
  • the cloud network 1400 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.
  • LTE Long Term Evolution
  • the server 1460 includes at least one of a robot 1410 , an autonomous vehicle 1420 , an XR device 1430 , a smartphone 1440 , a home appliance 1450 and/or an HMD 1470 , and a cloud network 1400 . It is connected through and may help at least a part of the processing of the connected devices 1410 to 1470 .
  • a Head-Mount Display (HMD) 1470 represents one of the types in which an XR device and/or a PCC device according to embodiments may 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.
  • the devices 1410 to 1450 shown in FIG. 14 may be linked/coupled with the point cloud data transmission/reception device according to the above-described embodiments.
  • XR / PCC device 1430 is PCC and / or XR (AR + VR) technology is applied, HMD (Head-Mount Display), HUD (Head-Up Display) provided in the vehicle, television, mobile phone, smart phone, It may be implemented as a computer, a wearable device, a home appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.
  • HMD Head-Mount Display
  • HUD Head-Up Display
  • the XR/PCC device 1430 analyzes three-dimensional point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for three-dimensional points in the surrounding space or real objects. Information can be obtained and the XR object to be output can be rendered and output. For example, the XR/PCC apparatus 1430 may output an XR object including additional information on the recognized object to correspond to the recognized object.
  • the XR/PCC device 1430 may be implemented as a mobile phone 1440 or the like to which PCC technology is applied.
  • the mobile phone 1440 may decode and display the point cloud content based on the PCC technology.
  • the autonomous vehicle 1420 may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying PCC technology and XR technology.
  • the autonomous driving vehicle 1420 to which the XR/PCC technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image.
  • the autonomous driving vehicle 1420 that is the target of control/interaction within the XR image may be distinguished from the XR device 1430 and may be interlocked with each other.
  • the autonomous vehicle 1420 having means for providing an XR/PCC image may obtain sensor information from sensors including a camera, and output an XR/PCC image generated based on the acquired sensor information.
  • the autonomous vehicle 1420 may provide an XR/PCC object corresponding to a real object or an object in a screen to a passenger by outputting an XR/PCC image with a HUD.
  • the XR/PCC object when the XR/PCC object is output to the HUD, at least a portion of the XR/PCC object may be output to overlap the real object to which the passenger's gaze is directed.
  • the XR/PCC object 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 to overlap the object in the screen.
  • the autonomous vehicle 1220 may output XR/PCC objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.
  • VR Virtual Reality
  • AR Augmented Reality
  • MR Magnetic Reality
  • PCC Point Cloud Compression
  • VR technology is a display technology that provides objects or backgrounds in the real world only as CG images.
  • AR technology refers to a technology that shows a virtual CG image on top of an actual object image.
  • the MR technology is similar to the AR technology described above in that it shows the virtual objects by mixing and combining them in the real world.
  • AR technology the distinction between real objects and virtual objects made of CG images is clear, and virtual objects are used in a form that complements real objects, whereas in MR technology, virtual objects are regarded as having the same characteristics as real objects. distinct from technology. More specifically, for example, a hologram service to which the aforementioned MR technology is applied.
  • VR, AR, and MR technologies are sometimes called XR (extended reality) technologies rather than clearly distinguishing them. Accordingly, embodiments of the present invention are applicable to all of VR, AR, MR, and XR technologies. For these technologies, encoding/decoding based on PCC, V-PCC, and G-PCC technologies may be applied.
  • the PCC method/apparatus according to the embodiments may be applied to a vehicle providing an autonomous driving service.
  • a vehicle providing an autonomous driving service is connected to a PCC device for wired/wireless communication.
  • the point cloud data (PCC) transceiver receives/processes AR/VR/PCC service-related content data that can be provided together with the autonomous driving service when connected to a vehicle to enable wired/wireless communication, can be sent to
  • the point cloud transceiver may receive/process AR/VR/PCC service-related content data according to a user input signal input through the user interface device and provide it to the user.
  • a vehicle or a user interface device may receive a user input signal.
  • a user input signal according to embodiments may include a signal indicating an autonomous driving service.
  • 15 is a block diagram illustrating an apparatus for transmitting point cloud data according to embodiments.
  • the encoder 15 is an in-cloud data transmission apparatus (eg, the transmission device 10000 of FIG. 1 , the point cloud video encoder 10002 of FIG. 1 , the point cloud encoder of FIG. 4 , and the transmission device of FIG. 12 according to embodiments. , a block representing the point cloud data encoder 1500 included in the encoder of FIG. 12 and the XR device 1430 of FIG. 14 ).
  • the encoder according to embodiments may include an attribute transform unit 1501 , an attribute encoder 1502 , a quantizer 1503 , and/or an entropy coder 1504 .
  • the encoding unit according to the embodiments may further include one or more elements for performing the same or similar operation to the encoding operation described with reference to FIGS. 1 to 14 .
  • the attribute transform unit may perform attribute transformation for transforming an attribute based on a geometry of the point cloud data.
  • the attribute transform unit according to embodiments may perform the same or similar operation to the operation of the attribute transform unit 40007 described above with reference to FIG. 4 .
  • the attribute encoder may include Region Adaptive Hierarchical Transform (RAHT) coding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, or interpolation-based hierarchical nearest-neighbor prediction with an update/lifting.
  • Attribute encoding eg, the attribute encoding described in FIGS. 2, 4, 6, 8, and 12 to 14
  • RAHT, predictive transform and lifting are the same or similar to those described above in FIG. 4 .
  • the attribute converted by the attribute converter may be input to the RAHT converter or the LOD generator.
  • the transformed attribute may be input to the RAHT transform unit.
  • the transformed attribute may be input to the LOD generator.
  • the attribute encoder may include an LOD generator, a predictive transform unit, and/or a lifting transform unit.
  • the LOD generator (not shown in this figure) may generate an LOD to perform predictive transformation and/or lifting transformation.
  • the LOD generator according to embodiments may perform the same or similar operation to the operation of the LOD generator 40009 described above with reference to FIG. 4 .
  • the LOD according to the embodiments is the same as or similar to the LOD described above with reference to FIGS. 4, 8, 9, and 11 to 13 .
  • the LOD according to embodiments may be generated by rearranging points.
  • the prediction transformation unit (not shown in this figure) may calculate a prediction attribute of each point of the point cloud data.
  • the prediction attribute of a point may be generated based on an average value of values obtained by multiplying attributes of neighboring points of the point by a weight calculated based on the distance to each neighboring point.
  • a weight calculated based on a distance of a point to each neighboring point according to embodiments may be referred to as a prediction weight. That is, the prediction weight value may be a value calculated based on a distance between a point that is a prediction attribute calculation target and one or more neighboring points of the point.
  • the predictive transform unit according to embodiments may perform the same or similar operation to the predictive transform coding described above with reference to FIG. 9 .
  • the lifting transform unit (not shown in this figure) may perform an operation including the above-described prediction transform unit operation. That is, the lifting transform unit may be built on the above-described prediction transform unit (The Lifting Transform builds on top of the Predicting Transform).
  • lifting transform coding is similar to predictive transform coding, but is different in that it includes a process of accumulatively applying the above-described weights. The process of accumulatively applying the weights is the same as or similar to that described above with reference to FIG. 9 .
  • the cumulatively applied weight according to the embodiments may be referred to as a quantization weight.
  • the quantizer according to embodiments may quantize the encoded attribute.
  • the quantizer according to embodiments may perform the same or similar operation as that of the coefficient quantizer 40011 of FIG. 4 .
  • the entropy coder may encode the quantized attribute based on arithmetic coding.
  • the entropy coder according to embodiments may perform the same or similar operation to the operation of the arithmetic encoder 40012 described above with reference to FIG. 4 .
  • the point cloud data transmission apparatus may perform attribute encoding on attribute information of point cloud data through the encoder described in FIG. 15, and transmit a bitstream in which a geometry bitstream and/or an attribute bitstream is multiplexed. have.
  • the bitstream according to the embodiments may further include signaling information regarding the above-described attribute encoding.
  • the point cloud data transmission apparatus may encapsulate the bitstream and transmit it in the form of segments and/or files.
  • 16 is a block diagram illustrating an example of a lifting transformation process according to embodiments.
  • FIG. 16 is a block diagram illustrating an example of a lifting transformation process for an attribute (attribute described in FIGS. 1 to 15) performed by a lifting transformation unit (eg, the lifting transformation unit described in FIG. 15) according to embodiments; am.
  • a lifting transformation unit eg, the lifting transformation unit described in FIG. 15
  • the lifting transformation process may include a split process 1601 , a prediction process 1602 , an update process 1603 , and/or a feedback process 1604 .
  • can 1601 to 1604 may be performed by one or more elements included in the lifting transformation unit.
  • the lifting conversion unit according to embodiments may perform 1601 to 1604 only once, or may be performed multiple times (eg, N times).
  • the lifting transformation process according to embodiments may be performed in units of attributes corresponding to one point.
  • the split process receives an attribute (s_(k, j-1)), and sets the received attribute to d_(i, j-1) (or first component) and s_(i, j-1) (or a second component) may include a process of splitting.
  • the split process may separate the input attribute into a first component and a second component through sampling.
  • the first component may be referred to as a high frequency component (or high frequency)
  • the second component may be referred to as a low frequency component (or low frequency).
  • the attribute (s_(k, j-1)), the first component, and the second component may be substituted with each other.
  • k is an identifier for identifying an attribute of a point.
  • k represents the attribute of the k-th point among all N points.
  • the lifting transformation process may be performed multiple times.
  • j of s_(k, j-1) is an identifier for identifying the current lifting transformation process among the entire lifting transformation process.
  • j represents a j-th lifting transformation process (eg, 1601 to 1603) among all N lifting transformation processes.
  • the attribute of s_(k, j-1) may be output as s_(k, j) through one lifting transformation process (eg, 1601 to 1603).
  • i is an identifier for identifying a splitting process.
  • i denotes an i-th split process among all N split processes.
  • j of d_(i, j-1) representing the first component and s_(i, j-1) representing the second component is the same as described above.
  • the prediction process may include calculating a predicted first component based on the second component. That is, the second component may be used to calculate the predicted value of the first component.
  • the process of calculating the predicted first component according to the embodiments is the same as or similar to the process of calculating the prediction attribute described above with reference to FIG. 15 . That is, the predicted first component may correspond to the prediction attribute of the attribute s_(k, j-1). As described above with reference to FIG. 15 , the process of calculating the prediction attribute may be based on the prediction weight value p_(i, j). Descriptions of i and j of the prediction weight value p_(i, j) are the same as described above.
  • the prediction weight value p_(i, j) may be calculated as follows.
  • x_i, y_i, and z_i represent x-coordinate values, y-coordinate values, and z-coordinate values of coordinates indicating the position of a point to be an attribute prediction.
  • Theta represents 1 or more real numbers. That is, the prediction weight may be calculated based on the distance from the point that is the target of attribute prediction to neighboring points.
  • the prediction weight of the target point may exist as many (eg, three) as the number of neighboring points of the target point.
  • the method of calculating the prediction weight according to the embodiments is not limited to the above-described example.
  • the lifting transform unit subtracts the predicted first component (the first component predicted through the prediction process) from the first component (or the first voice substituted with the second component), so that the attribute s_(k , j-1)) of the prediction residual value d_(i, j)) can be calculated. That is, the difference between the predicted values of the attribute (s_(k, j-1)) and the attribute (s_(k, j-1)) is the difference between the first component and the predicted value of the first component (or the predicted first component). may be applicable.
  • j is an identifier for identifying current lifting transformation processes 1601 to 1603 among all lifting transformation processes.
  • a prediction residual value according to embodiments is input to a process 1603, and information that has been processed in 1603 may be used for a next lifting transformation process. Therefore, when the above-described process of obtaining the prediction residual value is performed, j increases by 1. That is, the prediction residual value d_(i, j) according to the embodiments may be expressed as follows.
  • N_(i, j) represents a set of neighboring points of a point corresponding to an attribute subject to a lifting transformation process.
  • N_(i, j) ⁇ u_j may represent the intersection of a set of neighboring points used in a prediction process and a set of neighboring points used in a prediction update process among a set of neighboring points of a point subject to lifting transformation. That is, in the above equation, s_(k, j-1) represents each attribute of neighboring points. Accordingly, the predicted first component may be calculated as the sum of values obtained by multiplying each attribute of the neighboring points by a dictionary weight.
  • a set of neighboring points of a point to be subjected to lifting transformation according to embodiments, neighboring points used in a prediction process, and a set of neighboring points used in a prediction update process may be the same as or different from each other.
  • points belonging to an arbitrary LOD are included in points belonging to an LOD having a level lower than the level of the arbitrary LOD.
  • the prediction process is performed based on the attributes of neighboring points of the point to be subjected to the prediction process. Accordingly, the attributes of points belonging to the low-level LOD can be used more in the prediction process than the attributes of the points belonging to the high-level LOD. That is, by the LOD-based prediction process, the attributes of points belonging to the low-level LOD may be more influential than the attributes of the points belonging to the high-level LOD.
  • the lifting transformation process according to embodiments may be performed from a point belonging to the highest level LOD (LOD n). That is, the lifting transformation process may be performed in descending order of the LOD level.
  • the lifting transform unit according to embodiments may perform an update process 1603 based on a quantization weight to reflect the above-described influence.
  • the updating process according to the embodiments may include calculating the quantization weight value u_(n, j) (the quantization weight value described with reference to FIG. 15 ).
  • a quantization weight according to embodiments may be calculated (or generated) based on a prediction value.
  • a quantization weight according to embodiments may exist as many as the number of neighboring points of a target point, like the above-described prediction weight.
  • An example of a method for obtaining a quantization weight is as follows.
  • a weight value may be preset to 1 for all points (or a point attribute that is a target of the points).
  • a quantization weight value of the checked point is generated (or calculated) based on the weight value 1 described above.
  • the quantization weight of the currently checked point (eg, the nth checked point) is based on the quantization weight and the prediction weight corresponding to the previously checked point (eg, the n ⁇ 1th checked point). can be calculated as
  • the currently checked point may correspond to a point to be subjected to the above-described lifting transformation.
  • An example of a process for calculating a quantization weight of a checked point according to embodiments is as follows.
  • the updating process may include updating the prediction residual value d_(i, j) based on the calculated quantization weight value u_(n, j).
  • n of u_(n, j) representing a quantization weight is an identifier for identifying a prediction process. For example, n indicates that a prediction residual value that has undergone an n-th prediction process among all N prediction processes is input to the update process.
  • the description of j of u_(n, j) representing the quantization weight is the same as described above.
  • the update process according to the embodiments may be performed based on u_(k, i, j) generated based on the quantization weight value u_(n, j).
  • i is an identifier for identifying neighboring points of a target point. That is, i may be greater than or equal to 0 and less than or equal to a value obtained by subtracting 1 from a value corresponding to the number of neighboring points.
  • the quantization weight value u_(n, j) may exist as many as the number of neighboring points of the target point. That is, a quantization weight may exist for each of the neighboring points.
  • u_(k, i, j) may represent a ratio of a quantization weight value to an i-th neighboring point in the total of quantization weight values of a target point.
  • n may be less than or equal to k and/or i.
  • u_(k, i, j) corresponds to a value obtained by dividing the quantization weight of the i-th neighboring point by the sum of the quantization weights of the target point.
  • quantization weights Both u_(n, j) and u_(k, i, j) according to embodiments may be referred to as quantization weights.
  • the update process according to the embodiments may update the prediction residual value d_(i, j) based on u_(k, i, j).
  • the updating process according to the embodiments may include multiplying the calculated prediction residual value by the above-described quantization weight value.
  • the updated prediction residual value may be calculated as a sum of values obtained by multiplying the calculated prediction residual value by the above-described u_(k, i, j).
  • the lifting transform unit may transform an attribute by adding an updated prediction residual value to an existing attribute (eg, s_(k, j-1)). That is, the attribute transformed by the lifting transform unit according to the embodiments may be expressed as follows.
  • N_(k, j) represents a set of neighboring points of a point corresponding to an attribute s_(k, j-1) that is a target of the lifting transformation process.
  • N_(k, j) ⁇ P_j represents neighboring points used in the prediction process among a set of neighboring points of the point corresponding to the attribute s_(k, j-1).
  • a prediction weight and/or a quantization weight according to embodiments may correspond to a filter factor of a filter for the above-described prediction process and/or update process.
  • a method of obtaining a filter coefficient of a filter for a prediction process and/or an update process according to embodiments is not limited to the above-described example.
  • the above-described lifting transformation process considers the distance between a point to be subjected to lifting transformation and neighboring points of the point. For example, both the prediction weight and the quantization weight described above are weight values calculated by considering only the distance between the target point and the neighboring points of the target point. That is, the attribute transformed by the above-described lifting transformation process is determined based only on the distance from the target point to the neighboring points.
  • an attribute of the point cloud data may have a compression (eg, attribute encoding) error depending on a sampling environment, a frequency band, and the like.
  • the lifting transformation process by the lifting transformation unit according to the embodiments may further include a feedback process 1604 to improve the transformation process based only on distances to neighboring points as described above.
  • the feedback process according to the embodiments may be performed by one or more elements included in the lifting transformation unit.
  • the feedback process according to the embodiments may include a process of feeding back the above-described transformed attribute based on an alpha value (or a first value) and a beta value (or a second value).
  • the first value according to embodiments may be a value based on a sampling frequency and a cut-off frequency (or a cut-off frequency) of the point cloud data.
  • the second value according to embodiments may correspond to a real number (or integer) of 1 or more.
  • the first value according to embodiments may be generated based on an exponential function, and the exponential of the exponential function may include a sampling frequency and a cutoff frequency. Examples of the first value alpha and the second value beta according to embodiments are as follows.
  • f_c represents the cutoff frequency of the point cloud data.
  • f_s represents the sampling frequency of the point cloud data.
  • the first value according to embodiments may be expressed as follows.
  • the first value according to the embodiments is expressed as the equation of Equation 5, so that complexity can be reduced compared to the equation of Equation 4.
  • An example of a process of feeding back the transformed attribute based on the first value and the second value according to the embodiments is as follows.
  • the existing attribute included in the transformed attribute according to the embodiments is fed back based on the first value, and the updated prediction residual value included in the transformed attribute is obtained by subtracting the first value from the second value. can be fed back.
  • the lifting transformation unit further performs a feedback process based on the first value and the second value, further considering the distance from the target point to neighboring points, as well as the sampling environment of the point cloud data, the frequency band, etc. to transform the attribute.
  • the transmitting apparatus may include information on whether to perform feedback on the converted attribute in a bitstream and transmit it to the receiving apparatus.
  • Information on whether to perform feedback on the transformed attribute according to embodiments indicates whether a feedback process is performed.
  • information on whether to perform feedback on the transformed attribute may indicate that the transformed attribute is fed back.
  • the bitstream includes information on the above-described first value, information on the second value, information on the sampling frequency, and cutoff frequency. It may include more information about.
  • the lifting transformation process may be performed multiple times (eg, N times). Accordingly, the fed back attribute s'_(k, j) according to embodiments may again undergo a split process, a prediction process, an update process, and/or a feedback process.
  • 17 is a flowchart of a method for transmitting point cloud data according to embodiments.
  • a method of transmitting point cloud data includes a process of receiving an attribute (1700), a process of generating an LOD (1701), a lifting transformation process (1702), a quantization process (1703), and/or an arithmetic encoding process ( 1704).
  • the method for transmitting point cloud data according to embodiments may further include one or more processes for transmitting the point cloud data.
  • a process of receiving an attribute may include a process of receiving an attribute of points of a point cloud video.
  • the attribute of points of a point cloud video according to embodiments may be data acquired by the acquirer 10001 described above with reference to FIG. 2 .
  • the process of generating the LOD according to the embodiments may include a process of reorganizing points distributed in a 3D space into a set of refinement levels.
  • LODs according to embodiments are the same as or similar to those described above with reference to FIGS. 1 to 16 .
  • the process of generating the LOD according to the embodiments may be the same as or similar to the operation of the LOD generator 40009 described above with reference to FIG. 4 and the operation of the LOD generator described with reference to FIG. 15 .
  • the lifting transform process according to the embodiments may include transforming the attributes of points based on lifting transform coding.
  • the lifting transformation process according to the embodiments is the same as or similar to that described above with reference to FIGS. 15 to 16 .
  • the size of the attribute bitstream may be reduced by transforming the attributes of points by the lifting transformation process according to the embodiments and storing them in the bitstream.
  • the lifting transformation process may include a process of feeding back transformed attributes (eg, the feedback process 1604 of FIG. 16 ).
  • a feedback may be performed on a transformed attribute that is a result of the lifting transformation in consideration of a sampling environment and/or a frequency band. That is, through the feedback process, the point cloud data transmission apparatus can reduce distortion in the attribute encoding process.
  • a quantization process according to embodiments may include a process of quantizing an encoded attribute.
  • the quantization process according to the embodiments is the same as or similar to the operation of the coefficient quantization unit 40011 of FIG. 4 .
  • the quantization process according to the embodiments is the same as or similar to the operation of the quantization unit of FIG. 15 .
  • the arithmetic encoding process according to the embodiments may include encoding the quantized attribute based on arithmetic coding.
  • An arithmetic encoding process according to embodiments is the same as or similar to the operation of the arithmetic encoder 40012 of FIG. 4 .
  • An arithmetic encoding process according to embodiments is the same as or similar to the operation of the entropy coder of FIG. 15 .
  • the transmitting device may transmit a bitstream (eg, the bitstream described with reference to FIGS. 1 to 16 ) to the receiving device according to the point cloud data transmission method described with reference to FIG. 17 .
  • the bitstream may include a geometry bitstream, an attribute bitstream, and/or information about the lifting transformation process described with reference to FIGS. 15 to 16 .
  • FIG. 18 shows a structure of a bitstream according to embodiments.
  • the point cloud processing apparatus may transmit the encoded point cloud data in the form of a bitstream.
  • a bitstream is a sequence of bits that forms a representation of point cloud data (or point cloud frame).
  • Point cloud data (or point cloud frame) may be divided into tiles and slices.
  • Point cloud data may be partitioned into multiple slices and encoded within a bitstream.
  • One slice is a set of points and is expressed as a series of syntax elements representing all or part of encoded point cloud data.
  • One slice may or may not have a dependency on other slices.
  • one slice includes one geometry data unit, and may or may not have one or more attribute data units (zero attribute data unit).
  • the attribute data unit is based on the geometry data unit within the same slice. That is, the point cloud data receiving device (for example, the receiving device 10004 or the point cloud video decoder 10006) may process the attribute data based on the decoded geometry data. Therefore, within a slice, a geometry data unit must appear before the associated attribute data units. Data units within a slice are necessarily contiguous, and the order between slices is not specified.
  • a tile is a rectangular cuboid (three-dimensional) in a bounding box (eg, the bounding box described in FIG. 5).
  • a bounding box may contain one or more tiles.
  • One tile may completely or partially overlap another tile.
  • One tile may include one or more slices.
  • the point cloud data transmitting apparatus may provide high-quality point cloud content by processing data corresponding to a tile according to importance. That is, the point cloud data transmission apparatus according to the embodiments may perform point cloud compression coding processing on data corresponding to an area important to a user with better compression efficiency and appropriate latency.
  • a bitstream includes signaling information and a plurality of slices (slice 0, ..., slice n).
  • signaling information appears before slices in the bitstream.
  • the point cloud data receiving apparatus may first secure signaling information and sequentially or selectively process a plurality of slices based on the signaling information.
  • slice 0 (slice0) includes one geometry data unit (Geom00) and two attribute data units (Attr00, Attr10).
  • geometry data units appear before attribute data units within the same slice. Therefore, the point cloud data receiving apparatus first processes (decodes) the geometry data unit (or geometry data), and processes the attribute data unit (or attribute data) based on the processed geometry data.
  • the signaling information according to the embodiments may be referred to as signaling data, metadata, or the like, and is not limited to examples.
  • the signaling information includes a sequence parameter set (SPS), a geometry parameter set (GPS), and one or more attribute parameter sets (APS).
  • SPS is encoding information for the entire sequence, such as profile and level, and may include comprehensive information (sequence level) for the entire sequence, such as picture resolution and video format.
  • GPS is information about the geometry encoding applied to the geometry included in the sequence (bitstream).
  • the GPS may include information on an octree (eg, the octree described in FIG. 6 ), information on an octree depth, and the like.
  • APS is information on attribute encoding to which an attribute is included in a sequence (bitstream). As shown in the figure, the bitstream includes one or more APSs (eg, APS0, APS1.. shown in the figure) according to an identifier for identifying an attribute.
  • the signaling information according to embodiments may further include a TPS.
  • the TPS is information about a tile, and may include information about a tile identifier, a tile size, and the like.
  • the signaling information according to the embodiments is information of a sequence, that is, a bitstream level, and is applied to a corresponding bitstream.
  • the signaling information has a syntax structure including a syntax element and a descriptor for describing it. A pseudo code for describing the syntax may be used.
  • the point cloud receiving apparatus may sequentially parse and process the syntax elements appearing in the syntax.
  • the geometry data unit and the attribute data unit include a geometry header and an attribute header, respectively.
  • the geometry header and the attribute header according to the embodiments have the above-described syntax structure as signaling information applied at a corresponding slice level.
  • a geometry header includes information (or signaling information) for processing a corresponding geometry data unit. Therefore, the geometry header appears first in the corresponding geometry data unit.
  • the point cloud receiving apparatus may process the geometry data unit by first parsing the geometry header.
  • the geometry header has a relationship with the GPS including information on the entire geometry. Accordingly, the geometry header includes information specifying gps_geom_parameter_set_id included in GPS. Also, the geometry header includes tile information (eg, tile_id) related to the slice to which the geometry data unit belongs, and a slice identifier.
  • the attribute header includes information (or signaling information) for processing the corresponding attribute data unit. Therefore, the attribute header appears first in the corresponding attribute data unit.
  • the point cloud receiving apparatus may process the attribute data unit by first parsing the attribute header.
  • the attribute header has a relationship with the APS that includes information about all attributes. Accordingly, the attribute header includes information specifying aps_attr_parameter_set_id included in the APS. As described above, since attribute decoding is based on geometry decoding, the attribute header includes information specifying the slice identifier included in the geometry header in order to determine the geometry data unit associated with the corresponding attribute data unit.
  • FIG. 19 illustrates a structure of an Attribute Parameter Set (APS) of point cloud data according to embodiments.
  • APS Attribute Parameter Set
  • a bitstream of point cloud data according to embodiments may include an attribute parameter set described in this figure.
  • the attribute parameter set described in FIG. 19 may refer to the attribute parameter set described in FIG. 18 .
  • the point cloud data receiver according to the embodiments may decode the point cloud data according to the embodiments based on the attribute parameter set described with reference to FIG. 19 .
  • aps_attr_parameter_set_id may indicate an identifier for the APS for reference according to other syntax elements.
  • the value of aps_attr_parameter_set_id must be within the range of 0 to 15.
  • aps_seq_parameter_set_id may indicate a value of sps_seq_parameter_set_id for an active SPS.
  • the value of aps_seq_parameter_set_id must be within the range of 0 to 15.
  • Attr_coding_type may indicate a coding type for an attribute for a given value of attr_coding_type.
  • the value of attr_coding_type must be 0, 1, or 2.
  • the APS according to the embodiments may include aps_infinite_filter_flag (information on whether to perform feedback on the transformed attribute).
  • a description of aps_infinite_filter_flag according to embodiments is the same as or similar to a description of information on whether to perform the feedback described above with reference to FIG. 16 . That is, the aps_infinite_filter_flag may be flag information indicating whether a feedback process (eg, the feedback process 1604 of FIG. 16 ) is performed in the lifting transformation process.
  • isLifting (information on whether lifting transformation is performed) indicates whether a coding type for an attribute according to embodiments is a method based on a lifting method. For example, isLifting indicates whether the coding type is predicting weight lifting or fixed weight lifting. isLifting may have a specific value (eg, 0 or 1) to indicate whether the coding type for the attribute is a method based on the lifting method.
  • Attr_coding_type when the value of attr_coding_type according to the embodiments is 0 (ie, the coding type for the attribute is predictive weight lifting) or the value of attr_coding_type is 2 (ie, the coding type for the attribute is fixed) In case of weight lifting), isLifting may be 1 (ie, it may indicate that a coding type for an attribute according to embodiments is a method based on a lifting method). For example, when the value of attr_coding_type according to the embodiments is 1 (ie, when the coding type for the attribute is RAHT), isLifting may be 0 (ie, the coding type for the attribute according to the embodiments) It may indicate that the method is not based on this lifting method).
  • the APS according to the embodiments may include some or all of num_pred_nearest_neighbours, max_num_direct_predictors, lifting_search_range, lifting_quant_step_size, lifting_quant_step_size_chroma, lod_binary_tree_enabled_flag, and num_detail_levels_minus1 parameters when the isLifting information according to the embodiments is 1. Also, when the isLifting information according to the embodiments is 1, the APS 30000 according to the embodiments may include sampling_distance_squared information as much as the value of num_detail_levels_minus1 (ie, the number of LODs).
  • num_pred_nearest_neighbors (information on the number of neighboring points) may be information about the maximum number of near-list neighbors.
  • the value of numberOfNearestNeighboursInPrediction must be within the range of 1 to xx.
  • max_num_direct_predictors (information on the number of predictors for prediction) is information that may indicate the number of predictors used for direct prediction.
  • the value of max_num_direct_predictors must be within the range of 0 to num_pred_nearest_neighbours.
  • the value of the variable MaxNumPredictors used in the decoding operation can be expressed as follows.
  • MaxNumPredictors max_num_direct_predicots + 1
  • lifting_search_range (lifting transformation search range information) may mean a search range for lifting.
  • lifting_quant_step_size (lifting transform quantization step information) may indicate a quantization step for the first component of the attribute.
  • the value of lifting_quant_step_size must be within the range of 1 to xx.
  • lifting_quant_step_size_chroma (lifting transform quantization chroma information) may indicate a quantization step size for a chroma component of an attribute if the attribute is a color.
  • the value of lifting_quant_step_size_chroma must be within the range of 1 to xx.
  • lod_binary_tree_enabled_flag (information on whether to use the LOD binary tree) may indicate whether or not the binary tree is applied to the log generation.
  • num_detail_levels_minus1 (LOD number information) indicates the number of levels of detail for attribute encoding (attribute coding). The value of num_detail_levels_minus1 must be within the range of 0 to xx.
  • the APS according to the embodiments may further include aps_filter_alpha, aps_filter_beta, aps_filter_sampling_freq and/or aps_filter_cutoff_freq in response to the aforementioned aps_infinite_filter_flag indicating that the transformed attribute is fed back.
  • aps_filter_alpha (information about the first value) may indicate the first value (or alpha value).
  • the first value according to the embodiments is the same as or similar to that described above with reference to FIG. 16 .
  • the first value according to embodiments may be preset to 5.
  • aps_filter_beta (information about the second value) may indicate the second value (or beta value).
  • the second value according to the embodiments is the same as or similar to that described above with reference to FIG. 16 .
  • the second value according to embodiments may be preset to 2.
  • aps_filter_sampling_freq (information about the sampling frequency) may indicate a sampling frequency of point cloud data.
  • the sampling frequency according to the embodiments is the same as or similar to that described above with reference to FIG. 16 .
  • the sampling frequency according to embodiments may be preset to 1.
  • aps_filter_cutoff_freq (information about cutoff frequency) may indicate a cutoff frequency of point cloud data.
  • the cutoff frequency according to the embodiments is the same as or similar to that described above with reference to FIG. 16 .
  • the cutoff frequency according to embodiments may be preset to 1.
  • sampling_distance_squared [ idx ] may indicate the square of a sampling distance with respect to idx.
  • the value of sampling_distance_squared must be within the range of 0 to xx.
  • idx according to embodiments may have a range of a value of 0 to num_detail_levels_minus1. That is, the APS 30000 according to the embodiments may include the sampling_distance_squared parameter as many as the number of levels of detail (ie, num_detail_levels_minus1+1) for total attribute coding (attribute coding).
  • the APS according to the embodiments may further include an adaptive_prediction_threshold parameter when the value of attr_coding_type according to the embodiments is 0 (ie, when the coding type for the attribute is predicting weight lifting).
  • adaptive_prediction_threshold may indicate a threshold value of prediction (prediction threshold information).
  • the APS according to the embodiments may further include raht_depth, raht_binarylevel_threshold, and raht_quant_step_size parameters when the value of attr_coding_type according to the embodiments is 1 (ie, when the coding type for the attribute is RAHT).
  • raht_depth may mean the number of levels of detail for RAHT.
  • the value of depthRAHT may range from 1 to xx.
  • raht_binarylevel_threshold may mean a level of detail for cutting out a RAHT coefficient.
  • the value of binaryLevelThresholdRAHT must be in the range of 0 to xx.
  • raht_quant_step_size (RAHT quantization step size information) may indicate the size of a quantization operation for the first component of an attribute.
  • the value of quant_step_size must be in the range of 1 to xx.
  • aps_extension_present_flag 1 indicates that the aps_extension_data syntax structure exists in the APS RBSP syntax structure. If aps_extension_present_flag is 0, this indicates that this syntax structure described above does not exist. If the corresponding parameter does not exist, the value of aps_extension_present_flag may be interpreted as 0.
  • the APS 30000 may further include an aps_extension_data_flag parameter.
  • aps_extension_data_flag may have any value. Its presence and value may not affect decoder performance.
  • the point cloud data transmission apparatus transmits the information of the attribute parameter set described in FIG. 19, thereby causing the receiving apparatus to increase the compression rate and provide a high quality point cloud image by searching for a nearby neighbor node. can be encouraged to do so.
  • the point cloud data transmission apparatus may transmit the bitstream in the form described above, thereby increasing compression efficiency, increasing image quality performance, and reducing the burden on the receiving apparatus.
  • 20 is a block diagram illustrating an apparatus for receiving point cloud data according to embodiments.
  • FIG. 20 is a point cloud data receiving apparatus (eg, the receiving apparatus 10004 of FIG. 1 , the point cloud video decoder 10006 of FIG. 1 , the decoder of FIG. 10 , the receiving apparatus of FIG. 13 and FIG. 14 is a block diagram illustrating the point cloud data decoding unit 2000 included in the XR device 1430).
  • the decoding unit according to the embodiments may perform the same or similar operation to the decoding operation described with reference to FIGS. 1 to 19 , and may perform the reverse process of the operation of the encoding unit of FIG. 15 .
  • the decoder according to the embodiments may include an entropy decoder 2001 , an inverse quantizer 2002 , an attribute decoder 2003 , and/or an attribute transform unit 2004 .
  • the decoder according to the embodiments may further include one or more elements for performing the same or similar operation to the decoding operation described with reference to FIGS. 1 to 19 .
  • the entropy decoder according to the embodiments may decode the received attribute bitstream based on arithmetic coding.
  • the entropy decoder according to the embodiments may perform the same or similar operation as the operation of the arithmetic decoder 11005 described with reference to FIG. 11 .
  • the entropy decoder according to the embodiments may perform an operation corresponding to a reverse process of the operation of the entropy coder 1504 of FIG. 15 .
  • the inverse quantizer according to embodiments may perform inverse quantization on the attribute bitstream.
  • the inverse quantizer according to the embodiments may perform the same or similar operation to that of the inverse quantizer 11006 described with reference to FIG. 11 .
  • the inverse quantizer according to embodiments may perform an operation corresponding to a reverse process of the operation of the quantizer 1503 of FIG. 15 .
  • the attribute decoder may include Region Adaptive Hierarchical Transform (RAHT) coding, Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding, or interpolation-based hierarchical nearest-neighbor prediction with an update/lifting.
  • Attribute decoding eg, attribute decoding described in FIGS. 1 to 19
  • RAHT, predictive transform and lifting are the same or similar to those described above in FIG. 4 .
  • the attribute decoder according to the embodiments may perform an operation corresponding to a reverse process of the operation of the attribute encoder 1502 of FIG. 15 .
  • the attribute encoding may be based on at least one of RAHT coding, predictive transform coding, and/or lifting transform coding.
  • a bitstream according to embodiments may include information about an attribute coding type used for attribute encoding (eg, attr_coding_type of FIG. 19 ).
  • the encoder according to the embodiments may perform attribute decoding based on information about the attribute coding type included in the bitstream. That is, the attribute decoder according to the embodiments may perform a RAHT decoding process, an inverse prediction transform process, and/or an inverse lifting transform process based on information about the attribute coding type.
  • RAHT decoding, inverse prediction transform, and/or inverse lifting transform according to embodiments may be a process corresponding to the inverse process of the RAHT coding, predictive transform, and/or lifting transform described above in FIG. 15 .
  • the LOD generator (not shown in this figure) may generate an LOD to perform an inverse prediction transformation process and/or an inverse lifting transformation process.
  • the LOD generator according to embodiments may correspond to the LOD generator of FIG. 15 .
  • the inverse prediction transform unit (not shown in this figure) may calculate a prediction attribute of each point of the point cloud data.
  • the predictive transform unit according to embodiments may correspond to the predictive transform unit of FIG. 15 .
  • An inverse lifting transformation unit (not shown in this figure) may perform an inverse lifting transformation on an attribute of each point of the points.
  • An inverse lifting transform unit transforms an attribute of a point based on a prediction weight for a point (prediction weight in FIGS. 15 to 19) and a quantization weight for a point (quantization weight in FIGS. 15 to 19) can do.
  • Inverse lifting conversion unit may correspond to the lifting conversion unit of FIG. 15 .
  • the inverse lifting transformation process according to the embodiments may correspond to the reverse process of the lifting transformation process described with reference to FIGS. 15 to 19 .
  • the attribute transform unit according to the embodiments may perform inverse transform on the decoded attribute.
  • the attribute transform unit according to embodiments may perform the same or similar operation as that of the inverse transform unit 11010 of FIG. 11 .
  • the attribute transform unit according to embodiments may perform an operation corresponding to a reverse process of the operation of the attribute transform unit 1501 of FIG. 15 .
  • the point cloud data receiving apparatus outputs (or renders) the final point cloud video by performing attribute decoding (eg, inverse lifting transformation) on attribute information of the point cloud data through the decoder described in FIG. 20 . )can do.
  • attribute decoding eg, inverse lifting transformation
  • 21 is a block diagram illustrating an example of an inverse lifting transformation process according to embodiments.
  • 21 is an example of an inverse lifting transformation process (2100, inverse lifting transformation process of FIG. 20) for an attribute performed by an inverse lifting transformation unit (eg, the inverse lifting transformation unit described in FIG. 20) according to embodiments It is a block diagram showing
  • the inverse lifting transformation process 2100 may include an inverse update process 2101 , an inverse prediction process 2102 , a synthesis process 2103 , and/or a feedback process 2104 .
  • 2101 to 2104 may be performed by one or more elements included in the inverse lifting transformation unit.
  • the attribute decoder according to embodiments may perform 2101 to 2104 only once, or may perform multiple times (eg, N times).
  • the inverse lifting transformation process according to embodiments may be performed in units of attributes corresponding to one point.
  • Data input to the inverse lifting process may include the prediction residual value d_(i, j)) and the fed back attribute s'_(k, j)) described above with reference to FIG. 16 .
  • the inverse lifting transform unit may perform an inverse update process on the input prediction residual value d_(i, j).
  • the inverse update process according to embodiments may correspond to the reverse process of the update process 1603 described above with reference to FIG. 16 . That is, the inverse lifting transform unit may perform an inverse update process on the prediction residual value d_(i, j) based on the quantization weight value (eg, the quantization weight value described above with reference to FIGS. 15 to 20 ). For example, the inverse lifting transform unit may perform an inverse update process by multiplying an input prediction residual value by an inverse of a quantization weight value.
  • the inverse lifting transform unit subtracts the inverse-updated prediction residual value from the input feedback attribute (s'_(k, j)) (eg, the reverse process of Equation 3 of FIG. 16 ) can be done
  • the inverse lifting transform unit may perform an inverse prediction process on a value obtained by subtracting the inverse-updated prediction residual value from the above-described feedbacked attribute (s'_(k, j)).
  • the inverse prediction process according to embodiments may correspond to the reverse process of the prediction process 1602 of FIG. 16 . That is, the inverse lifting converter may perform the inverse prediction process based on the prediction weight (eg, the prediction weight described above with reference to FIGS. 15 to 20 ). For example, the inverse lifting transform unit may perform the inverse prediction process by multiplying a value obtained by subtracting the inverse updated prediction residual value from the fed back attribute by the inverse of the prediction weight.
  • the inverse lifting transform unit according to the embodiments performs a process of adding a result value of the inverse prediction process to the input prediction residual value d_(i, j) (eg, the reverse process of Equation 2 of FIG. 16 ) can do.
  • the inverse lifting transform unit performs a synthesis process 2103 of synthesizing a value obtained by subtracting the inverse updated prediction residual value from the feedback attribute and a value obtained by adding the result value of the inverse prediction process to the input prediction residual value.
  • the synthesis process according to the embodiments may correspond to the reverse process of the split process 1601 of FIG. 16 .
  • Inverse lifting conversion unit may perform a feedback process (2104). It may correspond to the feedback process 1604 described above in FIG. 16 .
  • the feedback process may be performed based on an alpha value (or a first value) and a beta value (or a second value).
  • the feedback process may include a process of multiplying the fed back attribute (s'_(k, j)) by a first value and multiplying the result value of the synthesis process by a value obtained by subtracting the first value from the second value.
  • the inverse lifting transform unit according to the embodiments may output the attributes converted through the 2101 to 2104 described above.
  • the transformed attribute output by the inverse lifting transform unit according to the embodiments corresponds to the same or similar value to the attribute before the lifting transform.
  • 22 is a flowchart of a method for receiving point cloud data according to embodiments.
  • a method of receiving point cloud data may include an arithmetic decoding process 2200 , an inverse quantization process 2201 , an LOD generation process 2202 , and/or an inverse lifting transformation process 2203 .
  • a method for receiving point cloud data according to embodiments may correspond to a reverse process of the method for transmitting point cloud data described with reference to FIG. 17 .
  • the method for receiving point cloud data according to embodiments may further include one or more processes for receiving the point cloud data.
  • the arithmetic decoding process according to the embodiments may include decoding the received attribute bitstream based on arithmetic coding.
  • the arithmetic decoding process according to the embodiments may be performed by the entropy decoder 2001 of FIG. 20 .
  • the arithmetic decoding process according to the embodiments may correspond to the reverse process of the arithmetic encoding process 1704 of FIG. 17 .
  • the inverse quantization process according to embodiments may include performing inverse quantization on the attribute bitstream.
  • the inverse quantization process according to embodiments may be performed by the inverse quantization unit 2002 of FIG. 20 .
  • the inverse quantization process according to embodiments may correspond to the inverse process of the quantization process 1703 of FIG. 17 .
  • the LOD generation process according to the embodiments may generate the LOD by rearranging points for inverse lifting transformation.
  • LODs according to embodiments are the same as or similar to those described with reference to FIGS. 1 to 21 .
  • the LOD generation process according to the embodiments may be performed by the LOD generation unit described above with reference to FIG. 20 .
  • the LOD generation process according to the embodiments may correspond to the LOD generation process 1701 of FIG. 17 .
  • the inverse lifting transformation process according to the embodiments may include a process of performing inverse lifting transformation on an attribute of each point of the points.
  • the inverse lifting transformation process according to embodiments may be performed by the inverse lifting transformation unit of FIG. 20 .
  • the inverse lifting transformation process according to the embodiments may include an inverse update process 2101 , an inverse prediction process 2102 , a synthesis process 2103 , and/or a feedback process 2104 as described above with reference to FIG. 21 . have.
  • the inverse lifting transformation process according to embodiments may correspond to the reverse process of the lifting transformation process of FIG. 16 .
  • the receiving device may convert the received attribute into a value equal to or similar to the attribute before the lifting transformation.
  • the receiving apparatus adjusts latency in attribute decoding according to the point cloud data transmission method described in FIG. 22, and performs attribute decoding (eg, inverse lifting transformation) on attribute information to final point cloud You can output (or render) video.
  • attribute decoding eg, inverse lifting transformation
  • FIG. 23 is an example of a flowchart illustrating a method of transmitting point cloud data according to embodiments.
  • FIG. 23 is a point cloud data transmission method of a point cloud data transmission apparatus (eg, the point cloud data transmission apparatus described in FIGS. 1, 2, 4, 11, 12, and 15) according to embodiments; indicates.
  • the transmitting apparatus according to the embodiments performs the same or similar operation to the encoding operation described with reference to FIGS. 1 to 22 .
  • the point cloud data transmission apparatus may encode the point cloud data ( 2300 ).
  • the apparatus for transmitting point cloud data according to embodiments may transmit a bitstream including encoded point cloud data ( 2301 ).
  • a point cloud data transmission apparatus includes a geometry encoder that encodes a geometry indicating a position of one or more points of point cloud data and an attribute encoder that encodes attributes of one or more points based on the encoded geometry and a transmitter for transmitting a bitstream including the encoded point cloud data.
  • the attribute encoder may include a lifting transform unit that performs a lifting transform on an attribute of each point of the points.
  • the lifting transform unit according to the embodiments is the same as or similar to the lifting transform unit described above in FIGS. 15 to 16 .
  • the lifting transform unit according to embodiments may transform an attribute of a point based on a prediction weight of the point and a quantization weight of the point.
  • a prediction weight according to embodiments may be a value calculated based on a distance between a point and one or more neighboring points of the point, and the quantization weight may be a value calculated based on a prediction weight. Descriptions of prediction weights and quantization weights according to embodiments are the same as or similar to those described with reference to FIGS. 15 to 22 .
  • the bitstream according to the embodiments may include information on whether to perform feedback on the transformed attribute. Information on whether to perform feedback on the transformed attribute according to embodiments is the same as or similar to that described above with reference to FIGS. 16 to 19 .
  • the lifting transform unit may feed back the transformed attribute based on the first value and the second value.
  • Descriptions of the first value and the second value according to the embodiments are the same as or similar to those described above with reference to FIG. 16 .
  • the first value according to the embodiments is a value based on a sampling frequency of the point cloud data and a cut-off frequency of the point cloud data, and the sum of the first value and the second value is 1 may be greater than or equal to Descriptions of the sampling frequency and the cutoff frequency according to the embodiments are the same as or similar to those described with reference to FIGS. 16 to 19 .
  • the first value according to embodiments may be generated based on an exponential function, and the exponential of the exponential function may include a sampling frequency and a cutoff frequency.
  • Information on whether to perform feedback on the transformed attribute included in the bitstream according to the embodiments indicates that the transformed attribute is fed back, and the bitstream includes information about a first value, information about a second value, and a sampling frequency. It may further include information about the cutoff frequency and information about the cutoff frequency. Descriptions of the information on the first value, the information on the second value, the information on the sampling frequency, and the information on the cutoff frequency according to the embodiments are the same as or similar to those described above with reference to FIGS. 16 and 19 .
  • FIG. 24 is an example of a flowchart illustrating a method of receiving point cloud data according to embodiments.
  • FIG. 24 illustrates a point cloud data receiving method of a point cloud data receiving apparatus (eg, the point cloud data receiving apparatus described with reference to FIGS. 1, 2, 11, 13, and 20) according to embodiments.
  • the reception apparatus according to the embodiments performs the same or similar operation to the decoding operation described with reference to FIGS. 1 to 22 .
  • the device for receiving point cloud data may receive a bitstream including point cloud data ( 2700 ). Bitstreams according to embodiments are the same as or similar to those described with reference to FIGS. 18 to 19 .
  • the point cloud data receiving apparatus may decode the point cloud data ( 2701 ).
  • a point cloud data receiving apparatus includes a geometry decoder for decoding a geometry indicating a position of one or more points of point cloud data and an attribute decoder for decoding an attribute of one or more points based on the decoded geometry include
  • the attribute decoder may include an inverse lifting transform unit that performs an inverse lifting transform on an attribute of each point of the points. Descriptions of the inverse lifting conversion unit according to the embodiments are the same as or similar to those described above with reference to FIGS. 20 to 22 .
  • the inverse lifting transform unit according to embodiments may transform an attribute of a point based on a prediction weight of the point and a quantization weight of the point. A description of a process of the inverse lifting conversion unit converting the attribute of a point according to embodiments is the same as or similar to that described above with reference to FIG. 21 .
  • the prediction weight according to embodiments may be a value calculated based on a distance between a point and one or more neighboring points of the point, and the quantization weight may be a value calculated based on the prediction weight. Descriptions of prediction weights and quantization weights according to embodiments are the same as or similar to those described with reference to FIGS. 15 to 22 .
  • the bitstream according to the embodiments may include information on whether to perform feedback on the transformed attribute. A description of information on whether to perform feedback on a transformed attribute according to embodiments is the same as or similar to that described above with reference to FIGS. 16 to 19 .
  • the bitstream includes information about a first value, information about a second value, Information on the sampling frequency and information on the cutoff frequency may be further included. Descriptions of the information on the first value, the information on the second value, the information on the sampling frequency, and the information on the cutoff frequency according to the embodiments are the same as or similar to those described above with reference to FIG. 19 .
  • the inverse lifting transform unit may feed back the transformed attribute based on the first value and the second value.
  • the description of the feedback process according to the embodiments is the same as or similar to that described above with reference to FIG. 21 .
  • a first value according to embodiments is a value based on a sampling frequency of point cloud data and a cut-off frequency of point cloud data, and the sum of the first value and the second value is greater than 1. may be greater than or equal to
  • the first value according to embodiments may be generated based on an exponential function, and the exponential of the exponential function may include a sampling frequency and a cutoff frequency. Descriptions of the first value, the second value, the sampling frequency, and the cutoff frequency according to the embodiments are the same as or similar to those described above with reference to FIGS. 16 to 22 .
  • Various components of the apparatus of the embodiments may be implemented by hardware, software, firmware, or a combination thereof.
  • Various components of the embodiments may be implemented with one chip, for example, one hardware circuit.
  • the components according to the embodiments may be implemented with separate chips.
  • at least one or more of the components of the device according to the embodiments may be composed of one or more processors capable of executing one or more programs, and the one or more programs may be implemented Any one or more of the operations/methods according to the examples may be performed or may include instructions for performing the operations/methods.
  • Executable instructions for performing the method/acts of the apparatus according to the embodiments may be stored in non-transitory CRM or other computer program products configured for execution by one or more processors, or one or more may be stored in temporary CRM or other computer program products configured for execution by processors.
  • the memory according to the embodiments may be used as a concept including not only a volatile memory (eg, RAM, etc.) but also a non-volatile memory, a flash memory, a PROM, and the like. Also, it may be implemented in the form of a carrier wave, such as transmission through the Internet.
  • the processor-readable recording medium is distributed in a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.
  • first, second, etc. may be used to describe various elements of the embodiments. However, interpretation of various components according to the embodiments should not be limited by the above terms. These terms are only used to distinguish one component from another. it is only For example, the first user input signal may be referred to as a second user input signal. Similarly, the second user input signal may be referred to as a first user input signal. Use of these terms should be interpreted as not departing from the scope of the various embodiments. Although both the first user input signal and the second user input signal are user input signals, they do not mean the same user input signals unless the context clearly indicates otherwise.
  • the embodiments may be wholly or partially applied to a point cloud data transmission/reception device and system.
  • Embodiments may include variations/modifications without departing from the scope of the claims and the like.

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Abstract

Selon des modes de réalisation, un procédé de traitement de données de nuage de points peut coder et transmettre des données de nuage de points. Selon des modes de réalisation de la présente invention, le procédé de traitement de données en nuage de points peut comprendre la réception et le décodage de données de nuage de points.
PCT/KR2021/000368 2020-03-23 2021-01-12 Appareil et procédé de traitement de données de nuage de points WO2021194065A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023179705A1 (fr) * 2022-03-25 2023-09-28 维沃移动通信有限公司 Procédés et appareils de codage et décodage, et dispositifs

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180079314A (ko) * 2015-09-29 2018-07-10 엘지전자 주식회사 그래프 기반 리프팅 변환을 이용하여 비디오 신호를 인코딩, 디코딩하는 방법 및 장치
US20190080483A1 (en) * 2017-09-14 2019-03-14 Apple Inc. Point Cloud Compression
US20200090373A1 (en) * 2018-09-14 2020-03-19 Sony Corporation Adaptive subband coding for lifting transform

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20180079314A (ko) * 2015-09-29 2018-07-10 엘지전자 주식회사 그래프 기반 리프팅 변환을 이용하여 비디오 신호를 인코딩, 디코딩하는 방법 및 장치
US20190080483A1 (en) * 2017-09-14 2019-03-14 Apple Inc. Point Cloud Compression
US20200090373A1 (en) * 2018-09-14 2020-03-19 Sony Corporation Adaptive subband coding for lifting transform

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
KATHARIYA BIRENDRA; ZAKHARCHENKO VLADYSLAV; LI ZHU; CHEN JIANLE: "Level-of-Detail Generation Using Binary-Tree for Lifting Scheme in LiDAR Point Cloud Attributes Coding", 2019 DATA COMPRESSION CONFERENCE (DCC), 26 March 2019 (2019-03-26), pages 580 - 580, XP033548522, DOI: 10.1109/DCC.2019.00092 *
KHALED MAMMOU, PHILIP A. CHOU, DAVID FLYNN, MAJA KRIVOKUĆA, OHJI NAKAGAMI , TOSHIYASU SUGIO: "G-PCC codec description v2", ISO/IEC JTC1/SC29/WG11 N18189, no. N18189, 1 January 2019 (2019-01-01), pages 1 - 39, XP055686871 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023179705A1 (fr) * 2022-03-25 2023-09-28 维沃移动通信有限公司 Procédés et appareils de codage et décodage, et dispositifs

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