[go: up one dir, main page]

WO2020189891A1 - Appareil de transmission de données de nuage de points, procédé de transmission de données de nuage de points, appareil de réception de données de nuage de points, et procédé de réception de données de nuage de points - Google Patents

Appareil de transmission de données de nuage de points, procédé de transmission de données de nuage de points, appareil de réception de données de nuage de points, et procédé de réception de données de nuage de points Download PDF

Info

Publication number
WO2020189891A1
WO2020189891A1 PCT/KR2020/001615 KR2020001615W WO2020189891A1 WO 2020189891 A1 WO2020189891 A1 WO 2020189891A1 KR 2020001615 W KR2020001615 W KR 2020001615W WO 2020189891 A1 WO2020189891 A1 WO 2020189891A1
Authority
WO
WIPO (PCT)
Prior art keywords
point cloud
cloud data
information
unit
geometry
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2020/001615
Other languages
English (en)
Korean (ko)
Inventor
오세진
허혜정
심동규
이종석
최한솔
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
LG Electronics Inc
Original Assignee
LG Electronics Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by LG Electronics Inc filed Critical LG Electronics Inc
Publication of WO2020189891A1 publication Critical patent/WO2020189891A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/90—Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums

Definitions

  • the embodiments are directed to a method and apparatus for processing point cloud content.
  • 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 media consisting of three dimensions, and provides various services such as VR (Virtual Reality, Virtual Reality), AR (Augmented Reality, Augmented Reality), MR (Mixed Reality, Mixed Reality), and autonomous driving services. Used to provide. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, a method for efficiently processing a vast 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: obtaining point cloud data; Encoding the point cloud data; And/or transmitting point cloud data; It may include.
  • a method of receiving point cloud data includes: receiving point cloud data; Decoding the point cloud data; And rendering the point cloud data. It may include.
  • 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 point cloud service of high quality.
  • the apparatus and method according to the embodiments may provide point cloud content for providing general-purpose services such as VR services and autonomous driving services.
  • 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 shows 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 reception device according to embodiments.
  • FIG. 14 illustrates an architecture for G-PCC-based point cloud content streaming according to embodiments.
  • 15 shows an example of a transmission device according to embodiments.
  • FIG. 16 shows an example of a receiving device according to embodiments.
  • FIG. 17 shows an example of a structure capable of interworking with a method/device for transmitting and receiving point cloud data according to embodiments.
  • FIG. 18 shows an example of a space of points constituting point cloud content according to embodiments.
  • FIG. 19 shows an example of a point cloud encoder according to embodiments.
  • FIG. 20 shows an example of a geometric information encoder according to embodiments.
  • 21 illustrates a bounding box of geometric information of point cloud data according to embodiments.
  • FIG. 22 shows an example of an operation of a space dividing unit according to embodiments.
  • 23 is a diagram illustrating an example of performing octtree division information and/or derivation information of geometric information of point cloud data according to embodiments.
  • FIG. 24 shows an example of a structure of point cloud data according to embodiments.
  • 25 shows an example of a process of inverse quantization of geometric information according to embodiments.
  • 26 illustrates an exemplary embodiment of inverse quantization of geometric information according to embodiments.
  • FIG. 27 shows an example of a point cloud decoder according to embodiments.
  • 29 shows an example of occupancy derivation according to embodiments.
  • FIG. 30 shows signaling information related to a geometry node according to embodiments.
  • 31 illustrates a method of transmitting point cloud data according to embodiments.
  • 32 illustrates 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 illustrated in FIG. 1 may include a transmission device 10000 and a reception device 10004.
  • the transmission device 10000 and the reception device 10004 are capable of wired or wireless communication to transmit and receive point cloud data.
  • the transmission device 10000 may secure, process, and transmit point cloud video (or point cloud content).
  • the transmission device 10000 is a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or server. And the like.
  • the transmission device 10000 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, Robots, vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (IoT) devices, AI devices/servers, etc. may be included.
  • 5G NR New RAT
  • LTE Long Term Evolution
  • the transmission device 10000 includes a point cloud video acquisition unit (Point Cloud Video Acquisition, 10001), a point cloud video encoder (Point Cloud Video Encoder, 10002) and/or a transmitter (Transmitter (or Communication module), 10003). Include)
  • the point cloud video acquisition unit 10001 acquires a point cloud video through a process such as capture, synthesis, or generation.
  • the point cloud video is point cloud content expressed as a point cloud, which is a set of points located in a three-dimensional space, and may be referred to as point cloud video data.
  • 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 secured 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
  • point cloud compression coding according to the embodiments is not limited to the above-described embodiments.
  • 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 (for example, 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 an encapsulation module) that performs an encapsulation operation.
  • the encapsulation unit may be included in the transmitter 10003.
  • a 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 perform wired/wireless communication with the reception device 10004 (or a receiver 10005) through a network such as 4G, 5G, or 6G.
  • the transmitter 10003 may perform necessary data processing operations according to a network system (for example, 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 reception device 10004 includes a receiver 10005, a point cloud video decoder 10006, and/or a renderer 10007.
  • the receiving device 10004 uses a wireless access technology (eg, 5G NR (New RAT), LTE (Long Term Evolution)) to communicate with a base station and/or other wireless devices, a robot , Vehicles, AR/VR/XR devices, portable devices, home appliances, Internet of Thing (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 necessary data processing operations according to a network system (for example, a communication network system such as 4G, 5G, or 6G).
  • the receiver 10005 may decapsulate the received file/segment and output a bitstream.
  • 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 the encoding method (for example, 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 point cloud content.
  • the display is not included in the renderer 10007 and may be implemented as a separate device or component.
  • the feedback information is information for reflecting an interaction ratio with a user who consumes 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 the content sending side (for example, the transmission device 10000) and/or a service provider.
  • the feedback information may be used not only in the transmitting device 10000 but also in the receiving device 10004, and may not be provided.
  • Head orientation information is information on a position, direction, angle, and movement of a user's head.
  • the receiving device 10004 may calculate viewport information based on the head orientation information.
  • the viewport information is information on the area of the point cloud video that the user is viewing.
  • a viewpoint is a point at which the user is watching a point cloud video, and may mean a center point of a 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 receiving 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 performs a gaze analysis and the like to check the point cloud consumption method of the user, the point cloud video area that the user gazes, and the gaze time.
  • the receiving device 10004 may transmit feedback information including the result of 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 transmitting feedback information secured by the renderer 10007.
  • the point cloud content providing system may process (encode/decode) point cloud data based on feedback information.
  • the point cloud video data decoder 10006 may perform a decoding operation based on the feedback information.
  • the receiving device 10004 may transmit feedback information to the transmitting device 10000.
  • the transmission device 10000 (or the point cloud video data encoder 10002) may perform an encoding operation based on feedback information. Therefore, the point cloud content providing system does not process (encode/decode) all point cloud data, but efficiently processes necessary data (e.g., point cloud data corresponding to the user's head position) based on feedback information. Point cloud content can be provided to users.
  • the transmission device 10000 may be referred to as an encoder, a transmission device, a transmitter, and the like
  • the reception device 10004 may be referred to as a decoder, a reception 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.
  • Elements of the point cloud content providing system shown in FIG. 1 may be implemented by hardware, software, 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
  • a point cloud content providing system may acquire a point cloud video (20000).
  • the point cloud video is expressed as a point cloud belonging to a coordinate system representing a three-dimensional space.
  • a point cloud video may include a Ply (Polygon File format or the Stanford Triangle format) file.
  • Ply files contain point cloud data such as the geometry and/or attributes of the point.
  • the geometry includes the positions of the 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 composed of XYZ axes).
  • Attributes include 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 attributes).
  • one point may have an attribute of one color, or two attributes of a color and reflectance.
  • geometry may be referred to as positions, geometry information, geometry data, and the like, and attributes may be referred to as attributes, attribute information, attribute data, and the like.
  • the point cloud content providing system (for example, the point cloud transmission device 10000 or the point cloud video acquisition unit 10001) provides points from information related to the acquisition process of the point cloud video (eg, depth information, color information, etc.). Cloud data can be secured.
  • the point cloud content providing system may encode 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 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 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 may receive a bitstream including encoded point cloud data.
  • the point cloud content providing system may demultiplex the bitstream.
  • the point cloud content providing system can decode the encoded point cloud data (e.g., geometry bitstream, 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) can 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 (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore positions (geometry) of points by decoding a geometry bitstream.
  • the point cloud content providing system may restore the attributes of points by decoding an attribute bitstream based on the restored geometry.
  • the point cloud content providing system (for example, the receiving device 10004 or the point cloud video decoder 10005) may restore the point cloud video based on the decoded attributes and positions according to the restored geometry.
  • the point cloud content providing system may render the decoded point cloud data (20004 ).
  • the point cloud content providing system may render geometry and attributes decoded through a decoding process according to a rendering method according to various rendering methods. Points of the point cloud content may be rendered as a vertex having a certain thickness, a cube having a specific minimum size centered on the vertex position, or a circle centered on 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 may secure feedback information (20005).
  • the point cloud content providing system may encode and/or decode point cloud data based on feedback information. Since the operation of the system for providing feedback information and point cloud content according to the embodiments is the same as the feedback information and operation described in FIG. 1, a detailed description will be omitted.
  • FIG 3 shows an example of a point cloud video capture process according to embodiments.
  • FIGS. 1 to 2 shows an example of a point cloud video capture process in the point cloud content providing system described in FIGS. 1 to 2.
  • the point cloud content is an object located in various three-dimensional spaces (for example, a three-dimensional space representing a real environment, a three-dimensional space representing a virtual environment, etc.) and/or a point cloud video (images and/or Videos). Therefore, the point cloud content providing system according to the embodiments includes one or more cameras (eg, an infrared camera capable of securing depth information, color information corresponding to the depth information) to generate the point cloud content. You can capture a point cloud video using an RGB camera that can extract the image), a projector (for example, an infrared pattern projector to secure depth information), and LiDAR.
  • cameras eg, an infrared camera capable of securing depth information, color information corresponding to the depth information
  • a projector for example, an infrared pattern projector to secure depth information
  • LiDAR LiDAR
  • the point cloud content providing system may obtain point cloud data by extracting a shape of a geometry composed of points in a 3D space from depth information, and extracting an attribute of each point from color information.
  • An image and/or an image according to the embodiments may be captured based on at least one or more of an inward-facing method and an outward-facing method.
  • the left side of Fig. 3 shows an inword-facing scheme.
  • the inword-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding a central object capture a central object.
  • the in-word-facing method provides point cloud content that provides users with 360-degree images of key objects (e.g., provides users with 360-degree images of objects (e.g., key objects such as characters, players, objects, actors, etc.) VR/AR content).
  • the outward-facing method refers to a method in which one or more cameras (or camera sensors) located surrounding the central object capture the environment of the central object other than the central object.
  • the outward-pacing method may be used to generate point cloud content (for example, content representing an external environment that may be provided to a user of a self-driving vehicle) to provide an environment that appears from a user's point of view.
  • the point cloud content may be generated based on the capture operation of one or more cameras.
  • the point cloud content providing system may calibrate one or more cameras to set a global coordinate system before the capture operation.
  • the point cloud content providing system may generate point cloud content by synthesizing an image and/or image captured by the above-described capture method with an arbitrary image and/or image.
  • the point cloud content providing system may not perform the capture operation described in FIG. 3 when generating point cloud content representing a virtual space.
  • the point cloud content providing system may perform post-processing on the captured image and/or image. In other words, the point cloud content providing system removes an unwanted area (e.g., background), recognizes the space where captured images and/or images are connected, and performs an operation to fill in a spatial hole if there is. I 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 acquired from each camera.
  • the point cloud content providing system may perform a coordinate system transformation of points based on the position coordinates of each camera. Accordingly, the point cloud content providing system may generate content representing a wide range, or 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 uses point cloud data (for example, positions and/or positions of points) to adjust the quality of the point cloud content (for example, lossless-lossless, loss-lossy, near-lossless) according to network conditions or applications. Attributes) and perform an encoding operation.
  • point cloud data for example, positions and/or positions of points
  • the quality of the point cloud content for example, lossless-lossless, loss-lossy, near-lossless
  • Attributes perform an encoding operation.
  • the point cloud content providing system may not be able to stream the content in real time. Therefore, the point cloud content providing system can reconstruct the point cloud content based on the maximum target bitrate in order to provide it according to the network environment.
  • the point cloud encoder may perform geometry encoding and attribute encoding. Geometry encoding is performed before attribute encoding.
  • Point cloud encoders include a coordinate system transform unit (Transformation Coordinates, 40000), a quantization unit (Quantize and Remove Points (Voxelize), 40001), an octree analysis unit (Analyze Octree, 40002), and a surface aproximation analysis unit ( Analyze Surface Approximation, 40003), Arithmetic Encode (40004), Reconstruct Geometry (40005), Transform Colors (40006), Transfer Attributes (40007), RAHT Transformation A unit 40008, an LOD generation unit (Generated LOD) 40009, a lifting transform unit (Lifting) 40010, a coefficient quantization unit (Quantize Coefficients, 40011), and/or an Arithmetic Encode (40012).
  • a coordinate system transform unit Transformation Coordinates, 40000
  • a quantization unit Quantization and Remove Points (Voxelize)
  • An octree analysis unit Analyze Octree, 40002
  • the coordinate system transform unit 40000, the quantization unit 40001, the octree analysis unit 40002, the surface aproximation 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 trisoup geometry encoding are applied selectively or in combination. Also, geometry encoding is not limited to the above example.
  • the coordinate system conversion unit 40000 receives positions and converts them into a coordinate system.
  • positions may be converted into position information in a three-dimensional space (eg, a three-dimensional space represented by an XYZ coordinate system).
  • the location information of the 3D space according to embodiments may be referred to as geometry information.
  • the quantization unit 40001 quantizes geometry. For example, the quantization unit 40001 may quantize points based on the minimum position values of all points (eg, 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 quantum scale value, and then performs a quantization operation to find the nearest integer value by performing a rounding or a rounding. Thus, one or more points may have the same quantized position (or position value). The quantization unit 40001 according to embodiments performs voxelization based on the quantized positions to reconstruct the quantized points.
  • the quantization unit 40001 performs voxelization based on the quantized positions to reconstruct the quantized points.
  • the minimum unit including the 2D image/video information is a pixel, and points of the point cloud content (or 3D point cloud video) according to the embodiments may be included in one or more voxels.
  • Voxel is a combination of volume and pixel
  • the quantization unit 40001 may match groups of points in a 3D space with voxels.
  • one voxel may include only one point.
  • one voxel may include one or more points.
  • a position of a center point (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 analysis unit 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 an octal tree structure.
  • the surface aproxiation analysis unit 40003 may analyze and approximate the octree.
  • the octree analysis and approximation according to the embodiments 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.
  • a geometry bitstream is generated.
  • Color conversion unit 40006, attribute conversion unit 40007, RAHT conversion unit 40008, LOD generation unit 40009, lifting conversion unit 40010, coefficient quantization unit 40011 and/or Arismatic encoder 40012 Performs attribute encoding.
  • one point may have one or more attributes. Attribute encoding according to embodiments is applied equally 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 includes color transform coding, attribute transform coding, Region Adaptive Hierarchial 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)) coding may be included.
  • RAHT Region Adaptive Hierarchial Transform
  • Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding Interpolaration-based hierarchical nearest-neighbor prediction-Prediction Transform coding
  • interpolation-based hierarchical nearest -Neighbor prediction with an update/lifting step (Lifting Transform)) coding may be included.
  • the aforementioned RAHT coding, predictive transform coding, and lifting transform coding may be selectively used, or a combination of one or more codings may be used.
  • attribute encoding according to embodiments is not limited to the above-de
  • 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 conversion unit 40006 according to the embodiments may be selectively applied according to color values included in 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 reconstructed geometry (or reconstructed geometry).
  • the attribute conversion unit 40007 performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed. As described above, since attributes are dependent on 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 the point of the position based on the position value of the point included in the voxel. As described above, when a position of a center point of a corresponding voxel is set based on positions of one or more points included in one voxel, the attribute conversion unit 40007 converts attributes of one or more points. When tri-soup geometry encoding is performed, the attribute conversion unit 40007 may convert attributes based on trisoup geometry encoding.
  • the attribute conversion unit 40007 is an average value of attributes or attribute values (for example, the color of each point or reflectance) of points neighboring within a specific position/radius from the position (or position value) of the center point of each voxel. Attribute conversion can be performed by calculating.
  • the attribute conversion unit 40007 may apply a weight according to a distance from a central point to each point when calculating an average value. Thus, each voxel has a position and a calculated attribute (or attribute value).
  • the attribute conversion 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 a K-D tree or a Molton code.
  • the K-D tree is a binary search tree and supports a data structure that can manage points based on location so that the Nearest Neighbor Search (NNS) can be quickly performed.
  • the Molton code represents a coordinate value (for example, (x, y, z)) representing a 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 conversion unit 40007 may sort points based on a Morton code value and perform a shortest neighbor search (NNS) through a depth-first traversal process. After the attribute transformation operation, when the shortest neighbor search (NNS) is required in another transformation 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 conversion unit 40008 performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT conversion unit 40008 may predict attribute information of a node at a higher level of the octree based on attribute information associated with a node at a lower level of the octree.
  • the LOD generation unit 40009 generates a level of detail (LOD) to perform predictive transform coding.
  • LOD level of detail
  • the LOD according to the embodiments is a degree representing the detail of the point cloud content, and a smaller LOD value indicates that the detail of the point cloud content decreases, and a larger LOD value indicates that the detail of the point cloud content is high. Points can be classified according to LOD.
  • the lifting transform unit 40010 performs lifting transform coding that transforms attributes of a point cloud based on weights. As described above, the lifting transform coding can be selectively applied.
  • the coefficient quantization unit 40011 quantizes attribute-coded attributes based on coefficients.
  • Arismatic encoder 40012 encodes quantized attributes based on Arismatic coding.
  • the elements of the point cloud encoder of FIG. 4 are not shown in the drawing, but 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 one or more of the operations and/or functions of the elements of the point cloud encoder of FIG. 4 described above. Further, 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 encoder of FIG. 4.
  • One or more memories according to embodiments may include high speed random access memory, and non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid state Memory devices (solid-state memory devices, etc.).
  • FIG. 5 shows an example of a voxel according to embodiments.
  • voxels located in a three-dimensional space represented by a coordinate system composed of three axes of the X-axis, Y-axis, and Z-axis.
  • a point cloud encoder eg, quantization unit 40001
  • voxel 5 is an octree structure recursively subdividing a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ) Shows an example of a voxel generated through.
  • One voxel includes at least one or more points.
  • the voxel can estimate spatial coordinates from the positional relationship with the voxel group.
  • voxels have attributes (color or reflectance, etc.) like pixels of a 2D image/video. A detailed description of the voxel is the same as that described with reference to FIG. 4 and thus is omitted.
  • FIG. 6 shows an example of an octree and an occupancy code according to embodiments.
  • a point cloud content providing system (point cloud video encoder 10002) or a point cloud encoder (for example, octree analysis unit 40002) efficiently manages the area and/or position of the voxel.
  • octree geometry coding (or octree coding) based on an octree structure is performed.
  • FIG. 6 shows an octree structure.
  • the three-dimensional space of the point cloud content according to the embodiments is expressed by axes of a coordinate system (eg, X-axis, Y-axis, Z-axis).
  • the octree structure is created by recursive subdividing of a cubical axis-aligned bounding box defined by two poles (0,0,0) and (2 d , 2 d , 2 d ). . 2d may be set to 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 d value 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 6 faces.
  • each of the eight spaces is divided again based on the axes of the coordinate system (eg, X axis, Y axis, Z axis).
  • axes of the coordinate system e.g, X axis, Y axis, Z axis.
  • each space is further divided into eight smaller spaces.
  • the divided small space is also represented as a cube with 6 faces. This division method is applied until a leaf node of an octree becomes a voxel.
  • the lower part of FIG. 6 shows the octree's ocupancy code.
  • the octree's ocupancy code is generated to indicate whether each of the eight divided spaces generated by dividing one space includes at least one point. Therefore, one Okufanshi code is represented by 8 child nodes. Each child node represents the occupancy of the divided space, and the child node has a value of 1 bit. Therefore, the Ocufanshi 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 node has a value of 1. If the point is not included in the space corresponding to the child node (empty), the node has a value of 0. Since the ocupancy code shown in FIG.
  • a point cloud encoder (for example, the Arismatic encoder 40004) according to embodiments may entropy encode an ocupancy code.
  • the point cloud encoder can intra/inter code the ocupancy code.
  • the reception device (for example, the reception device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs an octree based on an ocupancy code.
  • a point cloud encoder may perform voxelization and octree coding to store positions of points.
  • 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 over the entire 3D space. For example, if there are almost no points in a specific area, it is not necessary to perform voxelization to the corresponding area.
  • the point cloud encoder does not perform voxelization for the above-described specific region (or nodes other than the leaf nodes of the octree), but directly codes the positions of points included in the specific region. ) Can be performed. Coordinates of a direct coding point according to embodiments are referred to as a direct coding mode (DCM).
  • the point cloud encoder according to embodiments may perform trisoup geometry encoding in which positions of points within a specific region (or node) are reconstructed based on voxels based on a surface model. Trisoup geometry encoding is a geometry encoding that expresses the representation of an object as a series of triangle meshes.
  • Direct coding and trisoup geometry encoding may be selectively performed.
  • direct coding and trisoup geometry encoding according to embodiments may be performed in combination with octree geometry coding (or octree coding).
  • the option to use direct mode to apply direct coding must be activated, and the node to which direct coding is applied is not a leaf node, but below the threshold within a specific node. There must be points of. In addition, the number of all points subject to direct coding must not exceed a preset limit.
  • the point cloud encoder (or the arithmetic encoder 40004) according to the embodiments may entropy-code the positions (or position values) of the points.
  • the point cloud encoder determines a specific level of the octree (if the level is less than the depth d of the octree), and from that level, the node Trisoup geometry encoding that reconstructs the position of a point in the region based on voxels can be performed (tri-soup mode).
  • a point cloud encoder may designate a level to which trisoup geometry encoding is applied. For example, if the specified level is equal to the depth of the octree, the point cloud encoder does not operate in the try-soup mode.
  • the point cloud encoder may operate in the try-soup mode only when the specified level is less than the depth value of the octree.
  • a three-dimensional cube area of nodes of a designated level according to the embodiments is referred to as a block.
  • One block may include one or more voxels.
  • the block or voxel may correspond to a brick.
  • the geometry is represented by a surface.
  • the surface according to embodiments may intersect each edge (edge) of the block at most once.
  • one block has 12 edges, there are at least 12 intersection points within one block. Each intersection is called a vertex (vertex, or vertex).
  • 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 occupied voxel refers to a voxel including a point. The position of the vertex detected along the edge is the average position along the edge of all voxels 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 of the edge (x, y, z), the direction vector of the edge ( ⁇ x, ⁇ y, ⁇ z), and vertex position values (relative position values within the edge). I can.
  • the point cloud encoder e.g., the geometry reconstruction unit 40005
  • the point cloud encoder performs a triangle reconstruction, up-sampling, and voxelization process. By doing so, you can create reconstructed geometry (reconstructed geometry).
  • the vertices located at the edge of the block determine the surface that passes through the block.
  • the surface according to the 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 calculate the squared values of the values subtracted from each vertex value by subtracting the center value, and calculate the sum of all the values.
  • each vertex is projected on the x-axis based on the center of the block, and projected on the (y, z) plane.
  • the projected value on the (y, z) plane is (ai, bi)
  • ⁇ is obtained through atan2(bi, ai)
  • vertices are aligned based on the ⁇ value.
  • the table below shows a combination of vertices for generating a triangle according to the number of vertices. Vertices are ordered from 1 to n.
  • the table below shows that for four vertices, two triangles may be formed according to a combination of vertices.
  • the first triangle may consist of 1st, 2nd, and 3rd vertices among the aligned vertices
  • the second triangle may consist of 3rd, 4th, and 1st vertices among the aligned vertices.
  • the upsampling process is performed to voxelize by adding points in the middle along the edge of the triangle. Additional points are created based on the upsampling factor and the width of the block. The additional point is called a refined vertice.
  • the point cloud encoder may voxelize refined vertices. In addition, 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.
  • a point cloud content providing system or a point cloud encoder directly converts the Ocufanshi code.
  • Entropy coding is possible.
  • the point cloud content providing system or point cloud encoder performs entropy encoding (intra encoding) based on the ocupancy code of the current node and the ocupancy of neighboring nodes, or entropy encoding (inter encoding) based on the ocupancy code of the previous frame. ) Can be performed.
  • a frame according to embodiments means a set of point cloud videos generated at the same time.
  • the compression efficiency of intra-encoding/inter-encoding may vary depending on the number of referenced neighbor nodes. The larger the bit, the more complicated it is, but it can be skewed to one side, increasing the compression efficiency. For example, if you have a 3-bit context, you have to code in 8 ways. The divided coding part affects the complexity of the implementation. Therefore, it is necessary to match the appropriate level of compression efficiency and complexity.
  • a point cloud encoder determines occupancy of neighboring nodes of each node of an octree and obtains a value of a neighbor pattern.
  • the neighboring node pattern is used to infer the occupancy pattern of the corresponding node.
  • the left side of FIG. 7 shows a cube corresponding to a node (centered cube) and six cubes (neighbor nodes) that share at least one surface with the cube. Nodes shown in the figure are nodes of the same depth (depth). Numbers shown in the figure indicate 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 neighboring node pattern values.
  • the neighbor node pattern value is the sum of values multiplied by weights of the occupied neighbor nodes (neighbor nodes having points). Therefore, the neighbor node pattern value has a value from 0 to 63. When the neighbor node pattern value is 0, it indicates that no node (occupied node) has a point among neighboring nodes of the corresponding node. If the neighboring node pattern value is 63, it indicates that all neighboring nodes are occupied nodes. As shown in the figure, since neighboring nodes to which weights 1, 2, 4, and 8 are assigned are occupied 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 neighboring node pattern value (for example, if the neighboring node pattern value is 63, 64 codings are performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing a neighbor node pattern value (for example, based on a table changing 64 to 10 or 6).
  • the encoded geometry is reconstructed (decompressed) before attribute encoding is performed.
  • the geometry reconstruction operation may include changing the placement 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, voxelization, and the attribute is dependent on geometry, so the attribute encoding is performed based on the reconstructed geometry.
  • the point cloud encoder may reorganize points for each LOD.
  • the figure shows point cloud content corresponding to the LOD.
  • the left side of the figure shows the original point cloud content.
  • the second figure from the left of the figure shows the distribution of the lowest LOD points, and the rightmost figure in the figure shows the distribution of the highest LOD points. 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 spacing (or distance) between points becomes shorter.
  • a point cloud content providing system or a point cloud encoder (for example, a point cloud video encoder 10002, a point cloud encoder in FIG. 4, or an LOD generator 40009) generates an LOD. can do.
  • the LOD is generated by reorganizing the points into a set of refinement levels according to a set LOD distance value (or a set of Euclidean distance).
  • the LOD generation process is performed in the point cloud decoder as well as the point cloud encoder.
  • 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 represents the order of points P0 to P9 before LOD generation.
  • the LOD based order of FIG. 9 represents the order of points according to 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 selectively or combine predictive transform coding, lifting transform coding, and RAHT transform coding.
  • the point cloud encoder may generate a predictor for points and perform predictive transform coding to set a predicted attribute (or predicted attribute value) of each point. That is, N predictors may be generated for N points.
  • the predicted attribute (or attribute value) is a weight calculated based on the distance to each neighboring point to the attributes (or attribute values, for example, color, reflectance, etc.) of neighboring points set in the predictor of each point. It is set as the average value multiplied by (or weight value).
  • a point cloud encoder e.g., the coefficient quantization unit 40011
  • the quantization process is as shown in the following table.
  • the point cloud encoder (for example, the arithmetic encoder 40012) according to the embodiments may entropy-code the quantized and dequantized residual values as described above when there are points adjacent to the predictors of each point.
  • the point cloud encoder according to embodiments (for example, the arithmetic encoder 40012) may entropy-code attributes of the corresponding point without performing the above-described process if there are no points adjacent to 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 calculated LOD to the predictor, registers neighboring points, and increases 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 above-described predictive transform coding, but differs in that a weight is accumulated and applied to an attribute value.
  • a process of cumulatively applying a weight to an attribute value according to embodiments is as follows.
  • the weights calculated by additionally multiplying the weights calculated for all predictors by the weights stored in the QW corresponding to the predictor indexes are cumulatively added to the update weight array by the indexes of neighboring nodes.
  • the value obtained by multiplying the calculated weight by the attribute value of the index of the neighboring node is accumulated and summed.
  • the predicted attribute value is calculated by additionally multiplying the attribute value updated through the lift update process by the weight updated through the lift prediction process (stored in QW).
  • a point cloud encoder for example, the coefficient quantization unit 40011
  • the point cloud encoder for example, the Arismatic encoder 40012
  • the point cloud encoder (for example, the RAHT transform unit 40008) according to the embodiments may perform RAHT transform coding that estimates the attributes of higher-level nodes by using an attribute associated with a node at a lower level of the octree.
  • RAHT transform coding is an example of attribute intra coding through octree backward scan.
  • the point cloud encoder according to the embodiments scans from voxels to the entire area, and repeats the merging process up to the root node while merging the voxels into larger blocks in each step.
  • the merging process according to the embodiments is performed only for an occupied node.
  • the merging process is not performed for the empty node, and the merging process is performed for the node immediately above the empty node.
  • FIG. 10 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder illustrated in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1, and may perform the same or similar operation as that 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 performs geometry decoding on the geometry bitstream and outputs decoded geometry.
  • the attribute decoder outputs decoded attributes by performing attribute decoding on the basis of the decoded geometry and the attribute bitstream.
  • the decoded geometry and decoded attributes are used to reconstruct the point cloud content.
  • FIG. 11 shows an example of a point cloud decoder according to embodiments.
  • the point cloud decoder illustrated in FIG. 11 is an example of the point cloud decoder described in FIG. 10, and may perform a decoding operation that is a reverse process of the encoding operation of the point cloud encoder described in FIGS. 1 to 9.
  • the point cloud decoder may perform geometry decoding and attribute decoding. Geometry decoding is performed prior to attribute decoding.
  • the point cloud decoder includes an arithmetic decoder (11000), an octree synthesis unit (synthesize octree, 11001), a surface optimization synthesis unit (synthesize surface approximation, 11002), and a geometry reconstruction unit (reconstruct geometry). , 11003), inverse transform coordinates (11004), arithmetic decode (11005), inverse quantize (11006), RAHT transform unit (11007), LOD generator (generate LOD, 11008) ), Inverse lifting (11009), and/or inverse transform colors (11010).
  • the arithmetic decoder 11000, the octree synthesis unit 11001, the surface opoxidation synthesis unit 11002, the geometry reconstruction unit 11003, and the coordinate system inverse transform unit 11004 may perform geometry decoding.
  • Geometry decoding according to embodiments may include direct coding and trisoup geometry decoding. Direct coding and trisoup geometry decoding are optionally applied. Further, the geometry decoding is not limited to the above example, and is performed in the reverse process of the geometry encoding described in FIGS. 1 to 9.
  • the Arismatic decoder 11000 decodes the received geometry bitstream based on Arismatic coding.
  • the operation of the Arismatic decoder 11000 corresponds to the reverse process of the Arismatic encoder 40004.
  • the octree synthesizer 11001 may generate an octree by obtaining an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding).
  • a detailed description of the OQFancy code is as described in FIGS. 1 to 9.
  • the surface opoxidation synthesizer 11002 may synthesize a surface based on the decoded geometry and/or the generated octree.
  • the geometry reconstruction unit 11003 may regenerate the geometry based on the surface and/or the decoded geometry. 1 to 9, direct coding and trisoup geometry encoding are selectively applied. Accordingly, the geometry reconstruction unit 11003 directly imports and adds position information of points to which direct coding is applied. In addition, when trisoup geometry encoding is applied, the geometry reconstruction unit 11003 performs a reconstruction operation of the geometry reconstruction unit 40005, such as triangle reconstruction, up-sampling, and voxelization, to restore the geometry. have. Detailed contents are the same as those described in FIG.
  • the reconstructed geometry may include a point cloud picture or frame that does not include attributes.
  • the coordinate system inverse transform unit 11004 may acquire positions of points by transforming a coordinate system based on the restored geometry.
  • Arithmetic decoder 11005, inverse quantization unit 11006, RAHT conversion unit 11007, LOD generation unit 11008, inverse lifting unit 11009, and/or color inverse conversion unit 11010 are attributes described in FIG. Decoding can be performed.
  • Attribute decoding according to embodiments includes Region Adaptive Hierarchial 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 Hierarchial 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 Arismatic decoder 11005 decodes the attribute bitstream by arithmetic coding.
  • the inverse quantization unit 11006 inverse quantizes information on the decoded attribute bitstream or the attribute obtained as a result of decoding, and outputs inverse quantized attributes (or attribute values). Inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.
  • the RAHT conversion unit 11007, the LOD generation unit 11008 and/or the inverse lifting unit 11009 may process reconstructed geometry and inverse quantized attributes. As described above, the RAHT conversion unit 11007, the LOD generation unit 11008, and/or the inverse lifting unit 11009 may selectively perform a decoding operation corresponding thereto according to the encoding of the point cloud encoder.
  • the inverse color transform unit 11010 performs inverse transform coding for inverse transforming a color value (or texture) included in the 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.
  • elements of the point cloud decoder of FIG. 11 are not shown in the drawing, 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 one or more of the operations and/or functions of the elements of the point cloud decoder of FIG. 11 described above. Further, one or more processors may operate or execute a set of software programs and/or instructions for performing operations and/or functions of 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 a point cloud encoder of FIG. 4 ).
  • the transmission device illustrated in FIG. 12 may perform at least one or more of the same or similar operations and methods as the operations and encoding methods of the point cloud encoder described in FIGS. 1 to 9.
  • the transmission apparatus includes a data input unit 12000, a quantization processing unit 12001, a voxelization processing unit 12002, an octree occupancy code generation unit 12003, a surface model processing unit 12004, an intra/ Inter-coding processing unit (12005), Arithmetic coder (12006), metadata processing unit (12007), color conversion processing unit (12008), attribute transformation processing unit (or attribute transformation processing unit) (12009), prediction/lifting/RAHT transformation
  • a processing unit 12010, an Arithmetic coder 12011, and/or a transmission processing unit 12012 may be included.
  • 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 an 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.
  • the geometry encoding according to the embodiments is the same as or similar to the geometry encoding described in FIGS. 1 to 9, so a detailed description thereof will be omitted.
  • the quantization processing unit 12001 quantizes geometry (eg, a position value or position value of points).
  • the operation and/or quantization of the quantization processor 12001 is the same as or similar to the operation and/or quantization of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the voxelization processor 12002 voxelsizes the position values of the quantized points.
  • the voxelization processor 120002 may perform the same or similar operation and/or process as the operation and/or the voxelization process of the quantization unit 40001 described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the octree occupancy code generation unit 12003 performs octree coding on positions of voxelized points based on an octree structure.
  • the octree ocupancy code generation unit 12003 may generate an ocupancy code.
  • the octree occupancy code generation unit 12003 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (or octree analysis unit 40002) described in FIGS. 4 and 6. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the surface model processing unit 12004 may perform trisoup geometry encoding to reconstruct positions of points within a specific area (or node) based on a voxel based on a surface model.
  • the face model processing unit 12004 may perform the same or similar operation and/or method as the operation and/or method of the point cloud encoder (eg, the surface aproxiation analysis unit 40003) described in FIG. 4. Detailed descriptions are the same as those described in FIGS. 1 to 9.
  • the intra/inter coding processor 12005 may intra/inter code point cloud data.
  • the intra/inter coding processing unit 12005 may perform the same or similar coding as the intra/inter coding described in FIG. 7. The detailed description is the same as described in FIG. 7. According to embodiments, 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 operation and/or method to the operation and/or method of the arithmetic encoder 40004.
  • the metadata processing unit 12007 processes metadata related to point cloud data, for example, a set value, and provides it to a necessary processing such as geometry encoding and/or attribute encoding.
  • the metadata processing unit 12007 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. In addition, 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.
  • Attribute encoding according to embodiments is the same as or similar to the attribute encoding described in FIGS. 1 to 9, and thus a detailed description thereof will be omitted.
  • the color conversion processing unit 12008 performs color conversion coding that converts color values included in attributes.
  • the color conversion processing unit 12008 may perform color conversion coding based on the reconstructed geometry. Description of the reconstructed geometry is the same as described in FIGS. 1 to 9. In addition, the same or similar operation and/or method to the operation and/or method of the color conversion unit 40006 described in FIG. 4 is performed. Detailed description will be omitted.
  • the attribute conversion processing unit 12009 performs attribute conversion for converting attributes based on the reconstructed geometry and/or positions for which geometry encoding has not been performed.
  • the attribute conversion processing unit 12009 performs the same or similar operation and/or method to the operation and/or method of the attribute conversion unit 40007 described in FIG. 4. Detailed description will be omitted.
  • the prediction/lifting/RAHT transform processing unit 12010 may code transformed attributes by using any one or a combination of RAHT coding, predictive transform coding, and lifting transform coding.
  • the prediction/lifting/RAHT conversion processing unit 12010 performs at least one of the same or similar operations as the RAHT conversion unit 40008, LOD generation unit 40009, and lifting conversion unit 40010 described in FIG. 4. do.
  • descriptions of predictive transform coding, lifting transform coding, and RAHT transform coding are the same as those described in FIGS.
  • the Arismatic coder 12011 may encode coded attributes based on Arismatic coding.
  • the Arismatic coder 12011 performs the same or similar operation and/or method to the operation and/or method of the Arismatic encoder 400012.
  • the transmission processor 12012 transmits each bitstream including the encoded geometry and/or the encoded attribute, and metadata information, or transmits the encoded geometry and/or the encoded attribute, and the metadata information in one piece. It can be configured as a bitstream and transmitted. When the encoded geometry and/or encoded attribute and metadata information according to the embodiments are configured as one 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 geometry information coding, an attribute parameter set (APS) for signaling of attribute information coding, and a tile.
  • SPS sequence parameter set
  • GPS geometry parameter set
  • APS attribute parameter set
  • TPS Transaction Parameter Set
  • Slice data may include information on one or more slices.
  • One slice according to embodiments may include one geometry bitstream (Geom0 0 ) and one or more attribute bitstreams (Attr0 0 and Attr1 0 ).
  • the TPS according to the embodiments may include information about each tile (eg, coordinate value information and height/size information of a bounding box) with respect to one or more tiles.
  • the geometry bitstream may include a header and a payload.
  • the header of the geometry bitstream may include identification information of a parameter set included in GPS (geom_ parameter_set_id), a tile identifier (geom_tile_id), a slice identifier (geom_slice_id), and information about data included in the payload.
  • the metadata processing unit 12007 may generate and/or process signaling information and transmit the generated and/or processed signaling information to the transmission processing unit 12012.
  • elements that perform geometry encoding and elements that perform attribute encoding may share data/information with each other as dotted line processing.
  • the transmission processing unit 12012 according to the embodiments may perform the same or similar operation and/or transmission method as the operation and/or transmission method of the transmitter 10003. Detailed descriptions are the same as those described in FIGS. 1 to 2 and thus will be omitted.
  • FIG 13 is an example of a reception device according to embodiments.
  • the receiving device illustrated in FIG. 13 is an example of the receiving device 10004 of FIG. 1 (or the point cloud decoder of FIGS. 10 and 11 ).
  • the receiving device illustrated in FIG. 13 may perform at least one or more of the same or similar operations and methods as the operations and decoding methods of the point cloud decoder described in FIGS. 1 to 11.
  • the receiving apparatus includes a receiving unit 13000, a receiving processing unit 13001, an arithmetic decoder 13002, an octree reconstruction processing unit 13003 based on an Occupancy code, and a surface model processing unit (triangle reconstruction).
  • a receiving unit 13000 Up-sampling, voxelization) (13004), inverse quantization processing unit (13005), metadata parser (13006), arithmetic decoder (13007), inverse quantization processing unit (13008), prediction A /lifting/RAHT inverse transformation processing unit 13009, a color inverse transformation processing unit 13010, and/or a renderer 13011 may be included.
  • Each component of decoding according to the embodiments may perform a reverse process of the component of encoding according to the embodiments.
  • the receiving unit 13000 receives point cloud data.
  • the receiving unit 13000 may perform the same or similar operation and/or a receiving method as the operation and/or receiving method of the receiver 10005 of FIG. 1. Detailed description will be omitted.
  • the reception processing unit 13001 may obtain a geometry bitstream and/or an attribute bitstream from received data.
  • the reception processing unit 13001 may be included in the reception unit 13000.
  • the arithmetic decoder 13002, the ocupancy 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 in FIGS. 1 to 10, a detailed description will be omitted.
  • the Arismatic decoder 13002 may decode a geometry bitstream based on Arismatic coding.
  • the Arismatic decoder 13002 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic decoder 11000.
  • the ocupancy code-based octree reconstruction processing unit 13003 may obtain an ocupancy code from a decoded geometry bitstream (or information on a geometry obtained as a result of decoding) to reconstruct the octree.
  • the ocupancy code-based octree reconstruction processing unit 13003 performs the same or similar operation and/or method as the operation and/or the octree generation method of the octree synthesis unit 11001.
  • the surface model processing unit 13004 decodes the trisoup geometry based on the surface model method and reconstructs the related geometry (eg, triangle reconstruction, up-sampling, voxelization). Can be done.
  • the surface model processing unit 13004 performs an operation identical or similar to that of the surface opoxidation 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 metadata to geometry decoding and/or attribute decoding.
  • the detailed description of the metadata is the same as that described in 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 in FIGS. 1 to 10, a detailed description will be omitted.
  • the Arismatic decoder 13007 may decode the attribute bitstream through Arismatic coding.
  • the arithmetic decoder 13007 may perform decoding of the attribute bitstream based on the reconstructed geometry.
  • the Arismatic decoder 13007 performs the same or similar operation and/or coding as the operation and/or coding of the Arismatic 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 operation and/or method as the operation and/or the inverse quantization method of the inverse quantization unit 11006.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 may process reconstructed geometry and inverse quantized attributes.
  • the prediction/lifting/RAHT inverse transform processing unit 13009 is the same or similar to the operations and/or decodings of the RAHT transform unit 11007, the LOD generator 11008 and/or the inverse lifting unit 11009, and/or At least one or more of the decodings is performed.
  • the color inverse transform processing unit 13010 according to embodiments performs inverse transform coding for inverse transforming a color value (or texture) included in the decoded attributes.
  • the color inverse transform processing unit 13010 performs the same or similar operation and/or inverse transform coding as 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 architecture for G-PCC-based point cloud content streaming according to embodiments.
  • FIG. 14 shows a process in which the transmission device (for example, the transmission device 10000, the transmission device of FIG. 12, etc.) described in FIGS. 1 to 13 processes and transmits the point cloud content.
  • the transmission device for example, the transmission device 10000, the transmission device of FIG. 12, etc.
  • the transmission device may obtain audio Ba of the point cloud content (Audio Acquisition), encode the acquired audio, and output audio bitstreams Ea.
  • the transmission device acquires a point cloud (Bv) (or point cloud video) of the point cloud content (Point Acqusition), performs point cloud encoding on the acquired point cloud, and performs a point cloud video bitstream ( Eb) can be output.
  • the point cloud encoding of the transmission device is the same as or similar to the point cloud encoding (for example, the encoding of the point cloud encoder of FIG. 4) described in FIGS.
  • the transmission device may encapsulate the generated audio bitstreams and video bitstreams into files and/or segments (File/segment encapsulation).
  • the encapsulated file and/or segment may include a file of a file format such as ISOBMFF or a DASH segment.
  • Point cloud-related metadata may be included in an encapsulated file format and/or segment.
  • Meta data may be included in boxes of various levels in the ISOBMFF file format or may be included in separate tracks in the file.
  • the transmission device may encapsulate the metadata itself as a separate file.
  • the transmission device according to the embodiments may deliver the encapsulated file format and/or segment through a network. Since the encapsulation and transmission processing method of the transmission device is the same as those described in FIGS. 1 to 13 (for example, the transmitter 10003, the transmission step 20002 of FIG. 2, etc.), detailed descriptions are omitted.
  • FIG. 14 shows a process of processing and outputting point cloud content by the receiving device (for example, the receiving device 10004, the receiving device of FIG. 13, etc.) described in FIGS. 1 to 13.
  • the receiving device for example, the receiving device 10004, the receiving device of FIG. 13, etc.
  • the receiving device includes a device that outputs final audio data and final video data (e.g., loudspeakers, headphones, display), and a point cloud player that processes point cloud content ( Point Cloud Player).
  • the final data output device and the point cloud player may be configured as separate physical devices.
  • the point cloud player according to the embodiments may perform Geometry-based Point Cloud Compression (G-PCC) coding and/or Video based Point Cloud Compression (V-PCC) coding and/or next-generation coding.
  • G-PCC Geometry-based Point Cloud Compression
  • V-PCC Video based Point Cloud Compression
  • the receiving device secures a file and/or segment (F', Fs') included in the received data (for example, a broadcast signal, a signal transmitted through a network, etc.) and decapsulation (File/ segment decapsulation). Since the reception and decapsulation method of the reception device is the same as that described in FIGS. 1 to 13 (for example, the receiver 10005, the reception unit 13000, the reception processing unit 13001, etc.), a detailed description is omitted.
  • the receiving device secures an audio bitstream E'a and a video bitstream E'v included in a file and/or segment. As shown in the drawing, the receiving device outputs the decoded audio data B'a by performing audio decoding on the audio bitstream, and rendering the decoded audio data to final audio data. (A'a) is output through speakers or headphones.
  • the receiving device outputs decoded video data B'v by performing point cloud decoding on the video bitstream E'v. Since the point cloud decoding according to the embodiments is the same as or similar to the point cloud decoding described in FIGS. 1 to 13 (for example, decoding of the point cloud decoder of FIG. 11 ), a detailed description will be omitted.
  • the receiving device may render the decoded video data and output the final video data through the display.
  • the receiving device may perform at least one of decapsulation, audio decoding, audio rendering, point cloud decoding, and rendering operations based on metadata transmitted together.
  • the description of the metadata is the same as that described with reference to FIGS. 12 to 13 and thus will be omitted.
  • the receiving device may generate feedback information (orientation, viewport).
  • Feedback information may be used in a decapsulation process, a point cloud decoding process and/or a rendering process of a receiving device, or may be transmitted to a transmitting device. The description of the feedback information is the same as that described with reference to FIGS. 1 to 13 and thus will be omitted.
  • 15 shows an example of a transmission device according to embodiments.
  • the transmission device of FIG. 15 is a device that transmits point cloud content, and the transmission device described in FIGS. 1 to 14 (for example, the transmission device 10000 of FIG. 1, the point cloud encoder of FIG. 4, the transmission device of FIG. 12, 14). Accordingly, the transmission device of FIG. 15 performs the same or similar operation to that of the transmission device described in FIGS. 1 to 14.
  • the transmission device may perform at least one or more of point cloud acquisition, point cloud encoding, file/segment encapsulation, and delivery. Can be done.
  • the transmission device may perform geometry encoding and attribute encoding.
  • Geometry encoding according to embodiments may be referred to as geometry compression, and attribute encoding may be referred to as attribute compression.
  • attribute compression As described above, one point may have one geometry and one or more attributes. Therefore, the transmission device performs attribute encoding for each attribute.
  • the drawing shows an example in which a transmission device has performed one or more attribute compressions (attribute #1 compression, ...attribute #N compression).
  • the transmission apparatus may perform auxiliary compression. Additional compression is performed on the metadata. Description of the meta data is the same as that described with reference to FIGS. 1 to 14 and thus will be omitted.
  • the transmission device may perform mesh data compression.
  • Mesh data compression according to embodiments may include the trisoup geometry encoding described in FIGS. 1 to 14.
  • the transmission device may encapsulate bitstreams (eg, point cloud streams) output according to point cloud encoding into files and/or segments.
  • a transmission device performs media track encapsulation for carrying data other than metadata (for example, media data), and metadata tracak for carrying meta data. encapsulation) can be performed.
  • metadata may be encapsulated as a media track.
  • the transmitting device receives feedback information (orientation/viewport metadata) from the receiving device, and based on the received feedback information, at least one of point cloud encoding, file/segment encapsulation, and transmission operations. Any one or more can be performed. Detailed descriptions are the same as those described with reference to FIGS.
  • FIG. 16 shows an example of a receiving device according to embodiments.
  • the receiving device of FIG. 16 is a device that receives point cloud content, and the receiving device described in FIGS. 1 to 14 (for example, the receiving device 10004 of FIG. 1, the point cloud decoder of FIG. 11, the receiving device of FIG. 13, 14). Accordingly, the receiving device of FIG. 16 performs the same or similar operation to that of the receiving device described in FIGS. 1 to 14. In addition, the receiving device of FIG. 16 may receive a signal transmitted from the transmitting device of FIG. 15, and may perform a reverse process of the operation of the transmitting device of FIG.
  • the receiving device may perform at least one or more of delivery, file/segement decapsulation, point cloud decoding, and point cloud rendering. Can be done.
  • the reception device performs decapsulation on a file and/or segment acquired from a network or a storage device.
  • the receiving device performs media track decapsulation carrying data other than meta data (for example, media data), and metadata track decapsulation carrying meta data. decapsulation) can be performed.
  • the metadata track decapsulation is omitted.
  • the receiving device may perform geometry decoding and attribute decoding on bitstreams (eg, point cloud streams) secured through decapsulation.
  • Geometry decoding according to embodiments may be referred to as geometry decompression, and attribute decoding may be referred to as attribute decompression.
  • a point may have one geometry and one or more attributes, and are each encoded. Therefore, the receiving device performs attribute decoding for each attribute.
  • the drawing shows an example in which the receiving device performs one or more attribute decompressions (attribute #1 decompression, ...attribute #N decompression).
  • the reception device may perform auxiliary decompression. Additional decompression is performed on the metadata.
  • the receiving device may perform mesh data decompression.
  • the mesh data decompression according to embodiments may include decoding the trisoup geometry described with reference to FIGS. 1 to 14.
  • the reception device according to the embodiments may render the output point cloud data according to the point cloud decoding.
  • the receiving device secures orientation/viewport metadata using a separate sensing/tracking element, etc., and transmits feedback information including the same to a transmission device (for example, the transmission device of FIG. 15). Can be transmitted.
  • the receiving device may perform at least one or more of a receiving operation, file/segment decapsulation, and point cloud decoding based on the feedback information. Detailed descriptions are the same as those described with reference to FIGS.
  • FIG. 17 shows 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. 17 includes at least one of a server 1760, a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770.
  • a configuration connected to the cloud network 1710 is shown.
  • the robot 1710, the autonomous vehicle 1720, the XR device 1730, the smartphone 1740, the home appliance 1750, and the like are referred to as devices.
  • the XR device 1730 may correspond to a point cloud data (PCC) device according to embodiments or may be interlocked with a PCC device.
  • PCC point cloud data
  • the cloud network 1700 may constitute a part of a cloud computing infrastructure or may mean a network that exists in the cloud computing infrastructure.
  • the cloud network 1700 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 1760 includes at least one of a robot 1710, an autonomous vehicle 1720, an XR device 1730, a smartphone 1740, a home appliance 1750, and/or an HMD 1770, and a cloud network 1700.
  • the connected devices 1710 to 1770 may be connected through, and may help at least part of the processing of the connected devices.
  • the HMD (Head-Mount Display) 1770 represents one of 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 1710 to 1750 shown in FIG. 17 may be interlocked/coupled with the point cloud data transmission/reception apparatus according to the above-described embodiments.
  • the XR/PCC device 1730 is applied with PCC and/or XR (AR+VR) technology to provide a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, It may be implemented as a computer, wearable device, home appliance, digital signage, vehicle, fixed robot or mobile robot.
  • HMD head-mount display
  • HUD head-up display
  • vehicle a television
  • mobile phone a smart phone
  • It may be implemented as a computer, wearable device, home appliance, digital signage, vehicle, fixed robot or mobile robot.
  • the XR/PCC device 1730 analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate position data and attribute data for 3D points, thereby Information can be obtained, and the XR object to be output can be rendered and output.
  • the XR/PCC device 1730 may output an XR object including additional information on the recognized object in correspondence with the recognized object.
  • the autonomous vehicle 1720 may be implemented as a mobile robot, a vehicle, or an unmanned aerial vehicle by applying PCC technology and XR technology.
  • the autonomous driving vehicle 1720 to which the XR/PCC technology is applied may refer to an autonomous driving vehicle having a means for providing an XR image, an autonomous driving vehicle that is an object of control/interaction within the XR image.
  • the autonomous vehicle 1720 which is the object of control/interaction in the XR image, is distinguished from the XR device 1730 and may be interlocked with each other.
  • the autonomous vehicle 1720 having a means for providing an XR/PCC image may acquire sensor information from sensors including a camera, and may output an XR/PCC image generated based on the acquired sensor information.
  • the autonomous vehicle 1720 may provide an XR/PCC object corresponding to a real object or an object in a screen to the occupant 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 part of the XR/PCC object may be output to overlap the actual object facing the occupant's gaze.
  • the XR/PCC object when the XR/PCC object is output on 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 lanes, other vehicles, traffic lights, traffic signs, motorcycles, 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 virtually created CG image on a real object image.
  • MR technology is similar to the AR technology described above in that virtual objects are mixed and combined in the real world.
  • real objects and virtual objects made from CG images are clear, and virtual objects are used in a form that complements the real objects, whereas in MR technology, the virtual objects are regarded as having the same characteristics as the real objects. It is distinct from technology. More specifically, for example, it is a hologram service to which the aforementioned MR technology is applied.
  • VR, AR, and MR technologies are sometimes referred to as XR (extended reality) technology rather than clearly distinguishing between them. Therefore, embodiments of the present invention are applicable to all of VR, AR, MR, and XR technologies.
  • This technology can be applied to encoding/decoding based on PCC, V-PCC, and G-PCC technology.
  • the PCC method/apparatus according to the embodiments may be applied to a vehicle providing an autonomous driving service.
  • Vehicles providing autonomous driving service are connected to PCC devices to enable wired/wireless communication.
  • the vehicle receives/processes AR/VR/PCC service related content data that can be provided together with the autonomous driving service. Can be transferred to.
  • the point cloud transmission/reception device 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.
  • the vehicle or user interface device may receive a user input signal.
  • the user input signal may include a signal indicating an autonomous driving service.
  • FIG. 18 shows an example of a space of points constituting point cloud content according to embodiments.
  • Point cloud data may represent a three-dimensional space based on XYZ coordinates in an encoding/decoding unit, for example, a unit of a cube.
  • the method/apparatus according to the embodiments may encode/decode point cloud data based on an encoding/decoding unit.
  • the encoding/decoding unit 18000 represents a unit of space.
  • the encoding/decoding unit may have a box shape of a cube based on 3D coordinates.
  • the sub-geometric information or geometric information 18001 represents an information unit obtained by dividing an encoding/decoding unit. According to embodiments, such a box may be referred to as sub-geometric information and/or geometric information.
  • Point 18002 represents a point representing point cloud data.
  • Point represents point cloud content and represents geometric information.
  • the bounding box 18003 represents a box including sub geometric information and/or geometric information. It refers to a bounding box expressing a space in which points including geometric information are distributed. Depending on the embodiments, the bounding box may be a box that divides or includes a coding/decoding unit, and may include sub-geometric information or geometric information, or may refer to a box corresponding to sub-geometric information or geometric information.
  • the offset 18004 represents a value representing the position of the sub-geometric information, the geometric information, and/or the bounding box on a three-dimensional space coordinate.
  • the bounding box may be positioned at a position expressed based on x offset, y offset, and z offset based on the origin of the XYZ coordinate system.
  • the bounding box width, the bounding box height, and the bounding box depth 17005 represent the width, height, and depth of the bounding box, respectively.
  • Point cloud data or point cloud content represents 3D point cloud data.
  • a method/apparatus according to embodiments represents a method/apparatus for transmitting and/or receiving point cloud data according to the embodiments.
  • the method/apparatus according to the embodiments relates to a method for improving compression of location information (geometric information) of points in Geometry-based Point Cloud Compression (G-PCC) for compressing 3D point cloud data. .
  • the method/apparatus according to the embodiments proposes a method of dividing the geometric information of a point cloud and effectively predicting it in order to effectively compress the geometric information of a point constituting the point cloud data.
  • points constituting the point cloud content may exist while being biased to a partial area in space.
  • occupancy information indicating that the point does not exist should be encoded and transmitted to the region where the point does not exist on the oct tree indicating the geometric information of the points.
  • the scheme proposed by the embodiments proposes a scheme that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist.
  • Point cloud data may be represented by dividing a space into an encoding/decoding unit to represent a space.
  • the encoding/decoding unit may be in the form of a rectangular parallelepiped (or a cube, etc.).
  • the method/apparatus according to the embodiments proposes a method that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.
  • FIG. 19 shows an example of a point cloud encoder according to embodiments.
  • the encoder according to the embodiments may be referred to as a PCC encoder, and as shown in the drawing, may be configured with a geometric information encoder or/and an attribute information encoder.
  • the space division unit 19000 receives PCC data.
  • the spatial division unit may spatially divide PCC data into 3D blocks.
  • the spatial division unit may spatially divide point cloud data (PCC data) based on a bounding box and/or a sub-bounding box, and the method/device according to the embodiments is based on a divided unit (box).
  • PCC data point cloud data
  • the method/device according to the embodiments is based on a divided unit (box).
  • the geometry information encoding unit 19001 may encode geometry information (or geometry information).
  • the encoder may generate a bitstream including the encoded geometry information.
  • the encoding unit may generate reconstructed (reconstructed) geometry information.
  • the attribute information encoding unit 19002 may receive spatially divided PCC data and reconstructed geometric information.
  • the encoder may generate a bitstream including attribute information (or attribute information) by encoding the received data.
  • the attribute information encoder may encode attribute information based on the reconstructed geometric information.
  • the spatial division unit, the geometric information encoding unit, and the attribute information encoding unit may correspond to hardware, software, a processor, and/or a combination thereof.
  • the PCC data according to embodiments may be composed of geometric information or/and attribute information of a point.
  • the attribute information according to the embodiments is one or more, such as a vector representing the color of a point (R, G, B) or/and a brightness value or/and a reflection coefficient of a lidar or/and a temperature value obtained from a thermal imaging camera. It may be a vector of values obtained from two sensors.
  • the spatial division unit may divide the input PCC data into at least one 3D block.
  • the block may mean a coding unit (CU), a prediction unit (PU), or a transformation unit (TU).
  • the partitioning may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, and a k-d tree.
  • Point cloud data (PCC data) may have a form in which points are variously distributed in a 3D space.
  • the method/apparatus according to the embodiments may process variously distributed points based on a divided spatial box for efficient encoding/decoding of point cloud data.
  • the geometric information encoding unit generates an encoded geometric information bitstream and reconstructed geometric information from the received geometric information.
  • the generated bitstream may be transmitted to the PCC decoder.
  • the generated reconstructed geometric information may be input to the attribute information encoding unit.
  • the attribute information encoding unit receives the received attribute information and generates an attribute information bitstream.
  • the generated attribute information bitstream may be transmitted to the PCC decoder.
  • the spatial division unit which can be derived from a decoder, instead of encoding and delivering occupancy information of points in an area where points of point cloud content do not exist based on the spatial division process. Suggest a plan. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.
  • the point cloud data transmission method may further include spatially dividing the point cloud data.
  • the method of receiving point cloud data may further include the step of spatially dividing the point cloud data.
  • FIG. 20 shows an example of a geometric information encoder according to embodiments.
  • the coordinate system conversion unit 2001 may receive geometric information corresponding to the location information and convert a coordinate system of the geometric information.
  • the geometric information conversion quantization unit 2002 can quantize geometric information.
  • the residual geometric information quantization unit 2003 may quantize the quantized geometric information and/or the residual geometric information generated based on the predicted geometric information. For example, the residual value can be generated by subtracting the predicted geometric information from the geometric information.
  • the geometric information entropy encoder 2004 may encode geometric information based on an entropy encoding method.
  • the geometric information entropy encoding unit may generate a bitstream including geometric information.
  • the residual geometric information inverse quantization unit (2005) may inversely quantize the residual geometric information.
  • the filtering unit 1906 may perform filtering based on the inverse quantized geometric information and the predicted geometric information. For example, it is possible to filter the generated data by summing the predicted geometric information and residual geometric information.
  • the memory unit 2007 may store geometric information based on the filtered data.
  • the memory unit may generate the restored geometric information based on the stored geometric information.
  • the geometric information prediction unit 2008 may predict geometric information based on the geometric information stored in the memory.
  • the geometric information prediction unit may transmit the predicted data to the residual geometric information quantization unit and/or the full vehicle geometric information inverse quantization unit.
  • Each element of the geometric information encoder according to the embodiments may correspond to hardware, software, a processor, and/or a combination thereof.
  • the geometric information encoding unit may include a coordinate system transforming unit, a geometric information transforming quantization unit, a residual geometric information quantizing unit, a geometric information entropy encoding unit, a residual geometric information inverse quantizing unit, a memory, and a geometric information predicting unit.
  • the coordinate system conversion unit may receive geometric information as an input and convert it into a coordinate system different from the existing coordinate system. Alternatively, coordinate system transformation may not be performed.
  • the geometric information converted by the coordinate system may be input to the geometric information conversion quantization unit.
  • Whether the coordinate system is transformed and the coordinate system information may be signaled in units such as a sequence, frame, tile, slice, or block, or whether the coordinate system of neighboring blocks is transformed or not , It can be derived using the location of the unit, and the distance between the unit and the origin.
  • the coordinate system information to be converted may be signaled in units such as sequence, frame, tile, slice, block, etc. It can be derived using the size, number of points, quantization value, block division depth, unit location, and distance between the unit and the origin.
  • the geometric information transform quantization unit receives geometric information as input, applies one or more transforms such as position transform or/and rotation transform, divides the geometric information by a quantization value, and quantizes the transformed quantized geometric information. .
  • the transformed quantized geometric information may be input to a geometric information entropy encoding unit and a residual geometric information quantizing unit.
  • the geometric information prediction unit predicts geometric information through geometric information of points in a memory and generates predicted geometric information.
  • the prediction information used for prediction may be encoded by performing entropy encoding.
  • the residual geometric information quantization unit receives residual geometric information obtained by differentiating the transformed-quantized geometric information and the predicted geometric information, and quantizes it into a quantized value to generate quantized residual geometric information.
  • Quantized residual geometric information may be input to a geometric information entropy encoding unit and a residual geometric information inverse quantization unit.
  • the geometric information entropy encoding unit may receive quantized residual geometric information or geometric information and perform entropy encoding.
  • Entropy coding may use various coding methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC).
  • the residual geometric information inverse quantization unit receives the quantized residual geometric information and restores the residual geometric information by scaling the quantized value.
  • the restored residual geometric information may be restored to geometric information in addition to the predicted geometric information.
  • the filtering unit may perform filtering on the reconstructed geometric information.
  • Information related to filtering may be encoded by performing entropy encoding.
  • the memory may store geometric information calculated through a filtering unit.
  • the stored geometric information may be provided to the geometric information prediction unit when performing prediction.
  • 21 illustrates a bounding box of geometric information of point cloud data according to embodiments.
  • the geometric information according to the embodiments may be divided based on the bounding box according to the embodiments.
  • the bounding box 2100 refers to an area in the form of a rectangular parallelepiped (or a cube, etc.) having a minimum size including geometric information.
  • a bounding box including geometric information may be divided into a box including four sub-geometric information.
  • the sub-bounding box 2101 refers to a sub-box including geometric information included in the bounding box.
  • the sub/decoding unit 2102 may include one or more sub geometric information or geometric information, and may include one or more bounding boxes and/or sub bounding boxes.
  • the method/apparatus according to the embodiments may efficiently encode/decode points and geometric information distributed in space based on the bounding box.
  • the method/apparatus according to the embodiments may provide a technique for dividing geometric information of point cloud content.
  • the geometric information of the point cloud content can be divided into a bounding box (the bounding box may refer to a rectangular area of the smallest size including the geometric information).
  • the bounding box according to the embodiments may be divided into one or a plurality of sub-bounding boxes of equal or non-uniform size and may be encoded/decoded independently or dependent on each other.
  • the encoding/decoding unit may be configured in a square shape having a minimum size including a sub-bounding box or a bounding box according to embodiments.
  • the encoding/decoding unit may be configured in a square shape having the smallest size including the geometric information.
  • Geometry information may be expressed by dividing the sub/decoding unit according to the embodiments into an oct tree structure.
  • the method/apparatus according to the embodiments proposes a method that can be derived from a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist based on a bounding box of geometric information.
  • a decoder instead of encoding and transmitting occupancy information of points in an area where points of point cloud content do not exist based on a bounding box of geometric information.
  • FIG. 22 shows an example of an operation of a space dividing unit according to embodiments.
  • the spatial division unit parses higher-level information (2200), divides the bounding box into sub-bounding boxes, configures a coding/decoding unit (2201), and/or parses or derives divided information of a sub/decoding unit (2202). You can do it.
  • Each component according to the embodiments will be described as follows.
  • the high-level information parsing 2200 represents a process in which the receiving method/device according to the embodiments parses signaling information (or parameter set) included in the point cloud data.
  • the high-level information means signaling information or a parameter set according to embodiments, and may include information related to a spatial division unit according to embodiments.
  • the transmission method/apparatus according to the embodiments may perform spatial partitioning and include signaling information related to spatial partitioning as higher level information in higher level information.
  • the division of the bounding box into sub-bounding boxes and the configuration of a decoding/decoding unit 2201 represents a process in which the transmission/reception method/device according to the embodiments divides and represents the space of the point cloud data in the space division process.
  • points of point cloud data may be divided into bounding boxes.
  • the bounding box may be divided into one or more sub bounding boxes.
  • the bounding box/subbounding box may constitute an encoding/decoding unit.
  • the method/apparatus according to embodiments provides an effect of efficiently encoding/decoding points distributed based on a bounding box/subbounding box in a coding/decoding unit.
  • Parsing or deriving split information 2202 of a sub/decoding unit is a process in which a transmission method/device according to embodiments acquires coding units and/or split information necessary for encoding (encoding), or reception according to embodiments. It represents a process in which a method/apparatus obtains a decoding unit and/or split information required for decoding (decoding).
  • the spatial division unit for dividing geometric information includes a step of parsing high-level information, division of geometric information into sub-geometric information, parsing or deriving encoding/decoding unit information, and division of encoding/decoding units. It may involve parsing or deriving information.
  • the spatial division unit may be applied in the octtree coding step according to the embodiments.
  • coordinates of encoding/decoding units, size of encoding/decoding units, number of encoding/decoding units, minimum node size, maximum node division depth, coordinates of bounding box, width, height, depth of region The number of information, geometric information, etc. may be parsed or derived from higher level information.
  • the bounding box according to the embodiments may be divided into sub bounding boxes. By parsing the coordinates, size, and number of sub-bounding boxes from high-level information, division information of sub-geometric information can be found.
  • the encoding/decoding unit can be set in the form of a rectangular parallelepiped having a minimum size including a subbounding box.
  • a division method, and whether or not geometric information exists within a node may be parsed or derived to find out.
  • child nodes can be created by division, and node division can be repeatedly performed until division of the node is stopped. If the regions of the node and the sub-bounding box do not overlap, it is determined that geometric information does not exist in the node, and whether or not the node is divided may be derived.
  • the node and the bounding box area overlap using the coordinate information of the node, the size of the node, the coordinate information of the subbounding box, and the size of the subbounding box according to the embodiments. In the following cases, it can be determined that the regions of the node and the bounding box do not overlap.
  • the regions of the node and the bounding box according to the embodiments overlap, it is possible to parse or derive whether the node is divided, the division method, and whether or not geometric information exists in the node.
  • the regions of the node and the bounding box overlap and the size of the node is the same as the minimum node size whether or not the node is divided can be derived, meaning that the division of the node is stopped, and the existence of a point in the node can be derived or parsed.
  • Points constituting the point cloud content according to the embodiments may exist in a partial area in a space.
  • occupancy information indicating non-existence for a region that does not exist in the octtree of points must be encoded by the geometry information encoder.
  • One point cloud content may be divided into one or more bounding boxes, and geometric information may be encoded in units of each bounding box or sub bounding box.
  • the spatial dividing unit or the spatial dividing step of the method/apparatus instead of encoding and transmitting occupancy information of points in a region where points of point cloud content do not exist, a method that can be derived from a decoder is proposed. Through this, by reducing the amount of information to be encoded for the geometric information, it is possible to improve compression of the location information (geometric information) of the point cloud.
  • a method of transmitting point cloud data may include a step of dividing a bounding box in relation to a process of dividing the point cloud data into bounding boxes.
  • the encoding step may encode points of point cloud data included in the bounding box related to the spatial division.
  • a method of receiving point cloud data may include a step of dividing a bounding box in relation to a process of dividing the point cloud data into bounding boxes.
  • a point of point cloud data included in a bounding box related to spatial division may be decoded.
  • the point cloud data may include parameters including the number of bounding boxes related to spatial division, spatial coordinate information of the bounding box, and width, height, and depth of the bounding box.
  • 23 is a diagram illustrating an example of performing octtree division information and/or derivation information of geometric information of point cloud data according to embodiments.
  • the octree (or octtree) according to the embodiments may basically convey information in the form of, for example, 8 bits.
  • the method/apparatus according to the embodiments may divide a space in which points of point cloud data are distributed.
  • embodiments may represent points based on an octree structure. Since the spatial distribution map of the points varies, it may be inefficient to encode/decode all spaces.
  • embodiments may hierarchically divide points (or geometric information) into sub-bounding boxes, bounding boxes, and sub/decoding units as shown in the drawing, thereby efficiently expressing an area where points are located. .
  • an octree (or octtree) includes a node, and may have a structure between a parent node and a child node.
  • the parent node may include 8-bit child nodes.
  • the embodiments may express the octtree using bits of 0 and 1.
  • reference 1001 may indicate information on whether or not to be split provided from an encoder (encoder, transmission device, etc.) according to embodiments.
  • the method/apparatus according to the embodiments may encode/transmit/receive/decode only a portion where data exists without the need to encode/transmit/receive/decode all 8 bits. For example, an area in which 0 is continuously present is an area in which data does not exist, and the transmission method/apparatus according to the embodiments may transmit only 1 bit to 4 bits in which data exists.
  • the embodiments can provide high encoding/decoding performance by reducing data throughput compared to a method of transmitting information of all spaces by expressing a space of point cloud data as a cube or a rectangular parallelepiped.
  • various point cloud data such as lidar data
  • a partitioning method may be encoded/decoded using a partitioning method according to the embodiments.
  • the receiving method/apparatus does not actually receive an area corresponding to (0000) in the drawing, but restores and decodes based on the received point cloud data and parameter information included in the point cloud data. I can.
  • the method/apparatus or space dividing unit may divide the point cloud data into one or more bounding boxes.
  • the method/device or the spatial division unit sets the coordinate (range) of the bounding box or offset information on the coordinate system, You can set the width, height, and depth of the bounding box.
  • the method/apparatus according to the embodiments may signal the number of points included in the bounding box range.
  • the bounding box may be divided into one or more sub-bounding boxes.
  • the method/apparatus sets the number of sub-bounding boxes and obtains information such as coordinates, offsets, width, height, and depth to determine the range of each sub-bounding box. Can signal.
  • the receiving method/device does not need to receive all the bits of the spatially divided tree structure of point cloud data, and points are based on the bounding box/subbounding box/related signaling information (parameters), etc.
  • bounding box/subbounding box/related signaling information parameters
  • only information marked in yellow may be encoded and transmitted through a geometric information encoder.
  • the PCC decoder can implicitly derive the information of the part marked in blue by using the bounding box information, etc., and reconstruct the geometric information octtree of the point by combining the derived information and the transmitted information.
  • the following parameters may be included in the bit stream and transmitted.
  • the size of the encoding/decoding unit (for example, whether it is in a bounding box unit or a subbounding box unit, etc.)
  • Number of encoding/decoding units (number of encoding/decoding units based on the size of the encoding/decoding unit)
  • Sub-bounding such as coordinates (x offset, y offset, z offset) in space of each bounding box, width, height, depth, number of points included in the bounding box, etc.
  • x offset coordinates (x offset, y offset, z offset) in space of each bounding box
  • y offset offsets in space of each bounding box
  • the decoder implicitly induces portions corresponding to information about whether to be temporarily derived from the decoder, the number of bounding boxes, the number of subbounding boxes included in the bounding box, and the number of subbounding boxes. At least one or more of coordinates in space, width, height, and depth information of each subbounding box may be required. For example, bounding box/sub bounding box and coordinate information may be required.
  • FIG. 24 shows an example of a structure of point cloud data according to embodiments.
  • Point cloud data may have a bitstream form as shown in the drawing.
  • the point cloud data may include a sequence parameter set (SPS), a geometry parameter set (GPS), an attribute parameter set (APS), and a tile parameter set (TPS) including signaling information according to embodiments.
  • Point cloud data may include one or more geometry and/or attributes.
  • the point cloud data may include geometry and/or attributes in units of one or more slices.
  • the geometry may have a structure of a geometry slice header and geometry slice data.
  • the TPS including signaling information is Tile(0). It may include tile_bounding_box_xyz0, Tile(0)_tile_bounding_box_whd, and the like.
  • the geometry may include geom_geom_parameter_set_id, geom_tile_id, geom_slice_id, geomBoxOrigin, geom_box_log2_scale, geom_max_node_size_log2, geom_num_points, and the like.
  • the parameters according to the above-described embodiments may be added to and transmitted to SPS, GPS, or APS, or may be added to and transmitted to TPS or Geom for each Slice or Attr for each Slice.
  • Attrobite bitstream attribute blick header + attribute brick data
  • different partitioning techniques may be applied for each tile or slice.
  • the width, height, depth, and number of points included in the bounding box may be different for each slice.
  • the parameter may be added and transmitted to the TPS or Geom for each Slice or Attr for each Slice.
  • 25 shows an example of a process of inverse quantization of geometric information according to embodiments.
  • a decoder may perform geometric information decoding, and the geometric information decoding may inverse quantize the geometric information.
  • Each step according to the embodiments may be as follows.
  • Parsing or deriving geometric information dequantization value 2500 refers to a process of parsing or deriving a value for dequantizing geometric information of point cloud data.
  • the geometric information quantized at the transmission or encoder side according to the embodiments may be inverse quantized by a reception or decoder according to the embodiments in a reverse process.
  • the inverse quantization value may be parsed or derived based on point cloud data, parameter set and/or signaling information.
  • Performing geometric information inverse quantization 2501 refers to a process in which a receiving method/device, for example, a geometric information decoder, inverse quantizes geometric information based on a parsed or derived inverse quantization value.
  • a geometric information inverse quantization value may be parsed or derived from a geometric information decoder, and the geometric information inverse quantization may be performed based on this.
  • inverse quantization values in the width, height, and depth directions may be parsed or derived.
  • One or more parsed inverse quantization values can be shared in the width, height, or depth direction.
  • the inverse quantization value may be derived using a value or ratio of the width or depth or height of the bounding box.
  • Inverse quantization information can be shared in units of bounding boxes or subbounding boxes.
  • the scale values of the width, height, and depth may be the same or may be different from each other.
  • inverse quantization may be performed by multiplying the restored geometric information by scale values in the width, height, and depth directions, respectively.
  • 26 illustrates an exemplary embodiment of inverse quantization of geometric information according to embodiments.
  • Geometric information may have a width, a depth and/or a height.
  • the geometric information decoder may inverse quantize the geometric information.
  • the width, depth and/or height of the geometric information can be scaled by each value.
  • a width scale value, a depth scale value, and a height scale value may be the same or different.
  • FIG. 27 shows an example of a point cloud decoder according to embodiments.
  • Each component according to embodiments of the point cloud data receiving method/device or decoder (decoder) according to the embodiments is as follows.
  • the spatial dividing unit 2700 may receive the geometric information bitstream and divide the space of the geometric information. Based on the information obtained by spatially dividing the geometric information by the transmission method/device according to the embodiments, the decoder may also divide the space of the point cloud data.
  • the geometry information decoding unit 2701 may decode a geometry information bitstream.
  • the attribute information decoding unit 2702 may decode attribute information of the attribute information bitstream based on the restored geometric information. Geometric information and/or attribute information included in the point cloud data may be decoded and restored PCC data.
  • the PCC decoder may include a geometric information decoding unit and an attribute information decoding unit.
  • the spatial division unit may divide a space based on division information provided from an encoder or derived from a decoder.
  • the geometric information decoding unit restores the geometric information by decoding the received geometric information bitstream.
  • the restored geometric information may be input to the attribute information decoding unit.
  • the attribute information decoding unit receives the received attribute information bitstream and restored geometric information received from the geometry information decoding unit and restores attribute information.
  • the restored attribute information can be composed of restored PCC data along with the restored geometric information.
  • the geometric information decoder includes a geometric information entropy decoding unit 2800, a residual geometric information inverse quantization unit 2801, a geometric information prediction unit 2802, a filtering unit 2803, a memory unit 2804, and/or A coordinate system inverse transform unit 2805 may be included.
  • the geometric information entropy decoder 2800 may receive a bitstream including geometric information and decode the geometric information. For example, it can be decoded based on an entropy method.
  • the residual geometric information inverse quantization unit 2801 may inversely quantize the residual geometric information.
  • the geometric information prediction unit 2802 may predict geometric information.
  • the geometric information prediction unit may predict geometric information based on the geometric information stored in the memory.
  • the filtering unit 2803 may filter data generated based on inverse quantized residual geometric information and/or predicted geometric information.
  • data may be generated by the apparatus according to the embodiments by summing the inverse quantized residual geometric information and/or the predicted geometric information.
  • the memory unit 2804 may store filtered data.
  • the coordinate system inverse transform unit 2805 may receive geometric information stored in a memory and convert the coordinate system of the geometric information into an inverse manner.
  • the inverse coordinate system transform unit may generate geometric information.
  • the apparatus for decoding geometric information may include a geometric information entropy decoding unit, a residual geometric information inverse quantization unit, a geometric information prediction unit, and an inverse coordinate system transform unit.
  • the geometric information entropy decoder may perform entropy decoding on an input bitstream. For example, for entropy decoding, various methods such as Exponential Golomb, Context-Adaptive Variable Length Coding (CAVLC), and Context-Adaptive Binary Arithmetic Coding (CABAC) may be applied.
  • the geometric information entropy decoder may decode information related to geometric information prediction performed by the encoding apparatus. Quantized residual geometric information generated through entropy decoding may be input to the residual geometric information inverse quantization unit.
  • the residual geometric information inverse quantization unit may perform inverse quantization based on a quantization parameter and the received quantized residual geometric information to generate residual geometric information or geometric information.
  • the geometric information prediction unit may generate predicted geometric information based on information related to generation of predicted geometric information provided from the geometric information entropy decoder and previously decoded geometric information provided from a memory.
  • the geometric information prediction unit may include an inter prediction unit and an intra prediction unit.
  • the inter prediction unit uses information required for inter prediction of the current prediction unit provided by the encoding device, and determines the current prediction unit based on information included in at least one of a space before or after the current space including the current prediction unit. Inter prediction can be performed.
  • the intra prediction unit may generate predicted geometric information based on geometric information of a point in the current space.
  • intra prediction may be performed based on intra prediction mode information of the prediction unit provided by the encoding device.
  • the reconstructed geometric information may be generated by adding the reconstructed residual geometric information to the predicted geometric information.
  • the reconstructed geometric information according to the embodiments may be provided to the filtering unit.
  • the filtering unit may perform filtering based on the filtering-related information provided from the decoder or the characteristics of the reconstructed geometric information derived from the decoder.
  • the memory may store the reconstructed geometric information calculated through the filtering unit.
  • the inverse coordinate system transform unit may perform inverse coordinate system transformation based on information related to coordinate system transformation provided from the geometric information entropy decoding unit and restored geometric information stored in a memory.
  • 29 shows an example of occupancy derivation according to embodiments.
  • the method/apparatus according to the embodiments performs accupancy delivery. Can be done.
  • the method/apparatus according to the embodiments may perform the following process.
  • the method/apparatus determines whether each child node of the current node is included in the bounding box (subbounding box) when the current node is not completely included in the bounding box (subbounding box). Check, and encode/decode only the accupancy of child nodes that overlap with the bounding box (subbounding box).
  • the method/apparatus according to the embodiments has an effect of removing unnecessary zero-acupancy signaling when an empty area occupies space in relation to a bounding box (subbounding box).
  • FIG. 30 shows signaling information related to a geometry node according to embodiments.
  • Point cloud data may include signaling information related to a geometry node.
  • single_occupancy_flag represents a flag for single accupancy.
  • occupancy_idx represents identification information of accupancy.
  • the method/apparatus according to the embodiments refers to the accupant identification information.
  • overlapped_occupancy indicates a bitmap that identifies overlapped with contents bounding box and occupied child nodes of the current node, which overlap with the point cloud content bounding box.
  • the method/apparatus according to the embodiments may refer to this value when the current node is not completely included in the bounding box (subbounding box).
  • occupancy_map represents an accupancy map.
  • the method/apparatus according to the embodiments refers to the accufancy map.
  • occupancy_byte represents byte information during accupancy.
  • num_points_eq1_flag represents flag information related to the number of points.
  • num_points_minus2 represents information related to the number of points.
  • direct_mode_flag represents flag information related to the direct mode.
  • num_direct_points_minus1 represents information related to the number of direct points.
  • point_rem_x[ i ][ j ] represent the x, y, and z information of the point.
  • point_rem_y[ i ][ j ] represent the x, y, and z information of the point.
  • point_rem_z[ i ][ j] represent the x, y, and z information of the point.
  • the method/apparatus according to the embodiments may perform a space division operation, and may efficiently encode/decode point cloud data without wasting space.
  • 31 illustrates a method of transmitting point cloud data according to embodiments.
  • Point cloud data transmission method acquiring point cloud data; (S31001) encoding point cloud data; And/or (S31002) transmitting point cloud data; It may include.
  • the method/apparatus according to the embodiments may acquire point cloud data.
  • the process of obtaining point cloud data according to embodiments may include the process described in FIGS. 1 to 3, and the like.
  • the method/apparatus according to the embodiments may encode point cloud data.
  • the process of encoding point cloud data according to embodiments may include the processes described in FIGS. 1 to 2, 3, 5-9, 12, 18 to 23, and the like.
  • the method/apparatus according to the embodiments may transmit point cloud data.
  • the process of transmitting point cloud data according to embodiments may include the process described in FIGS. 1 to 2, 23, 14-16, and the like.
  • the point cloud data transmission method/apparatus according to the embodiments can be combined with the above-described embodiments, and divides the geometric information of the point cloud to effectively compress the geometric information of the points constituting the point cloud data. It can provide an effect that can effectively predict this.
  • the method/apparatus for transmitting point cloud data according to the embodiments is an accue indicating that points do not exist for an area where a point does not exist in the oct tree in consideration of the presence area and/or the non-existence area when points are distributed in space.
  • Fansi information can be transmitted, and data transmission efficiency can be increased based on a bounding box (or subbounding box).
  • 32 illustrates a method of receiving point cloud data according to embodiments.
  • a method for receiving point cloud data includes (S32000) receiving point cloud data; (S32001) decoding the point cloud data; And/or (S32002) rendering the point cloud data; It may include.
  • the method/apparatus according to the embodiments may receive point cloud data.
  • the process of receiving point cloud data according to embodiments may include the process described in FIGS. 1 to 2, 10-11, 13-16, and the like.
  • the method/apparatus according to the embodiments may decode the point cloud data.
  • the decoding process of point cloud data according to embodiments may include the process described in FIGS. 1-2, 5-9, 10-11, 13, 18, 21-28, and the like.
  • the method/apparatus according to the embodiments may render point cloud data.
  • the rendering process according to embodiments may include the process described in FIGS. 1-2, 23, 14-16, and the like.
  • Each of the above-described parts, modules or units may be software, processor, or hardware parts that execute successive processes stored in a memory (or storage unit). Each of the steps described in the above-described embodiment may be performed by processor, software, and hardware parts. Each module/block/unit described in the above-described embodiment may operate as a processor, software, or hardware. In addition, methods suggested by the embodiments may be executed as code. This code can be written to a storage medium that can be read by the processor, and thus can be read by a processor provided by the apparatus.
  • the processor-readable recording medium includes all types of recording devices that store data that can be read by the processor. Examples of recording media that can be read by the processor include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, etc., and also include those implemented in the form of carrier waves such as transmission through the Internet. . Further, the processor-readable recording medium is distributed over a computer system connected through a network, so that the processor-readable code can be stored and executed in a distributed manner.
  • Various elements of the embodiments may be performed by hardware, software, firmware, or a combination thereof.
  • Various elements of the embodiments may be implemented on a single chip such as a hardware circuit.
  • the embodiments may optionally be performed on individual needles.
  • at least one of the elements of the embodiments may be executed in one or more processors including instructions for performing operations according to the embodiments.
  • first and second are used to describe various elements of the embodiments. These terms do not limit the interpretation of the elements of the embodiments. These terms are used to distinguish between one element and another.
  • a first user input signal may be referred to as a second user input signal.
  • the second user input signal may be referred to as a first user input signal.
  • Both the first user input signal and the second user input signal are user input signals, and do not mean the same user input signals unless clearly indicated in context.
  • Conditional expressions such as when, when, and when used to describe the embodiments are not limited to an optional case. When a specific condition is satisfied, it is intended to perform a related operation in response to a specific condition or to interpret the related definition.
  • the embodiments may be applied wholly or partially to the point cloud data transmission/reception apparatus and system.
  • Embodiments may include changes/modifications, and changes/modifications do not depart from the scope of the claims and the same.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

Selon des modes de réalisation, l'invention concerne un procédé de transmission de données de nuage de points qui peut comprendre les étapes consistant à: coder des données de nuage de points ; et/ou transmettre un train de bits comprenant les données de nuage de points. De plus, un procédé de réception de données de nuage de points selon des modes de réalisation peut comprendre les étapes consistant à: recevoir des données de nuage de points; décoder les données de nuage de points; et restituer les données de nuage de points.
PCT/KR2020/001615 2019-03-15 2020-02-04 Appareil de transmission de données de nuage de points, procédé de transmission de données de nuage de points, appareil de réception de données de nuage de points, et procédé de réception de données de nuage de points Ceased WO2020189891A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962819437P 2019-03-15 2019-03-15
US62/819,437 2019-03-15

Publications (1)

Publication Number Publication Date
WO2020189891A1 true WO2020189891A1 (fr) 2020-09-24

Family

ID=72519143

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2020/001615 Ceased WO2020189891A1 (fr) 2019-03-15 2020-02-04 Appareil de transmission de données de nuage de points, procédé de transmission de données de nuage de points, appareil de réception de données de nuage de points, et procédé de réception de données de nuage de points

Country Status (1)

Country Link
WO (1) WO2020189891A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3792875A4 (fr) * 2018-05-11 2021-06-30 Panasonic Intellectual Property Corporation of America Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
WO2022092891A1 (fr) * 2020-10-30 2022-05-05 엘지전자 주식회사 Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2022258010A1 (fr) * 2021-06-11 2022-12-15 维沃移动通信有限公司 Procédé et appareil de traitement de codage de nuage de points, et procédé et appareil de traitement de décodage de nuage de points

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106846425A (zh) * 2017-01-11 2017-06-13 东南大学 一种基于八叉树的散乱点云压缩方法
US9734595B2 (en) * 2014-09-24 2017-08-15 University of Maribor Method and apparatus for near-lossless compression and decompression of 3D meshes and point clouds
KR101798132B1 (ko) * 2016-12-26 2017-11-16 한국생산기술연구원 로봇의 충돌 검출 고속화를 위한 작업 환경 모델링 장치 및 방법
WO2019014078A2 (fr) * 2017-07-12 2019-01-17 Topcon Positioning Systems, Inc Procédé et appareil pour nuage de points
WO2019012975A1 (fr) * 2017-07-10 2019-01-17 ソニー株式会社 Dispositif et procédé de traitement d'informations

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9734595B2 (en) * 2014-09-24 2017-08-15 University of Maribor Method and apparatus for near-lossless compression and decompression of 3D meshes and point clouds
KR101798132B1 (ko) * 2016-12-26 2017-11-16 한국생산기술연구원 로봇의 충돌 검출 고속화를 위한 작업 환경 모델링 장치 및 방법
CN106846425A (zh) * 2017-01-11 2017-06-13 东南大学 一种基于八叉树的散乱点云压缩方法
WO2019012975A1 (fr) * 2017-07-10 2019-01-17 ソニー株式会社 Dispositif et procédé de traitement d'informations
WO2019014078A2 (fr) * 2017-07-12 2019-01-17 Topcon Positioning Systems, Inc Procédé et appareil pour nuage de points

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3792875A4 (fr) * 2018-05-11 2021-06-30 Panasonic Intellectual Property Corporation of America Procédé de codage de données tridimensionnelles, procédé de décodage de données tridimensionnelles, dispositif de codage de données tridimensionnelles et dispositif de décodage de données tridimensionnelles
US11989921B2 (en) 2018-05-11 2024-05-21 Panasonic Intellectual Property Corporation Of America Three-dimensional data encoding method, three-dimensional data decoding method, three-dimensional data encoding device, and three-dimensional data decoding device
WO2022092891A1 (fr) * 2020-10-30 2022-05-05 엘지전자 주식회사 Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
US12260600B2 (en) 2020-10-30 2025-03-25 Lg Electronics Inc. Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method
WO2022258010A1 (fr) * 2021-06-11 2022-12-15 维沃移动通信有限公司 Procédé et appareil de traitement de codage de nuage de points, et procédé et appareil de traitement de décodage de nuage de points
US12470721B2 (en) 2021-06-11 2025-11-11 Vivo Mobile Communication Co., Ltd. Point cloud encoding processing method and apparatus and point cloud decoding processing method and apparatus

Similar Documents

Publication Publication Date Title
WO2020189976A1 (fr) Appareil et procédé permettant de traiter des données de nuage de points
WO2021002604A1 (fr) Procédé et appareil de traitement de données de nuages de points
WO2020242244A1 (fr) Procédé et dispositif pour traiter des données de nuage de points
WO2020190093A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2020256308A1 (fr) Dispositif et procédé de traitement de données de nuage de points
WO2020190090A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points, et procédé de réception de données de nuage de points
WO2020189982A1 (fr) Dispositif et procédé de traitement de données de nuage de points
WO2020197086A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et/ou procédé de réception de données de nuage de points
WO2020246689A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points, et procédé de réception de données de nuage de points
WO2021002558A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021206291A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission, dispositif de traitement et procédé de traitement
WO2021045601A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021060850A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2020242077A1 (fr) Appareil et procédé pour traiter des données de nuage de points
WO2021002594A1 (fr) Appareil et procédé de traitement de données de nuage de points
WO2020262831A1 (fr) Appareil et procédé pour traiter des données de nuage de points
WO2021002592A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021002665A1 (fr) Dispositif et procédé de traitement de données de nuage de points
WO2021029511A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2020189943A1 (fr) Dispositif d'émission de données de nuage de points, procédé d'émission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021002636A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021145573A1 (fr) Appareil et procédé de traitement de données de nuage de points
WO2021242036A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021045603A1 (fr) Dispositif de transmission de données de nuage de points, procédé de transmission de données de nuage de points, dispositif de réception de données de nuage de points et procédé de réception de données de nuage de points
WO2021201384A1 (fr) Appareil et procédé permettant de traiter des données de nuage de points

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20774383

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20774383

Country of ref document: EP

Kind code of ref document: A1