Patent ID: 12250414

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. The detailed description, which will be given below with reference to the accompanying drawings, is intended to explain exemplary embodiments of the present disclosure, rather than to show the only embodiments that may be implemented according to the present disclosure. The following detailed description includes specific details in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without such specific details.

Although most terms used in the present disclosure have been selected from general ones widely used in the art, some terms have been arbitrarily selected by the applicant and their meanings are explained in detail in the following description as needed. Thus, the present disclosure should be understood based upon the intended meanings of the terms rather than their simple names or meanings.

FIG.1shows an exemplary point cloud content providing system according to embodiments.

The point cloud content providing system illustrated inFIG.1may include a transmission device10000and a reception device10004. The transmission device10000and the reception device10004are capable of wired or wireless communication to transmit and receive point cloud data.

The point cloud data transmission device10000according to the embodiments may secure and process point cloud video (or point cloud content) and transmit the same. According to embodiments, the transmission device10000may include a fixed station, a base transceiver system (BTS), a network, an artificial intelligence (AI) device and/or system, a robot, an AR/VR/XR device and/or server. According to embodiments, the transmission device10000may include a device, a robot, a vehicle, an AR/VR/XR device, a portable device, a home appliance, an Internet of Thing (IoT) device, and an AI device/server which are configured to perform communication with a base station and/or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The transmission device10000according to the embodiments includes a point cloud video acquirer10001, a point cloud video encoder10002, and/or a transmitter (or communication module)10003.

The point cloud video acquirer10001according to the embodiments acquires a point cloud video through a processing process such as capture, synthesis, or generation. The point cloud video is point cloud content represented by a point cloud, which is a set of points positioned in a 3D space, and may be referred to as point cloud video data. The point cloud video according to the embodiments may include one or more frames. One frame represents a still image/picture. Therefore, the point cloud video may include a point cloud image/frame/picture, and may be referred to as a point cloud image, frame, or picture.

The point cloud video encoder10002according to the embodiments encodes the acquired point cloud video data. The point cloud video encoder10002may encode the point cloud video data based on point cloud compression coding. The point cloud compression coding according to the embodiments may include geometry-based point cloud compression (G-PCC) coding and/or video-based point cloud compression (V-PCC) coding or next-generation coding. The point cloud compression coding according to the embodiments is not limited to the above-described embodiment. The point cloud video encoder10002may output a bitstream containing the encoded point cloud video data. The bitstream may contain not only the encoded point cloud video data, but also signaling information related to encoding of the point cloud video data.

The transmitter10003according to the embodiments transmits the bitstream containing the encoded point cloud video data. The bitstream according to the embodiments is encapsulated in a file or segment (for example, a streaming segment), and is transmitted over various networks such as a broadcasting network and/or a broadband network. Although not shown in the figure, the transmission device10000may include an encapsulator (or an encapsulation module) configured to perform an encapsulation operation. According to embodiments, the encapsulator may be included in the transmitter10003. According to embodiments, the file or segment may be transmitted to the reception device10004over a network, or stored in a digital storage medium (e.g., USB, SD, CD, DVD, Blu-ray, HDD, SSD, etc.). The transmitter10003according to the embodiments is capable of wired/wireless communication with the reception device10004(or the receiver10005) over a network of 4G, 5G, 6G, etc. In addition, the transmitter may perform a necessary data processing operation according to the network system (e.g., a 4G, 5G or 6G communication network system). The transmission device10000may transmit the encapsulated data in an on-demand manner.

The reception device10004according to the embodiments includes a receiver10005, a point cloud video decoder10006, and/or a renderer10007. According to embodiments, the reception device10004may include a device, a robot, a vehicle, an AR/VR/XR device, a portable device, a home appliance, an Internet of Things (IoT) device, and an AI device/server which are configured to perform communication with a base station and/or other wireless devices using a radio access technology (e.g., 5G New RAT (NR), Long Term Evolution (LTE)).

The receiver10005according to the embodiments receives the bitstream containing the point cloud video data or the file/segment in which the bitstream is encapsulated from the network or storage medium. The receiver10005may perform necessary data processing according to the network system (for example, a communication network system of 4G, 5G, 6G, etc.). The receiver10005according to the embodiments may decapsulate the received file/segment and output a bitstream. According to embodiments, the receiver10005may include a decapsulator (or a decapsulation module) configured to perform a decapsulation operation. The decapsulator may be implemented as an element (or component) separate from the receiver10005.

The point cloud video decoder10006decodes the bitstream containing the point cloud video data. The point cloud video decoder10006may decode the point cloud video data according to the method by which the point cloud video data is encoded (for example, in a reverse process of the operation of the point cloud video encoder10002). Accordingly, the point cloud video decoder10006may decode the point cloud video data by performing point cloud decompression coding, which is the inverse process of the point cloud compression. The point cloud decompression coding includes G-PCC coding.

The renderer10007renders the decoded point cloud video data. The renderer10007may output point cloud content by rendering not only the point cloud video data but also audio data. According to embodiments, the renderer10007may include a display configured to display the point cloud content. According to embodiments, the display may be implemented as a separate device or component rather than being included in the renderer10007.

The arrows indicated by dotted lines in the drawing represent a transmission path of feedback information acquired by the reception device10004. The feedback information is information for reflecting interactivity with a user who consumes the point cloud content, and includes information about the user (e.g., head orientation information, viewport information, and the like). In particular, when the point cloud content is content for a service (e.g., self-driving service, etc.) that requires interaction with the user, the feedback information may be provided to the content transmitting side (e.g., the transmission device10000) and/or the service provider. According to embodiments, the feedback information may be used in the reception device10004as well as the transmission device10000, or may not be provided.

The head orientation information according to embodiments is information about the user's head position, orientation, angle, motion, and the like. The reception device10004according to the embodiments may calculate the viewport information based on the head orientation information. The viewport information may be information about a region of a point cloud video that the user is viewing. A viewpoint is a point through which the user is viewing the point cloud video, and may refer to a center point of the viewport region. That is, the viewport is a region centered on the viewpoint, and the size and shape of the region may be determined by a field of view (FOV). Accordingly, the reception device10004may extract the viewport information based on a vertical or horizontal FOV supported by the device in addition to the head orientation information. Also, the reception device10004performs gaze analysis or the like to check the way the user consumes a point cloud, a region that the user gazes at in the point cloud video, a gaze time, and the like. According to embodiments, the reception device10004may transmit feedback information including the result of the gaze analysis to the transmission device10000. The feedback information according to the embodiments may be acquired in the rendering and/or display process. The feedback information according to the embodiments may be secured by one or more sensors included in the reception device10004. According to embodiments, the feedback information may be secured by the renderer10007or a separate external element (or device, component, or the like). The dotted lines inFIG.1represent a process of transmitting the feedback information secured by the renderer10007. The point cloud content providing system may process (encode/decode) point cloud data based on the feedback information. Accordingly, the point cloud video data decoder10006may perform a decoding operation based on the feedback information. The reception device10004may transmit the feedback information to the transmission device10000. The transmission device10000(or the point cloud video data encoder10002) may perform an encoding operation based on the feedback information. Accordingly, the point cloud content providing system may efficiently process necessary data (e.g., point cloud data corresponding to the user's head position) based on the feedback information rather than processing (encoding/decoding) the entire point cloud data, and provide point cloud content to the user.

According to embodiments, the transmission device10000may be called an encoder, a transmission device, a transmitter, or the like, and the reception device10004may be called a decoder, a receiving device, a receiver, or the like.

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

The elements of the point cloud content providing system illustrated inFIG.1may be implemented by hardware, software, a processor, and/or a combination thereof.

FIG.2is a block diagram illustrating a point cloud content providing operation according to embodiments.

The block diagram ofFIG.2shows the operation of the point cloud content providing system described inFIG.1. As described above, the point cloud content providing system may process point cloud data based on point cloud compression coding (e.g., G-PCC).

The point cloud content providing system according to the embodiments (for example, the point cloud transmission device10000or the point cloud video acquirer10001) may acquire a point cloud video (20000). The point cloud video is represented by a point cloud belonging to a coordinate system for expressing a 3D space. The point cloud video according to the embodiments may include a Ply (Polygon File format or the Stanford Triangle format) file. When the point cloud video has one or more frames, the acquired point cloud video may include one or more Ply files. The Ply files contain point cloud data, such as point geometry and/or attributes. The geometry includes positions of points. The position of each point may be represented by parameters (for example, values of the X, Y, and Z axes) representing a three-dimensional coordinate system (e.g., a coordinate system composed of X, Y and Z axes). The attributes include attributes of points (e.g., information about texture, color (in YCbCr or RGB), reflectance r, transparency, etc. of each point). A point has one or more attributes. For example, a point may have an attribute that is a color, or two attributes that are color and reflectance. According to embodiments, the geometry may be called positions, geometry information, geometry data, or the like, and the attribute may be called attributes, attribute information, attribute data, or the like. The point cloud content providing system (for example, the point cloud transmission device10000or the point cloud video acquirer10001) may secure point cloud data from information (e.g., depth information, color information, etc.) related to the acquisition process of the point cloud video.

The point cloud content providing system (for example, the transmission device10000or the point cloud video encoder10002) according to the embodiments may encode the point cloud data (20001). The point cloud content providing system may encode the point cloud data based on point cloud compression coding. As described above, the point cloud data may include the geometry and attributes of a point. Accordingly, the point cloud content providing system may perform geometry encoding of encoding the geometry and output a geometry bitstream. The point cloud content providing system may perform attribute encoding of encoding attributes and output an attribute bitstream. According to embodiments, the point cloud content providing system may perform the attribute encoding based on the 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 the embodiments may further contain signaling information related to the geometry encoding and attribute encoding.

The point cloud content providing system (for example, the transmission device10000or the transmitter10003) according to the embodiments may transmit the encoded point cloud data (20002). As illustrated inFIG.1, the encoded point cloud data may be represented by a geometry bitstream and an attribute bitstream. In addition, 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 (for example, signaling information related to the geometry encoding and the attribute encoding). The point cloud content providing system may encapsulate a bitstream that carries the encoded point cloud data and transmit the same in the form of a file or segment.

The point cloud content providing system (for example, the reception device10004or the receiver10005) according to the embodiments may receive the bitstream containing the encoded point cloud data. In addition, the point cloud content providing system (for example, the reception device10004or the receiver10005) may demultiplex the bitstream.

The point cloud content providing system (e.g., the reception device10004or the point cloud video decoder10005) may decode the encoded point cloud data (e.g., the geometry bitstream, the attribute bitstream) transmitted in the bitstream. The point cloud content providing system (for example, the reception device10004or the point cloud video decoder10005) may decode the point cloud video data based on the signaling information related to encoding of the point cloud video data contained in the bitstream. The point cloud content providing system (for example, the reception device10004or the point cloud video decoder10005) may decode the geometry bitstream to reconstruct the positions (geometry) of points. The point cloud content providing system may reconstruct the attributes of the points by decoding the attribute bitstream based on the reconstructed geometry. The point cloud content providing system (for example, the reception device10004or the point cloud video decoder10005) may reconstruct the point cloud video based on the positions according to the reconstructed geometry and the decoded attributes.

The point cloud content providing system according to the embodiments (for example, the reception device10004or the renderer10007) may render the decoded point cloud data (20004). The point cloud content providing system (for example, the reception device10004or the renderer10007) may render the geometry and attributes decoded through the decoding process, using various rendering methods. Points in the point cloud content may be rendered to a vertex having a certain thickness, a cube having a specific minimum size centered on the corresponding vertex position, or a circle centered on the corresponding vertex position. All or part of the rendered point cloud content is provided to the user through a display (e.g., a VR/AR display, a general display, etc.).

The point cloud content providing system (e.g., the reception device10004) according to the embodiments may secure feedback information (20005). The point cloud content providing system may encode and/or decode point cloud data based on the feedback information. The feedback information and the operation of the point cloud content providing system according to the embodiments are the same as the feedback information and the operation described with reference toFIG.1, and thus detailed description thereof is omitted.

FIG.3illustrates an exemplary process of capturing a point cloud video according to embodiments.

FIG.3illustrates an exemplary point cloud video capture process of the point cloud content providing system described with reference toFIGS.1to2.

Point cloud content includes a point cloud video (images and/or videos) representing an object and/or environment located in various 3D spaces (e.g., a 3D space representing a real environment, a 3D space representing a virtual environment, etc.). Accordingly, the point cloud content providing system according to the embodiments may capture a point cloud video using one or more cameras (e.g., an infrared camera capable of securing depth information, an RGB camera capable of extracting color information corresponding to the depth information, etc.), a projector (e.g., an infrared pattern projector to secure depth information), a LiDAR, or the like. The point cloud content providing system according to the embodiments may extract the shape of geometry composed of points in a 3D space from the depth information and extract the attributes of each point from the color information to secure point cloud data. An image and/or video according to the embodiments may be captured based on at least one of the inward-facing technique and the outward-facing technique.

The left part ofFIG.3illustrates the inward-facing technique. The inward-facing technique refers to a technique of capturing images a central object with one or more cameras (or camera sensors) positioned around the central object. The inward-facing technique may be used to generate point cloud content providing a 360-degree image of a key object to the user (e.g., VR/AR content providing a 360-degree image of an object (e.g., a key object such as a character, player, object, or actor) to the user).

The right part ofFIG.3illustrates the outward-facing technique. The outward-facing technique refers to a technique of capturing images an environment of a central object rather than the central object with one or more cameras (or camera sensors) positioned around the central object. The outward-facing technique may be used to generate point cloud content for providing a surrounding environment that appears from the user's point of view (e.g., content representing an external environment that may be provided to a user of a self-driving vehicle).

As shown in the figure, the point cloud content may be generated based on the capturing operation of one or more cameras. In this case, the coordinate system may differ among the cameras, and accordingly the point cloud content providing system may calibrate one or more cameras to set a global coordinate system before the capturing operation. In addition, the point cloud content providing system may generate point cloud content by synthesizing an arbitrary image and/or video with an image and/or video captured by the above-described capture technique. The point cloud content providing system may not perform the capturing operation described inFIG.3when it generates point cloud content representing a virtual space. The point cloud content providing system according to the embodiments may perform post-processing on the captured image and/or video. In other words, the point cloud content providing system may remove an unwanted area (for example, a background), recognize a space to which the captured images and/or videos are connected, and, when there is a spatial hole, perform an operation of filling the spatial hole.

The point cloud content providing system may generate one piece of point cloud content by performing coordinate transformation on points of the point cloud video secured from each camera. The point cloud content providing system may perform coordinate transformation on the points based on the coordinates of the position of each camera. Accordingly, the point cloud content providing system may generate content representing one wide range, or may generate point cloud content having a high density of points.

FIG.4illustrates an exemplary point cloud encoder according to embodiments.

FIG.4shows an example of the point cloud video encoder10002ofFIG.1. The point cloud encoder reconstructs and encodes point cloud data (e.g., positions and/or attributes of the points) to adjust the quality of the point cloud content (to, for example, lossless, lossy, or near-lossless) according to the network condition or applications. When the overall size of the point cloud content is large (e.g., point cloud content of 60 Gbps is given for 30 fps), the point cloud content providing system may fail to stream the content in real time. Accordingly, the point cloud content providing system may reconstruct the point cloud content based on the maximum target bitrate to provide the same in accordance with the network environment or the like.

As described with reference toFIGS.1and2, the point cloud encoder may perform geometry encoding and attribute encoding. The geometry encoding is performed before the attribute encoding.

The point cloud encoder according to the embodiments includes a coordinate transformer (Transform coordinates)40000, a quantizer (Quantize and remove points (voxelize))40001, an octree analyzer (Analyze octree)40002, and a surface approximation analyzer (Analyze surface approximation)40003, an arithmetic encoder (Arithmetic encode)40004, a geometry reconstructor (Reconstruct geometry)40005, a color transformer (Transform colors)40006, an attribute transformer (Transform attributes)40007, a RAHT transformer (RAHT)40008, an LOD generator (Generate LOD)40009, a lifting transformer (Lifting)40010, a coefficient quantizer (Quantize coefficients)40011, and/or an arithmetic encoder (Arithmetic encode)40012.

The coordinate transformer40000, the quantizer40001, the octree analyzer40002, the surface approximation analyzer40003, the arithmetic encoder40004, and the geometry reconstructor40005may perform geometry encoding. The geometry encoding according to the embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. The direct coding and trisoup the geometry encoding are applied selectively or in combination. The geometry encoding is not limited to the above-described example.

As shown in the figure, the coordinate transformer40000according to the embodiments receives positions and transforms the same into coordinates. For example, the positions may be transformed into position information in a three-dimensional space (for example, a three-dimensional space represented by an XYZ coordinate system). The position information in the three-dimensional space according to the embodiments may be referred to as geometry information.

The quantizer40001according to the embodiments quantizes the geometry. For example, the quantizer40001may quantize the points based on a minimum position value of all points (for example, a minimum value on each of the X, Y, and Z axes). The quantizer40001performs a quantization operation of multiplying the difference between the minimum position value and the position value of each point by a preset quantization scale value and then finding the nearest integer value by rounding the value obtained through the multiplication. Thus, one or more points may have the same quantized position (or position value). The quantizer40001according to the embodiments performs voxelization based on the quantized positions to reconstruct quantized points. As in the case of a pixel, which is the minimum unit containing 2D image/video information, points of point cloud content (or 3D point cloud video) according to the embodiments may be included in one or more voxels. The term voxel, which is a compound of volume and pixel, refers to a 3D cubic space generated when a 3D space is divided into units (unit=1.0) based on the axes representing the 3D space (e.g., X-axis, Y-axis, and Z-axis). The quantizer40001may match groups of points in the 3D space with voxels. According to embodiments, one voxel may include only one point. According to embodiments, one voxel may include one or more points. In order to express one voxel as one point, the position of the center of a voxel may be set based on the positions of one or more points included in the voxel. In this case, attributes of all positions included in one voxel may be combined and assigned to the voxel.

The octree analyzer40002according to the embodiments performs octree geometry coding (or octree coding) to present voxels in an octree structure. The octree structure represents points matched with voxels, based on the octal tree structure.

The surface approximation analyzer40003according to the embodiments may analyze and approximate the octree. The octree analysis and approximation according to the embodiments is a process of analyzing a region containing a plurality of points to efficiently provide octree and voxelization.

The arithmetic encoder40004according to the embodiments performs entropy encoding on the octree and/or the approximated octree. For example, the encoding scheme includes arithmetic encoding. As a result of the encoding, a geometry bitstream is generated.

The color transformer40006, the attribute transformer40007, the RAHT transformer40008, the LOD generator40009, the lifting transformer40010, the coefficient quantizer40011, and/or the arithmetic encoder40012perform attribute encoding. As described above, one point may have one or more attributes. The attribute encoding according to the embodiments is equally applied to the attributes that one point has. However, when an attribute (e.g., color) includes one or more elements, attribute encoding is independently applied to each element. The attribute encoding according to the embodiments includes color transform coding, attribute transform coding, region adaptive hierarchical transform (RAHT) coding, interpolation-based hierarchical nearest-neighbor prediction (prediction transform) coding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (lifting transform) coding. Depending on the point cloud content, the RAHT coding, the prediction transform coding and the lifting transform coding described above may be selectively used, or a combination of one or more of the coding schemes may be used. The attribute encoding according to the embodiments is not limited to the above-described example.

The color transformer40006according to the embodiments performs color transform coding of transforming color values (or textures) included in the attributes. For example, the color transformer40006may transform the format of color information (for example, from RGB to YCbCr). The operation of the color transformer40006according to embodiments may be optionally applied according to the color values included in the attributes.

The geometry reconstructor40005according to the embodiments reconstructs (decompresses) the octree and/or the approximated octree. The geometry reconstructor40005reconstructs the octree/voxels based on the result of analyzing the distribution of points. The reconstructed octree/voxels may be referred to as reconstructed geometry (restored geometry).

The attribute transformer40007according to the embodiments performs attribute transformation to transform the attributes based on the reconstructed geometry and/or the positions on which geometry encoding is not performed. As described above, since the attributes are dependent on the geometry, the attribute transformer40007may transform the attributes based on the reconstructed geometry information. For example, based on the position value of a point included in a voxel, the attribute transformer40007may transform the attribute of the point at the position. As described above, when the position of the center of a voxel is set based on the positions of one or more points included in the voxel, the attribute transformer40007transforms the attributes of the one or more points. When the trisoup geometry encoding is performed, the attribute transformer40007may transform the attributes based on the trisoup geometry encoding.

The attribute transformer40007may perform the attribute transformation by calculating the average of attributes or attribute values of neighboring points (e.g., color or reflectance of each point) within a specific position/radius from the position (or position value) of the center of each voxel. The attribute transformer40007may apply a weight according to the distance from the center to each point in calculating the average. Accordingly, each voxel has a position and a calculated attribute (or attribute value).

The attribute transformer40007may search for neighboring points existing within a specific position/radius from the position of the center of each voxel based on the K-D tree or the Morton code. The K-D tree is a binary search tree and supports a data structure capable of managing points based on the positions such that nearest neighbor search (NNS) can be performed quickly. The Morton code is generated by presenting coordinates (e.g., (x, y, z)) representing 3D positions of all points as bit values and mixing the bits. For example, when the coordinates representing the position of a point are (5, 9, 1), the bit values for the coordinates are (0101, 1001, 0001). Mixing the bit values according to the bit index in order of z, y, and x yields 010001000111. This value is expressed as a decimal number of 1095. That is, the Morton code value of the point having coordinates (5, 9, 1) is 1095. The attribute transformer40007may order the points based on the Morton code values and perform NNS through a depth-first traversal process. After the attribute transformation operation, the K-D tree or the Morton code is used when the NNS is needed in another transformation process for attribute coding.

As shown in the figure, the transformed attributes are input to the RAHT transformer40008and/or the LOD generator40009.

The RAHT transformer40008according to the embodiments performs RAHT coding for predicting attribute information based on the reconstructed geometry information. For example, the RAHT transformer40008may predict attribute information of a node at a higher level in the octree based on the attribute information associated with a node at a lower level in the octree.

The LOD generator40009according to the embodiments generates a level of detail (LOD) to perform prediction transform coding. The LOD according to the embodiments is a degree of detail of point cloud content. As the LOD value decrease, it indicates that the detail of the point cloud content is degraded. As the LOD value increases, it indicates that the detail of the point cloud content is enhanced. Points may be classified by the LOD.

The lifting transformer40010according to the embodiments performs lifting transform coding of transforming the attributes a point cloud based on weights. As described above, lifting transform coding may be optionally applied.

The coefficient quantizer40011according to the embodiments quantizes the attribute-coded attributes based on coefficients.

The arithmetic encoder40012according to the embodiments encodes the quantized attributes based on arithmetic coding.

Although not shown in the figure, the elements of the point cloud encoder ofFIG.4may be implemented by hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, software, firmware, or a combination thereof. The one or more processors may perform at least one of the operations and/or functions of the elements of the point cloud encoder ofFIG.4described above. Additionally, the one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud encoder ofFIG.4. The one or more memories according to the embodiments may include a high speed random access memory, or include a non-volatile memory (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

FIG.5shows an example of voxels according to embodiments.

FIG.5shows voxels positioned in a 3D space represented by a coordinate system composed of three axes, which are the X-axis, the Y-axis, and the Z-axis. As described with reference toFIG.4, the point cloud encoder (e.g., the quantizer40001) may perform voxelization. Voxel refers to a 3D cubic space generated when a 3D space is divided into units (unit=1.0) based on the axes representing the 3D space (e.g., X-axis, Y-axis, and Z-axis).FIG.5shows an example of voxels generated through an octree structure in which a cubical axis-aligned bounding box defined by two poles (0, 0, 0) and (2d, 2d, 2d) is recursively subdivided. One voxel includes at least one point. The spatial coordinates of a voxel may be estimated from the positional relationship with a voxel group. As described above, a voxel has an attribute (such as color or reflectance) like pixels of a 2D image/video. The details of the voxel are the same as those described with reference toFIG.4, and therefore a description thereof is omitted.

FIG.6shows an example of an octree and occupancy code according to embodiments.

As described with reference toFIGS.1to4, the point cloud content providing system (point cloud video encoder10002) or the point cloud encoder (for example, the octree analyzer40002) performs octree geometry coding (or octree coding) based on an octree structure to efficiently manage the region and/or position of the voxel.

The upper part ofFIG.6shows an octree structure. The 3D space of the point cloud content according to the embodiments is represented by axes (e.g., X-axis, Y-axis, and Z-axis) of the coordinate system. The octree structure is created by recursive subdividing of a cubical axis-aligned bounding box defined by two poles (0, 0, 0) and (2d, 2d, 2d). Here, 2d may be set to a value constituting the smallest bounding box surrounding all points of the point cloud content (or point cloud video). Here, d denotes the depth of the octree. The value of d is determined in the following equation. In the following equation, (xintn, yintn, zintn) denotes the positions (or position values) of quantized points.
d=Ceil(Log 2(Max(xnint,ynint,znint,n=1, . . . ,N)+1))

As shown in the middle of the upper part ofFIG.6, the entire 3D space may be divided into eight spaces according to partition. Each divided space is represented by a cube with six faces. As shown in the upper right ofFIG.6, each of the eight spaces is divided again based on the axes of the coordinate system (e.g., X-axis, Y-axis, and Z-axis). Accordingly, each space is divided into eight smaller spaces. The divided smaller space is also represented by a cube with six faces. This partitioning scheme is applied until the leaf node of the octree becomes a voxel.

The lower part ofFIG.6shows an octree occupancy code. The occupancy code of the octree is generated to indicate whether each of the eight divided spaces generated by dividing one space contains at least one point. Accordingly, a single occupancy code is represented by eight child nodes. Each child node represents the occupancy of a divided space, and the child node has a value in 1 bit. Accordingly, the occupancy code is represented as an 8-bit code. That is, when at least one point is contained in the space corresponding to a child node, the node is assigned a value of 1. When no point is contained in the space corresponding to the child node (the space is empty), the node is assigned a value of 0. Since the occupancy code shown inFIG.6is 00100001, it indicates that the spaces corresponding to the third child node and the eighth child node among the eight child nodes each contain at least one point. As shown in the figure, each of the third child node and the eighth child node has eight child nodes, and the child nodes are represented by an 8-bit occupancy code. The figure shows that the occupancy code of the third child node is 10000111, and the occupancy code of the eighth child node is 01001111. The point cloud encoder (for example, the arithmetic encoder40004) according to the embodiments may perform entropy encoding on the occupancy codes. In order to increase the compression efficiency, the point cloud encoder may perform intra/inter-coding on the occupancy codes. The reception device (for example, the reception device10004or the point cloud video decoder10006) according to the embodiments reconstructs the octree based on the occupancy codes.

The point cloud encoder (for example, the point cloud encoder ofFIG.4or the octree analyzer40002) according to the embodiments may perform voxelization and octree coding to store the positions of points. However, points are not always evenly distributed in the 3D space, and accordingly there may be a specific region in which fewer points are present. Accordingly, it is inefficient to perform voxelization for the entire 3D space. For example, when a specific region contains few points, voxelization does not need to be performed in the specific region.

Accordingly, for the above-described specific region (or a node other than the leaf node of the octree), the point cloud encoder according to the embodiments may skip voxelization and perform direct coding to directly code the positions of points included in the specific region. The coordinates of a direct coding point according to the embodiments are referred to as direct coding mode (DCM). The point cloud encoder according to the embodiments may also perform trisoup geometry encoding, which is to reconstruct the positions of the points in the specific region (or node) based on voxels, based on a surface model. The trisoup geometry encoding is geometry encoding that represents an object as a series of triangular meshes. Accordingly, the point cloud decoder may generate a point cloud from the mesh surface. The direct coding and trisoup geometry encoding according to the embodiments may be selectively performed. In addition, the direct coding and trisoup geometry encoding according to the embodiments may be performed in combination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applying direct coding should be activated. A node to which direct coding is to be applied is not a leaf node, and points less than a threshold should be present within a specific node. In addition, the total number of points to which direct coding is to be applied should not exceed a preset threshold. When the conditions above are satisfied, the point cloud encoder (or the arithmetic encoder40004) according to the embodiments may perform entropy coding on the positions (or position values) of the points.

The point cloud encoder (for example, the surface approximation analyzer40003) according to the embodiments may determine a specific level of the octree (a level less than the depth d of the octree), and the surface model may be used staring with that level to perform trisoup geometry encoding to reconstruct the positions of points in the region of the node based on voxels (Trisoup mode). The point cloud encoder according to the embodiments may specify a level at which trisoup geometry encoding is to be applied. For example, when the specific level is equal to the depth of the octree, the point cloud encoder does not operate in the trisoup mode. In other words, the point cloud encoder according to the embodiments may operate in the trisoup mode only when the specified level is less than the value of depth of the octree. The 3D cube region of the nodes at the specified level according to the embodiments is called a block. One block may include one or more voxels. The block or voxel may correspond to a brick. Geometry is represented as a surface within each block. The surface according to embodiments may intersect with each edge of a block at most once.

One block has 12 edges, and accordingly there are at least 12 intersections in one block. Each intersection is called a vertex (or apex). A vertex present along an edge is detected when there is at least one occupied voxel adjacent to the edge among all blocks sharing the edge. The occupied voxel according to the embodiments refers to a voxel containing a point. The position of the vertex detected along the edge is the average position along the edge of all voxels adjacent to the edge among all blocks sharing the edge.

Once the vertex is detected, the point cloud encoder according to the embodiments may perform entropy encoding on the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When the trisoup geometry encoding is applied, the point cloud encoder according to the embodiments (for example, the geometry reconstructor40005) may generate restored geometry (reconstructed geometry) by performing the triangle reconstruction, up-sampling, and voxelization processes.

The vertices positioned at the edge of the block determine a surface that passes through the block. The surface according to the embodiments is a non-planar polygon. In the triangle reconstruction process, a surface represented by a triangle is reconstructed based on the starting point of the edge, the direction vector of the edge, and the position values of the vertices. The triangle reconstruction process is performed by: i) calculating the centroid value of each vertex, ii) subtracting the center value from each vertex value, and iii) estimating the sum of the squares of the values obtained by the subtraction.

[μxμyμz]=1n⁢∑i=1n[xiyizi];i)[x_iy_iz_i]=[xiyizi]-[μxμyμz];ii)[σx2σy2σz2]=∑i=1n[x_i2y_i2z_i2]iii)

The minimum value of the sum is estimated, and the projection process is performed according to the axis with the minimum value. For example, when the element x is the minimum, each vertex is projected on the x-axis with respect to the center of the block, and projected on the (y, z) plane. When the values obtained through projection on the (y, z) plane are (ai, bi), the value of θ is estimated through atan2(bi, ai), and the vertices are ordered based on the value of θ. The table below shows a combination of vertices for creating a triangle according to the number of the vertices. The vertices are ordered from 1 to n. The table below shows that for four vertices, two triangles may be constructed according to combinations of vertices. The first triangle may consist of vertices 1, 2, and 3 among the ordered vertices, and the second triangle may consist of vertices 3, 4, and 1 among the ordered vertices.

TABLE 2-1Triangles formed from vertices ordered 1, . . . , nntriangles3(1, 2, 3)4(1, 2, 3), (3, 4, 1)5(1, 2, 3), (3, 4, 5), (5, 1, 3)6(1, 2, 3), (3, 4, 5), (5, 6, 1), (1, 3, 5)7(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 1, 3), (3, 5, 7)8(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 1), (1, 3, 5), (5, 7, 1)9(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 1, 3), (3, 5, 7), (7, 9, 3)10(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 10, 1), (1, 3, 5), (5, 7, 9), (9, 1, 5)11(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 10, 11), (11, 1, 3), (3, 5, 7), (7, 9, 11), (11, 3, 7)12(1, 2, 3), (3, 4, 5), (5, 6, 7), (7, 8, 9), (9, 10, 11), (11, 12, 1), (1, 3, 5), (5, 7, 9), (9, 11, 1),(1, 5, 9)

The upsampling process is performed to add points in the middle along the edge of the triangle and perform voxelization. The added points are generated based on the upsampling factor and the width of the block. The added points are called refined vertices. The point cloud encoder according to the embodiments may voxelize the refined vertices. In addition, the point cloud encoder may perform attribute encoding based on the voxelized positions (or position values).

FIG.7shows an example of a neighbor node pattern according to embodiments.

In order to increase the compression efficiency of the point cloud video, the point cloud encoder according to the embodiments may perform entropy coding based on context adaptive arithmetic coding.

As described with reference toFIGS.1to6, the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder10002, the point cloud encoder or arithmetic encoder40004ofFIG.4) may perform entropy coding on the occupancy code immediately. In addition, the point cloud content providing system or the point cloud encoder may perform entropy encoding (intra encoding) based on the occupancy code of the current node and the occupancy of neighboring nodes, or perform entropy encoding (inter encoding) based on the occupancy code of the previous frame. A frame according to embodiments represents a set of point cloud videos generated at the same time. The compression efficiency of intra encoding/inter encoding according to the embodiments may depend on the number of neighboring nodes that are referenced. When the bits increase, the operation becomes complicated, but the encoding may be biased to one side, which may increase the compression efficiency. For example, when a 3-bit context is given, coding needs to be performed using 23=8 methods. The part divided for coding affects the complexity of implementation. Accordingly, it is necessary to meet an appropriate level of compression efficiency and complexity.

FIG.7illustrates a process of obtaining an occupancy pattern based on the occupancy of neighbor nodes. The point cloud encoder according to the embodiments determines occupancy of neighbor nodes of each node of the octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node. The left part ofFIG.7shows a cube corresponding to a node (a cube positioned in the middle) and six cubes (neighbor nodes) sharing at least one face with the cube. The nodes shown in the figure are nodes of the same depth. The numbers shown in the figure represent weights (1, 2, 4, 8, 16, and 32) associated with the six nodes, respectively. The weights are assigned sequentially according to the positions of neighboring nodes.

The right part ofFIG.7shows neighbor node pattern values. A neighbor node pattern value is the sum of values multiplied by the weight of an occupied neighbor node (a neighbor node having a point). Accordingly, the neighbor node pattern values are 0 to 63. When the neighbor node pattern value is 0, it indicates that there is no node having a point (no occupied node) among the neighbor nodes of the node. When the neighbor node pattern value is 63, it indicates that all neighbor nodes are occupied nodes. As shown in the figure, since neighbor nodes to which weights 1, 2, 4, and 8 are assigned are occupied nodes, the neighbor node pattern value is 15, the sum of 1, 2, 4, and 8. The point cloud encoder may perform coding according to the neighbor node pattern value (for example, when the neighbor node pattern value is 63, 64 kinds of coding may be 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 by which 64 is changed to 10 or 6).

FIG.8illustrates an example of point configuration in each LOD according to embodiments.

As described with reference toFIGS.1to7, encoded geometry is reconstructed (decompressed) before attribute encoding is performed. When direct coding is applied, the geometry reconstruction operation may include changing the placement of direct coded points (e.g., placing the direct coded points in front of the point cloud data). When trisoup geometry encoding is applied, the geometry reconstruction process is performed through triangle reconstruction, up-sampling, and voxelization. Since the attribute depends on the geometry, attribute encoding is performed based on the reconstructed geometry.

The point cloud encoder (for example, the LOD generator40009) may classify (reorganize) points by LOD. The figure shows the point cloud content corresponding to LODs. The leftmost picture in the figure represents original point cloud content. The second picture from the left of the figure represents distribution of the points in the lowest LOD, and the rightmost picture in the figure represents distribution of the points in the highest LOD. That is, the points in the lowest LOD are sparsely distributed, and the points in the highest LOD are densely distributed. That is, as the LOD rises in the direction pointed by the arrow indicated at the bottom of the figure, the space (or distance) between points is narrowed.

FIG.9illustrates an example of point configuration for each LOD according to embodiments.

As described with reference toFIGS.1to8, the point cloud content providing system, or the point cloud encoder (for example, the point cloud video encoder10002, the point cloud encoder ofFIG.4, or the LOD generator40009) may generates an LOD. 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 distances). The LOD generation process is performed not only by the point cloud encoder, but also by the point cloud decoder.

The upper part ofFIG.9shows examples (P0 to P9) of points of the point cloud content distributed in a 3D space. InFIG.9, the original order represents the order of points P0 to P9 before LOD generation. InFIG.9, the LOD based order represents the order of points according to the LOD generation. Points are reorganized by LOD. Also, a high LOD contains the points belonging to lower LODs. As shown inFIG.9, LOD0 contains P0, P5, P4 and P2. LOD1 contains the points of LOD0, P1, P6 and P3. LOD2 contains the points of LOD0, the points of LOD1, P9, P8 and P7.

As described with reference toFIG.4, the point cloud encoder according to the embodiments may perform prediction transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

The point cloud encoder according to the embodiments may generate a predictor for points to perform prediction transform coding for setting a predicted attribute (or predicted attribute value) of each point. That is, N predictors may be generated for N points. The predictor according to the embodiments may calculate a weight (=1/distance) based on the LOD value of each point, indexing information about neighboring points present within a set distance for each LOD, and a distance to the neighboring points.

The predicted attribute (or attribute value) according to the embodiments is set to the average of values obtained by multiplying the attributes (or attribute values) (e.g., color, reflectance, etc.) of neighbor points set in the predictor of each point by a weight (or weight value) calculated based on the distance to each neighbor point. The point cloud encoder according to the embodiments (for example, the coefficient quantizer40011) may quantize and inversely quantize the residuals (which may be called residual attributes, residual attribute values, or attribute prediction residuals) obtained by subtracting a predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is configured as shown in the following table.

TABLE Attribute prediction residuals quantizationpseudo codeint PCCQuantization(int value, int quantStep) {if( value >=0) {return floor(value/quantStep + 1.0/3.0);} else {return -floor(-value/quantStep + 1.0/3.0);}}TABLE Attribute prediction residuals inversequantization pseudo codeint PCCInverseQuantization(int value, int quantStep) {if( quantStep == 0) {return value;} else {return value * quantStep;}}

When the predictor of each point has neighbor points, the point cloud encoder (e.g., the arithmetic encoder40012) according to the embodiments may perform entropy coding on the quantized and inversely quantized residual values as described above. When the predictor of each point has no neighbor point, the point cloud encoder according to the embodiments (for example, the arithmetic encoder40012) may perform entropy coding on the attributes of the corresponding point without performing the above-described operation.

The point cloud encoder according to the embodiments (for example, the lifting transformer40010) may generate a predictor of each point, set the calculated LOD and register neighbor points in the predictor, and set weights according to the distances to neighbor points to perform lifting transform coding. The lifting transform coding according to the embodiments is similar to the above-described prediction transform coding, but differs therefrom in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to the attribute values according to embodiments is configured as follows.1) Create an array Quantization Weight (QW) for storing the weight value of each point. The initial value of all elements of QW is 1.0. Multiply the QW values of the predictor indexes of the neighbor nodes registered in the predictor by the weight of the predictor of the current point, and add the values obtained by the multiplication.2) Lift prediction process: Subtract the value obtained by multiplying the attribute value of the point by the weight from the existing attribute value to calculate a predicted attribute value.3) Create temporary arrays called updateweight and update and initialize the temporary arrays to zero.4) Cumulatively add the weights calculated by multiplying the weights calculated for all predictors by a weight stored in the QW corresponding to a predictor index to the updateweight array as indexes of neighbor nodes. Cumulatively add, to the update array, a value obtained by multiplying the attribute value of the index of a neighbor node by the calculated weight.5) Lift update process: Divide the attribute values of the update array for all predictors by the weight value of the updateweight array of the predictor index, and add the existing attribute value to the values obtained by the division.6) Calculate predicted attributes by multiplying the attribute values updated through the lift update process by the weight updated through the lift prediction process (stored in the QW) for all predictors. The point cloud encoder (e.g., coefficient quantizer40011) according to the embodiments quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., the arithmetic encoder40012) performs entropy coding on the quantized attribute values.

The point cloud encoder (for example, the RAHT transformer40008) according to the embodiments may perform RAHT transform coding in which attributes of nodes of a higher level are predicted using the attributes associated with nodes of a lower level in the octree. RAHT transform coding is an example of attribute intra coding through an octree backward scan. The point cloud encoder according to the embodiments scans the entire region from the voxel and repeats the merging process of merging the voxels into a larger block at each step until the root node is reached. The merging process according to the embodiments is performed only on the occupied nodes. The merging process is not performed on the empty node. The merging process is performed on an upper node immediately above the empty node.

The equation below represents a RAHT transformation matrix. In the equation, glx,y,zdenotes the average attribute value of voxels at level l. glx,y,zmay be calculated based on gl+12x,y,zand gl+12x+1,y,z. The weights for gl2x,y,zand gl2x+1,y,zare w1=wl2x,y,zand w2=wl2x+1,y,z.

⌈gl-1x,y,zhl-1x,y,z⌉=Tw⁢1⁢w⁢2⁢⌈gl2⁢x,y,zgl2⁢x+1,y,z⌉,Tw⁢1⁢w⁢2=1w⁢1+w⁢2[w⁢1w⁢2-w⁢2w⁢1]

Here, gl−1x,y,zis a low-pass value and is used in the merging process at the next higher level. hl−1x,y,zdenotes high-pass coefficients. The high-pass coefficients at each step are quantized and subjected to entropy coding (for example, encoding by the arithmetic encoder400012). The weights are calculated as wl−1x,y,z=wl2x,y,z+wl2x+1,y,z. The root node is created through the gl0,0,0and gl0,0,1as follows.

⌈gDCh00,0,0⌉=Tw⁢1000⁢w⁢1001⁢⌈g10,0,0⁢zg10,0,1⌉

FIG.10illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated inFIG.10is an example of the point cloud video decoder10006described inFIG.1, and may perform the same or similar operations as the operations of the point cloud video decoder10006illustrated inFIG.1. As shown in the figure, the point cloud decoder may receive a geometry bitstream and an attribute bitstream contained 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 performs attribute decoding based on the decoded geometry and the attribute bitstream, and outputs decoded attributes. The decoded geometry and decoded attributes are used to reconstruct point cloud content (a decoded point cloud).

FIG.11illustrates a point cloud decoder according to embodiments.

The point cloud decoder illustrated inFIG.11is an example of the point cloud decoder illustrated inFIG.10, and may perform a decoding operation, which is an inverse process of the encoding operation of the point cloud encoder illustrated inFIGS.1to9.

As described with reference toFIGS.1and10, the point cloud decoder may perform geometry decoding and attribute decoding. The geometry decoding is performed before the attribute decoding.

The point cloud decoder according to the embodiments includes an arithmetic decoder (Arithmetic decode)11000, an octree synthesizer (Synthesize octree)11001, a surface approximation synthesizer (Synthesize surface approximation)11002, and a geometry reconstructor (Reconstruct geometry)11003, a coordinate inverse transformer (Inverse transform coordinates)11004, an arithmetic decoder (Arithmetic decode)11005, an inverse quantizer (Inverse quantize)11006, a RAHT transformer11007, an LOD generator (Generate LOD)11008, an inverse lifter (inverse lifting)11009, and/or a color inverse transformer (Inverse transform colors)11010.

The arithmetic decoder11000, the octree synthesizer11001, the surface approximation synthesizer11002, the geometry reconstructor11003, and the coordinate inverse transformer11004may perform geometry decoding. The geometry decoding according to the embodiments may include direct coding and trisoup geometry decoding. The direct coding and trisoup geometry decoding are selectively applied. The geometry decoding is not limited to the above-described example, and is performed as an inverse process of the geometry encoding described with reference toFIGS.1to9.

The arithmetic decoder11000according to the embodiments decodes the received geometry bitstream based on the arithmetic coding. The operation of the arithmetic decoder11000corresponds to the inverse process of the arithmetic encoder40004.

The octree synthesizer11001according to the embodiments may generate an octree by acquiring an occupancy code from the decoded geometry bitstream (or information on the geometry secured as a result of decoding). The occupancy code is configured as described in detail with reference toFIGS.1to9.

When the trisoup geometry encoding is applied, the surface approximation synthesizer11002according to the embodiments may synthesize a surface based on the decoded geometry and/or the generated octree.

The geometry reconstructor11003according to the embodiments may regenerate geometry based on the surface and/or the decoded geometry. As described with reference toFIGS.1to9, direct coding and trisoup geometry encoding are selectively applied. Accordingly, the geometry reconstructor11003directly imports and adds position information about the points to which direct coding is applied. When the trisoup geometry encoding is applied, the geometry reconstructor11003may reconstruct the geometry by performing the reconstruction operations of the geometry reconstructor40005, for example, triangle reconstruction, up-sampling, and voxelization. Details are the same as those described with reference toFIG.6, and thus description thereof is omitted. The reconstructed geometry may include a point cloud picture or frame that does not contain attributes.

The coordinate inverse transformer11004according to the embodiments may acquire positions of the points by transforming the coordinates based on the reconstructed geometry.

The arithmetic decoder11005, the inverse quantizer11006, the RAHT transformer11007, the LOD generator11008, the inverse lifter11009, and/or the color inverse transformer11010may perform the attribute decoding described with reference toFIG.10. The attribute decoding according to the embodiments includes region adaptive hierarchical transform (RAHT) decoding, interpolation-based hierarchical nearest-neighbor prediction (prediction transform) decoding, and interpolation-based hierarchical nearest-neighbor prediction with an update/lifting step (lifting transform) decoding. The three decoding schemes described above may be used selectively, or a combination of one or more decoding schemes may be used. The attribute decoding according to the embodiments is not limited to the above-described example.

The arithmetic decoder11005according to the embodiments decodes the attribute bitstream by arithmetic coding.

The inverse quantizer11006according to the embodiments inversely quantizes the information about the decoded attribute bitstream or attributes secured as a result of the decoding, and outputs the inversely quantized attributes (or attribute values). The inverse quantization may be selectively applied based on the attribute encoding of the point cloud encoder.

According to embodiments, the RAHT transformer11007, the LOD generator11008, and/or the inverse lifter11009may process the reconstructed geometry and the inversely quantized attributes. As described above, the RAHT transformer11007, the LOD generator11008, and/or the inverse lifter11009may selectively perform a decoding operation corresponding to the encoding of the point cloud encoder.

The color inverse transformer11010according to the embodiments performs inverse transform coding to inversely transform a color value (or texture) included in the decoded attributes. The operation of the color inverse transformer11010may be selectively performed based on the operation of the color transformer40006of the point cloud encoder.

Although not shown in the figure, the elements of the point cloud decoder ofFIG.11may be implemented by hardware including one or more processors or integrated circuits configured to communicate with one or more memories included in the point cloud providing device, software, firmware, or a combination thereof. The one or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud decoder ofFIG.11described above. Additionally, the one or more processors may operate or execute a set of software programs and/or instructions for performing the operations and/or functions of the elements of the point cloud decoder ofFIG.11.

FIG.12illustrates a transmission device according to embodiments.

The transmission device shown inFIG.12is an example of the transmission device10000ofFIG.1(or the point cloud encoder ofFIG.4). The transmission device illustrated inFIG.12may perform one or more of the operations and methods the same as or similar to those of the point cloud encoder described with reference toFIGS.1to9. The transmission device according to the embodiments may include a data input unit12000, a quantization processor12001, a voxelization processor12002, an octree occupancy code generator12003, a surface model processor12004, an intra/inter-coding processor12005, an arithmetic coder12006, a metadata processor12007, a color transform processor12008, an attribute transform processor12009, a prediction/lifting/RAHT transform processor12010, an arithmetic coder12011and/or a transmission processor12012.

The data input unit12000according to the embodiments receives or acquires point cloud data. The data input unit12000may perform an operation and/or acquisition method the same as or similar to the operation and/or acquisition method of the point cloud video acquirer10001(or the acquisition process20000described with reference toFIG.2).

The data input unit12000, the quantization processor12001, the voxelization processor12002, the octree occupancy code generator12003, the surface model processor12004, the intra/inter-coding processor12005, and the arithmetic coder12006perform geometry encoding. The geometry encoding according to the embodiments is the same as or similar to the geometry encoding described with reference toFIGS.1to9, and thus a detailed description thereof is omitted.

The quantization processor12001according to the embodiments quantizes geometry (e.g., position values of points). The operation and/or quantization of the quantization processor12001is the same as or similar to the operation and/or quantization of the quantizer40001described with reference toFIG.4. Details are the same as those described with reference toFIGS.1to9.

The voxelization processor12002according to the embodiments voxelizes the quantized position values of the points. The voxelization processor120002may perform an operation and/or process the same or similar to the operation and/or the voxelization process of the quantizer40001described with reference toFIG.4. Details are the same as those described with reference toFIGS.1to9.

The octree occupancy code generator12003according to the embodiments performs octree coding on the voxelized positions of the points based on an octree structure. The octree occupancy code generator12003may generate an occupancy code. The octree occupancy code generator12003may perform an operation and/or method the same as or similar to the operation and/or method of the point cloud encoder (or the octree analyzer40002) described with reference toFIGS.4and6. Details are the same as those described with reference toFIGS.1to9.

The surface model processor12004according to the embodiments may perform trigsoup geometry encoding based on a surface model to reconstruct the positions of points in a specific region (or node) on a voxel basis. The surface model processor12004may perform an operation and/or method the same as or similar to the operation and/or method of the point cloud encoder (for example, the surface approximation analyzer40003) described with reference toFIG.4. Details are the same as those described with reference toFIGS.1to9.

The intra/inter-coding processor12005according to the embodiments may perform intra/inter-coding on point cloud data. The intra/inter-coding processor12005may perform coding the same as or similar to the intra/inter-coding described with reference toFIG.7. Details are the same as those described with reference toFIG.7. According to embodiments, the intra/inter-coding processor12005may be included in the arithmetic coder12006.

The arithmetic coder12006according to the embodiments performs entropy encoding on an octree of the point cloud data and/or an approximated octree. For example, the encoding scheme includes arithmetic encoding. The arithmetic coder12006performs an operation and/or method the same as or similar to the operation and/or method of the arithmetic encoder40004.

The metadata processor12007according to the embodiments processes metadata about the point cloud data, for example, a set value, and provides the same to a necessary processing process such as geometry encoding and/or attribute encoding. Also, the metadata processor12007according to the embodiments may generate and/or process signaling information related to the geometry encoding and/or the attribute encoding. The signaling information according to the embodiments may be encoded separately from the geometry encoding and/or the attribute encoding. The signaling information according to the embodiments may be interleaved.

The color transform processor12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor12010, and the arithmetic coder12011perform the attribute encoding. The attribute encoding according to the embodiments is the same as or similar to the attribute encoding described with reference toFIGS.1to9, and thus a detailed description thereof is omitted.

The color transform processor12008according to the embodiments performs color transform coding to transform color values included in attributes. The color transform processor12008may perform color transform coding based on the reconstructed geometry. The reconstructed geometry is the same as described with reference toFIGS.1to9. Also, it performs an operation and/or method the same as or similar to the operation and/or method of the color transformer40006described with reference toFIG.4is performed. The detailed description thereof is omitted.

The attribute transform processor12009according to the embodiments performs attribute transformation to transform the attributes based on the reconstructed geometry and/or the positions on which geometry encoding is not performed. The attribute transform processor12009performs an operation and/or method the same as or similar to the operation and/or method of the attribute transformer40007described with reference toFIG.4. The detailed description thereof is omitted. The prediction/lifting/RAHT transform processor12010according to the embodiments may code the transformed attributes by any one or a combination of RAHT coding, prediction transform coding, and lifting transform coding. The prediction/lifting/RAHT transform processor12010performs at least one of the operations the same as or similar to the operations of the RAHT transformer40008, the LOD generator40009, and the lifting transformer40010described with reference toFIG.4. In addition, the prediction transform coding, the lifting transform coding, and the RAHT transform coding are the same as those described with reference toFIGS.1to9, and thus a detailed description thereof is omitted.

The arithmetic coder12011according to the embodiments may encode the coded attributes based on the arithmetic coding. The arithmetic coder12011performs an operation and/or method the same as or similar to the operation and/or method of the arithmetic encoder400012.

The transmission processor12012according to the embodiments may transmit each bitstream containing encoded geometry and/or encoded attributes and metadata information, or transmit one bitstream configured with the encoded geometry and/or the encoded attributes and the metadata information. When the encoded geometry and/or the encoded attributes and the metadata information according to the embodiments are configured into one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments may contain signaling information including 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 parameter set (TPS) for signaling of a tile level, and slice data. The slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream Geom00 and one or more attribute bitstreams Attr00 and Attr10.

A slice refers to a series of syntax elements representing the entirety or part of a coded point cloud frame.

The TPS according to the embodiments may include information about each tile (for example, coordinate information and height/size information about a bounding box) for one or more tiles. The geometry bitstream may contain a header and a payload. The header of the geometry bitstream according to the embodiments may contain a parameter set identifier (geom_parameter_set_id), a tile identifier (geom_tile_id) and a slice identifier (geom_slice_id) included in the GPS, and information about the data contained in the payload. As described above, the metadata processor12007according to the embodiments may generate and/or process the signaling information and transmit the same to the transmission processor12012. According to embodiments, the elements to perform geometry encoding and the elements to perform attribute encoding may share data/information with each other as indicated by dotted lines. The transmission processor12012according to the embodiments may perform an operation and/or transmission method the same as or similar to the operation and/or transmission method of the transmitter10003. Details are the same as those described with reference toFIGS.1and2, and thus a description thereof is omitted.

FIG.13illustrates a reception device according to embodiments.

The reception device illustrated inFIG.13is an example of the reception device10004ofFIG.1(or the point cloud decoder ofFIGS.10and11). The reception device illustrated inFIG.13may perform one or more of the operations and methods the same as or similar to those of the point cloud decoder described with reference toFIGS.1to11.

The reception device according to the embodiment includes a receiver13000, a reception processor13001, an arithmetic decoder13002, an occupancy code-based octree reconstruction processor13003, a surface model processor (triangle reconstruction, up-sampling, voxelization)13004, an inverse quantization processor13005, a metadata parser13006, an arithmetic decoder13007, an inverse quantization processor13008, a prediction/lifting/RAHT inverse transform processor13009, a color inverse transform processor13010, and/or a renderer13011. Each element for decoding according to the embodiments may perform an inverse process of the operation of a corresponding element for encoding according to the embodiments.

The receiver13000according to the embodiments receives point cloud data. The receiver13000may perform an operation and/or reception method the same as or similar to the operation and/or reception method of the receiver10005ofFIG.1. The detailed description thereof is omitted.

The reception processor13001according to the embodiments may acquire a geometry bitstream and/or an attribute bitstream from the received data. The reception processor13001may be included in the receiver13000.

The arithmetic decoder13002, the occupancy code-based octree reconstruction processor13003, the surface model processor13004, and the inverse quantization processor13005may perform geometry decoding. The geometry decoding according to embodiments is the same as or similar to the geometry decoding described with reference toFIGS.1to10, and thus a detailed description thereof is omitted.

The arithmetic decoder13002according to the embodiments may decode the geometry bitstream based on arithmetic coding. The arithmetic decoder13002performs an operation and/or coding the same as or similar to the operation and/or coding of the arithmetic decoder11000.

The occupancy code-based octree reconstruction processor13003according to the embodiments may reconstruct an octree by acquiring an occupancy code from the decoded geometry bitstream (or information about the geometry secured as a result of decoding). The occupancy code-based octree reconstruction processor13003performs an operation and/or method the same as or similar to the operation and/or octree generation method of the octree synthesizer11001. When the trisoup geometry encoding is applied, the surface model processor13004according to the embodiments may perform trisoup geometry decoding and related geometry reconstruction (for example, triangle reconstruction, up-sampling, voxelization) based on the surface model method. The surface model processor13004performs an operation the same as or similar to that of the surface approximation synthesizer11002and/or the geometry reconstructor11003.

The inverse quantization processor13005according to the embodiments may inversely quantize the decoded geometry.

The metadata parser13006according to the embodiments may parse metadata contained in the received point cloud data, for example, a set value. The metadata parser13006may pass the metadata to geometry decoding and/or attribute decoding. The metadata is the same as that described with reference toFIG.12, and thus a detailed description thereof is omitted.

The arithmetic decoder13007, the inverse quantization processor13008, the prediction/lifting/RAHT inverse transform processor13009and the color inverse transform processor13010perform attribute decoding. The attribute decoding is the same as or similar to the attribute decoding described with reference toFIGS.1to10, and thus a detailed description thereof is omitted.

The arithmetic decoder13007according to the embodiments may decode the attribute bitstream by arithmetic coding. The arithmetic decoder13007may decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder13007performs an operation and/or coding the same as or similar to the operation and/or coding of the arithmetic decoder11005.

The inverse quantization processor13008according to the embodiments may inversely quantize the decoded attribute bitstream. The inverse quantization processor13008performs an operation and/or method the same as or similar to the operation and/or inverse quantization method of the inverse quantizer11006.

The prediction/lifting/RAHT inverse transformer13009according to the embodiments may process the reconstructed geometry and the inversely quantized attributes. The prediction/lifting/RAHT inverse transform processor13009performs one or more of operations and/or decoding the same as or similar to the operations and/or decoding of the RAHT transformer11007, the LOD generator11008, and/or the inverse lifter11009. The color inverse transform processor13010according to the embodiments performs inverse transform coding to inversely transform color values (or textures) included in the decoded attributes. The color inverse transform processor13010performs an operation and/or inverse transform coding the same as or similar to the operation and/or inverse transform coding of the color inverse transformer11010. The renderer13011according to the embodiments may render the point cloud data.

FIG.14illustrates an exemplary structure operable in connection with point cloud data transmission/reception methods/devices according to embodiments.

The structure ofFIG.14represents a configuration in which at least one of a server1460, a robot1410, a self-driving vehicle1420, an XR device1430, a smartphone1440, a home appliance1450, and/or a head-mount display (HMD)1470is connected to the cloud network1400. The robot1410, the self-driving vehicle1420, the XR device1430, the smartphone1440, or the home appliance1450is called a device. Further, the XR device1430may correspond to a point cloud data (PCC) device according to embodiments or may be operatively connected to the PCC device.

The cloud network1400may represent a network that constitutes part of the cloud computing infrastructure or is present in the cloud computing infrastructure. Here, the cloud network1400may be configured using a 3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server1460may be connected to at least one of the robot1410, the self-driving vehicle1420, the XR device1430, the smartphone1440, the home appliance1450, and/or the HMD1470over the cloud network1400and may assist in at least a part of the processing of the connected devices1410to1470.

The HMD1470represents one of the implementation types of the XR device and/or the PCC device according to the embodiments. The HMD type device according to the embodiments includes a communication unit, a control unit, a memory, an I/O unit, a sensor unit, and a power supply unit.

Hereinafter, various embodiments of the devices1410to1450to which the above-described technology is applied will be described. The devices1410to1450illustrated inFIG.14may be operatively connected/coupled to a point cloud data transmission device and reception according to the above-described embodiments.
<PCC+XR>

The XR/PCC device1430may employ PCC technology and/or XR (AR+VR) technology, and may be implemented as an HMD, a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a stationary robot, or a mobile robot.

The XR/PCC device1430may analyze 3D point cloud data or image data acquired through various sensors or from an external device and generate position data and attribute data about 3D points. Thereby, the XR/PCC device1430may acquire information about the surrounding space or a real object, and render and output an XR object. For example, the XR/PCC device1430may match an XR object including auxiliary information about a recognized object with the recognized object and output the matched XR object.
<PCC+XR+Mobile phone>

The XR/PCC device1430may be implemented as a mobile phone1440by applying PCC technology.

The mobile phone1440may decode and display point cloud content based on the PCC technology.
<PCC+Self-driving+XR>

The self-driving vehicle1420may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, or the like by applying the PCC technology and the XR technology.

The self-driving vehicle1420to which the XR/PCC technology is applied may represent a self-driving vehicle provided with means for providing an XR image, or a self-driving vehicle that is a target of control/interaction in the XR image. In particular, the self-driving vehicle1420which is a target of control/interaction in the XR image may be distinguished from the XR device1430and may be operatively connected thereto.

The self-driving vehicle1420having means for providing an XR/PCC image may acquire sensor information from sensors including a camera, and output the generated XR/PCC image based on the acquired sensor information. For example, the self-driving vehicle1420may have an HUD and output an XR/PCC image thereto, thereby providing an occupant with an XR/PCC object corresponding to a real object or an object present on the screen.

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 real object to which the occupant's eyes are directed. On the other hand, when the XR/PCC object is output on a display provided inside the self-driving vehicle, at least a part of the XR/PCC object may be output to overlap an object on the screen. For example, the self-driving vehicle1220may output XR/PCC objects corresponding to objects such as a road, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, and a building.

The virtual reality (VR) technology, the augmented reality (AR) technology, the mixed reality (MR) technology and/or the point cloud compression (PCC) technology according to the embodiments are applicable to various devices.

In other words, the VR technology is a display technology that provides only CG images of real-world objects, backgrounds, and the like. On the other hand, the AR technology refers to a technology that shows a virtually created CG image on the image of a real object. The MR technology is similar to the AR technology described above in that virtual objects to be shown are mixed and combined with the real world. However, the MR technology differs from the AR technology in that the AR technology makes a clear distinction between a real object and a virtual object created as a CG image and uses virtual objects as complementary objects for real objects, whereas the MR technology treats virtual objects as objects having equivalent characteristics as real objects. More specifically, an example of MR technology applications is a hologram service.

Recently, the VR, AR, and MR technologies are sometimes referred to as extended reality (XR) technology rather than being clearly distinguished from each other. Accordingly, embodiments of the present disclosure are applicable to any of the VR, AR, MR, and XR technologies. The encoding/decoding based on PCC, V-PCC, and G-PCC techniques is applicable to such technologies.

The PCC method/device according to the embodiments may be applied to a vehicle that provides a self-driving service.

A vehicle that provides the self-driving service is connected to a PCC device for wired/wireless communication.

When the point cloud data (PCC) transmission/reception device according to the embodiments is connected to a vehicle for wired/wireless communication, the device may receive/process content data related to an AR/VR/PCC service, which may be provided together with the self-driving service, and transmit the same to the vehicle. In the case where the PCC transmission/reception device is mounted on a vehicle, the PCC transmission/reception device may receive/process content data related to the AR/VR/PCC service according to a user input signal input through a user interface device and provide the same to the user. The vehicle or the user interface device according to the embodiments may receive a user input signal. The user input signal according to the embodiments may include a signal indicating the self-driving service.

Hereinafter, a geometry encoding operation for effectively compressing the geometry information will be described.

As described with reference toFIGS.1to14, the geometry (or called geometry information) of point cloud data according to embodiments represents positions of points. The positions of the points are indicated by parameters of a coordinate system representing a 3D space (e.g., parameters x, y, and z of three axes, an X-axis, a Y-axis, and a Z-axis representing the space). Attributes indicate the color (RGB, YUV, etc.) and/or reflectance of the points. As described with reference toFIGS.1to14, the attributes are encoded based on the encoded geometry.

A point cloud data processing device according to embodiments may perform low-latency coding according to the characteristics of the content of the point cloud data. For example, when the point cloud data is data captured in real time from LiDAR or 3D map data transmitted in real time, the point cloud data processing device needs to process the point cloud data to minimize delay and have high compression efficiency.

As described with reference toFIG.4, the geometry encoding according to the embodiments may include octree geometry coding, direct coding, trisoup geometry encoding, and entropy encoding. The direct coding and trisoup geometry encoding are applied selectively or in combination. Also, the geometry encoding according to the embodiments may include predictive tree encoding (or predictive geometry coding).

The predictive tree coding according to the embodiments is performed by defining a prediction structure for the point cloud data. This structure is represented by a predictive tree having a vertex associated with each point of the point cloud data. The predictive tree may include a root vertex and a leaf vertex, and vertices below the root vertex may have at least one child, and the depth increases toward the leaf vertex. Each vertex may be predicted from parent nodes in the predictive tree. For each vertex, various predictors (e.g., no prediction, delta prediction, linear prediction, a parallelogram predictor, etc.) may be used based on the positions of the parent, grand-parent, and grand-grandparent of the vertex. A predictive tree is encoded by traversing the tree in depth order and encoding the number of children at each vertex. In order to construct a predictive tree according to embodiments, points are re-sorted according to a reference (e.g., a Morton code, etc.). The predictive tree may be generated based on a Kdtree (or Kdtree data structure). The Kdtree structure is used to track a potential prediction variable. The first Kdtree is empty. Points are visited repeatedly in the order selected. K-nearest neighbors are determined in the Kdtree of the current point according to various references, and one of neighbors is selected as a predictor. Once the predictor is selected, the current vertex is added to the children of the vertices associated with the predictor. Then, the next predictor is generated based on the current vertex, and the predicted positions are added to the kdtree. This process is repeated until all points are traversed. At the end of this process, the predictive tree structure is determined. The predictive tree coding according to the embodiments may be performed using a coding algorithm.

As described with reference toFIGS.6to8, the octree geometry coding is a method of scanning and coding all points in a breadth-first manner. The predictive tree coding according to the embodiments is a depth-first scheme. The point cloud data processing device (e.g., the point cloud data transmission device described with reference toFIGS.1,11,14and15) according to the embodiments may sort the points by performing predictive tree coding, and generate a predictive tree by scanning the points step by step, generate a predicted value through the geometry between a parent node and a child node in the generated tree structure, and entropy-code a residual to output a geometry bitstream. The predictive tree coding eliminates the need to wait for all point cloud data to be captured because step-by-step scanning of all points is not performed. In addition, the predictive tree coding allows captured point cloud data to be progressively encoded, and is therefore suitable for point cloud data content that requires low-latency processing.

However, the depth-first scheme may increase the residual compared to the breadth-first scheme, in which the entire point cloud data is analyzed, thereby increasing the bitstream size. Also, since predictive tree coding is performed based on sorted points, the sorting order of the points has a great influence on predictive tree generation. That is, the points actually positioned at a close distance may not be configured as parent and child nodes of the predictive tree, but adjacently arranged points among the sorted points may be configured as parent and child nodes. Since the distance between points may be affected by the characteristics of the content of the point cloud data, that is, the density in the 3D space, it is necessary to consider the characteristics of the content in sorting the points.

Accordingly, in performing predictive tree coding, the point cloud data processing device according to the embodiments may adaptively re-sort the points according to the characteristics of the content of the point cloud data. In addition, the point cloud data transmission device according to the embodiments may generate signaling information related to the predictive tree, and transmit a bitstream containing the same to the point cloud data reception device (e.g., the point cloud data reception device described with reference toFIGS.1,13,14and16). The point cloud decoder (e.g., the point cloud decoder described with reference toFIGS.1,13,14, and16) according to the embodiments perform the decoding operation of the predictive tree coding of the point cloud data processing device based on the signaling information related to the predictive tree.

FIG.15is an exemplary block diagram of a point cloud data processing device according to embodiments.

The point cloud data processing device1500shown inFIG.15is an example of the transmission device10000described with reference toFIG.1, the point cloud encoder10002described with reference toFIG.4, or the transmission device described with reference toFIG.12. The point cloud data processing device1500according to the embodiments includes one or more processors1510and one or more memories1520electrically and communicatively coupled with the one or more processors. Although the one or more processors1510are represented as a single block in the figure for simplicity, they may be configured as one or more physically separated hardware processors, or may be configured as a combination of software/hardware or a single hardware processor. The one or more processors1520according to the embodiments may be electrically and communicatively coupled with each other. Although the one or more memories1520are represented as a single block in the figure for simplicity, they may be configured as one or more physically separated memories or as one memory. The one or more memories according to the embodiments may store one or more programs for processing point cloud data.

The one or more processors1510according to the embodiments receive point cloud data (e.g., captured LiDAR data, 3D map data transmitted in real time, etc.) as an input, perform geometry encoding for encoding the geometry, and performs attribute encoding for encoding the attributes based on the encoded geometry. The geometry encoding and attribute encoding according to the embodiments may be performed using a coding algorithm.

The one or more processors1510according to the embodiments may include processors that perform geometry encoding and attribute encoding, respectively.

The geometry encoding of the one or more processors1510according to the embodiments may include one or more of the operations of the coordinate transformer40000, the quantizer40001, the octree analyzer40002, the surface approximation analyzer40003, the arithmetic encoder40004, and the geometry reconstructor40005described with reference toFIG.4, and one or more of the operations of the data input unit12000, the quantization processor12001, the voxelization processor12002, the octree occupancy code generator12003, the surface model processor12004, the intra/inter-coding processor12005, and the arithmetic coder12006.

The attribute encoding of the one or more processors1510according to the embodiments may include one or more of the operations of the color transformer40006, the attribute transformer40007, the RAHT transformer40008, the LOD generator40009, the lifting transformer40010, the coefficient quantizer40011, and/or the arithmetic encoder40012described with reference toFIG.4, and one or more of the operations of the color transform processor12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor12010, and the arithmetic coder12011described with reference toFIG.12.

At least one of the elements of the point cloud encoder described with reference toFIG.4and the elements of the transmission device described with reference toFIG.12may correspond to the process of at least one of the one or more processors1510.

The encoded geometry is output as the geometry bitstream described with reference toFIGS.1to14, and the encoded attribute is output as the attribute bitstream described with reference toFIGS.1to14. The geometry bitstream and the attribute bitstream may be multiplexed into one bitstream and output.

FIG.16is a flowchart illustrating a point cloud data processing method according to embodiments.

The flowchart1600ofFIG.16illustrates an example of a data processing method for the point cloud data processing device1500described with reference toFIG.15. For simplicity, the flowchart1600is expressed based on the elements of the point cloud data processing device described with reference toFIG.15. At least one element disclosed inFIG.16may correspond to the elements of the at least one processor described with reference toFIG.15, the transmission device10000described with reference toFIG.1, the point cloud encoder10002described with reference toFIG.4, and the transmission device described with reference toFIG.12, and the names or functions thereof are not limited to this example.

The data input unit of the point cloud data processing device receives point cloud data (geometry, attributes, and signaling information (or parameters)) (1601).

The coordinate transformer (e.g., the coordinate transformer40000described with reference toFIG.4) of the point cloud data processing device may receive the positions and transforms the same into coordinates (1602). The operation of the coordinate transformer is the same as that described with reference toFIG.4, and thus a detailed description thereof will be omitted.

The geometry information transform quantization processor (e.g., the quantizer40001ofFIG.4and the quantization processor12001ofFIG.12) quantizes the geometry (1603). The operation of the quantization processor is the same as that described with reference toFIGS.4and12, and thus a detailed description thereof will be omitted.

The spatial partitioner may partition the space in which the point cloud data is distributed into tiles, slices, or the like (1604).

The geometry encoder1610performs the geometry encoding described with reference toFIGS.1to14. The elements of the geometry encoder1610and the geometry encoder1610may be configured as a processor, a hardware encoder, software, or a combination of hardware and software as described with reference toFIG.15.

The voxelization processor (e.g., the quantizer40001ofFIG.4and the voxelization processor12002ofFIG.12) included in the geometry encoder1610performs voxelization based on the quantized positions to reconstruct the quantized points (1611). Since the operation of the voxelization processor is the same as that described with reference toFIGS.4and12, a detailed description thereof will be omitted.

The geometry encoder1610determines a geometry coding (geometry encoding) type (1612). As described with reference toFIG.15, the geometry coding includes octree geometry coding, predictive tree coding, and trisoup geometry encoding. When the geometry coding indicates the octree geometry coding, the octree generator generates an octree and performs octree geometry coding (1613-1). When the geometry coding indicates the predictive tree coding, the predictive tree generator generates a predictive tree and performs predictive tree coding (1613-2). When the geometry coding indicates the trisoup geometry coding, the trisoup generator generates an octree and performs trisoup geometry coding (1613-3). The prediction determiner may select an optimal prediction mode by performing rate distortion optimization (RDO) based on the generated predictive tree, and may generate a predicted value based on the selected optimal prediction mode (1614). The geometry position reconstructor (e.g., the geometry reconstructor40005inFIG.4) according to the embodiments reconstructs the geometry processed in at least one of the octree geometry coding, the predictive tree encoding, and the trisoup geometry coding, and outputs the reconstructed geometry (1614). Since the operation of the geometry position reconstructor is the same as that described with reference toFIGS.4and12, a detailed description thereof will be omitted.

The entropy encoder (e.g., the arithmetic coder12006described with reference toFIG.12) entropy-codes a residual value from the predicted value to output a geometry bitstream. Since the operation of the entropy encoder is the same as that described with reference toFIGS.4and12, a detailed description thereof will be omitted. The entropy encoder may be included in the geometry encoder1610.

The reconstructed geometry is delivered to the attribute encoder1630. The attribute encoder1630performs the attribute encoding described with reference toFIGS.1to14. The attribute encoder1630may be configured as a processor, a hardware encoder, software, or a combination of hardware and software as described with reference toFIG.15.

Since the attribute encoding is the same as that described with reference toFIGS.1to14, a detailed description thereof will be omitted. The encoded attribute is output in the form of a bitstream.

As described with reference toFIGS.15to16, the point cloud data processing device (or a prediction determiner) may generate a predictive tree by sorting the points and perform predictive tree coding. The predictive tree coding may be applied at a sequence, tile, or slice level corresponding to the entire point cloud data. The point cloud data processing device according to the embodiments sorts the points according to a reference (e.g., Morton code, azimuth, etc.) and generates a kd tree to generate a predictive tree.

However, since the density of points is not constant according to the characteristics of content, points that are actually closer to each other may be present at the same azimuth or the same radius. That is, when the points are sorted based on only one criterion without considering the characteristics of the content, the optimal order of points for constructing a predictive tree may not be secured. In addition, when the residual secured based on the predictive tree increases, the size of the bitstream may increase, thereby hindering efficient compression.

Accordingly, in performing predictive tree coding, the point cloud data processing device according to the embodiments sorts the points in consideration of the characteristics of the content and generates a predictive tree.

The point cloud data processing device according to the embodiments may sort the points based on various references such as Morton code, radius, azimuth, elevation, LiDAR sensor ID or capture time sequence. The references according to the embodiments are changed according to the characteristics of the content. For example, for content in the form of spinning data captured by LiDAR equipment, it is more efficient to generate a predictive tree by performing sorting based on azimuth. This sorting method is transmitted to the point cloud data reception device through signaling information.

The predictive tree according to the embodiments is generated by at least one or more predictive tree generation methods. A predictive tree generation pseudo code representing a predictive tree generation method is used to represent a coding algorithm used for geometry encoding and geometry decoding. In addition, information related to the predictive tree generation method according to the embodiments is transmitted to the point cloud data reception device through signaling information related to the predictive tree contained in the bitstream.

When the predictive tree is generated based on the order in which points are sorted, the predictive tree generation pseudo code representing the sorted order-based predictive tree generation method according to the embodiments is expressed as follows.Points[ ]: Indicates an array of all pointspointCount: Indicates the total number of points.KDTree (kd tree): Indicates a kd tree used for neighbor node search.1. for (i=0; I<pointCount; i++) {A. P=Points[i]: Indicates the i-th point that is sequentially sorted and input.B. Search the kd tree for a neighbor near the point P.C. If there is no search result, register the point P as a node in the kd tree.D. If there is a search result, check the number of children of the original node connected to the corresponding node in the kd tree. If the number is less than or equal to 3, register the nodes as children of the original node. If the number is greater than 3, check the next nearest nodes. If the number of the nodes is less than or equal to 3, register are the nodes as children of the node.E. Register the predicted results of point P (3results except the point) as nodes of the Kd tree.2.}

In operation E, the predicted results of the point P correspond to the results predicted from a parent node, the results predicted from a grandparent and the parent node, or the results predicted from a grand-grandparent, the grandparent, and the parent node.

A point having the most influence may be set as a root node, and the predictive tree according to the embodiments may be generated based on the influence. The influence according to the embodiments may be determined based on the degree to which other points are selected as neighbor points, similarity to a color registered as a neighbor point, and the like.

A predictive tree generation pseudo code representing a distance-based influence-based predictive tree generation method according to the embodiments a is expressed as follows.Points[ ]: Indicates an array of all pointspointCount: Indicates the total number of points.second_sorted_indexes[ ]: Indicates an index array of points finally sorted according to the above-described pseudo code.KDTree (kd tree): Indicates a kd tree used for neighbor node search.max_distance: the maximum distance allowed for a parent node1. for (i=0; I<pointCount; i++) {A. P=Points[i]: Indicates the i-th point that is sequentially sorted and input.B. Search the Kd tree for a neighbor near the point P.C. If there is no search result, register the point p as a node in the Kd tree.D. If there is a search result,i. When the distance between the three searched points and the target point is less than max_distance, register the points as children a child of the connected (original) node.E. Register the predicted results of point P (the three points except the point) as nodes of the Kd tree.2.}

In operation E, the predicted results of the point P correspond to the results predicted from a parent node, the results predicted from a grandparent and the parent node, or the results predicted from a great-grandparent, the grandparent, and the parent node.

The influence according to the embodiments may be determined according to the number of child nodes of a node to which the target point corresponds. That is, the number of child nodes increases, the node is considered to have more influence. When there are two or more points having the same number of child nodes, the earliest point in the index order of the points may be selected, or a point with the most influence may be selected in consideration of attribute similarity between the node and child nodes.

When the influence is determined based on the number of child nodes, the predictive tree generation pseudo code according to the embodiments is expressed as follows.1. Select a node having the largest number of child nodes as the root node.2. Register three nodes with the largest number of child nodes from the root node as child nodes.3. Repeat the steps until all nodes are registered in the predictive tree (Return to Step 1).

That is, the point cloud data processing device may register three or more child nodes according to the predictive tree generation pseudo code representing the above-described distance-based influence-based predictive tree generation method, and may optionally register three child nodes having the most influence based on the number of child nodes.

Since the predictive tree according to the embodiments is generated by determining a point having the most influence as a root node, points having a small residual are highly likely to be selected as neighbor nodes. Accordingly, the residual value may decrease, and thus the size of the bitstream may be reduced.

The influence according to the embodiments may be determined based on similarity in attribute between points.

Pseudo code for generating a predictive tree based on influence based on attribute similarity according to the embodiments is configured as follows.1. for (i=0; I<pointCount; i++) {A. P=Points[i]: Indicates points that are sequentially input.B. Search the Kd tree for a neighbor near the point P.C. If there is no search result, register the point p as a node in the Kd tree.D. If there is a search result,i. When the distance between the three searched points and the target point is less than max_distance, register the points as children a child of the connected (original) node.E. Register the predicted results of point P (the three points except the point) as nodes of the Kd tree.2.}3. for (i=0; I<pointCount; i++) {i. P=Points[i]ii. Calculate the average of the attributes of the children of the node connected to P (wherein the average may be calculated as an absolute average or a weighted average obtained by applying the distance between nodes as a weight).4.}

The influence according to the embodiments may be determined based on similarity in attribute between a node and child nodes of the node. That is, the influence is considered to increase as the difference between the attribute of a node and the average (or weighted average) attribute of the child nodes decreases. When one or more nodes have high attribute similarity, the point cloud processing device according to the embodiments may generate a predictive tree, considering the above-described distance-based influence as well.

The pseudo code for generating a predictive tree based on attribute similarity according to the embodiments is configured as follows.1. Select a node having the smallest difference between the attribute (e.g., color) of the node and the average of the attributes of the child nodes as the root node.2. If there are several nodes having the smallest attribute difference, select a node having the largest number of child nodes as the root node.3. Register three nodes with high attribute similarity from the root node as child nodes.4. Repeat the steps from step 1 until all nodes are registered in the tree.

That is, the point cloud data processing device may register three or more child nodes according to the predictive tree generation pseudo code representing the above-described distance-based influence-based predictive tree generation method, and may optionally register three child nodes having the most influence based on the attribute similarity.

Since the predictive tree according to the embodiments is constructed from points having a high influence, points having a small residual are likely to be selected as neighbor nodes. The bitstream size may be reduced.

According to embodiments, the attribute similarity may be measured based on at least one of Euclidian color distance measurement, Correlated Color Temperature (CCT) measurement, CIE94 (color difference model) measurement.

The method of measuring the Euclidean color distance according to the embodiments is expressed as the following equation.

color⁢distance=(R⁢2-R⁢1)2+(G⁢2-G⁢1)2+(B⁢2-B⁢1)2

When the attribute indicates a color as described above, the color distance is measured based on the difference between RGB values of two points (e.g. R1, G1, and B1, which are the RGB values of the first point and R2, G2, and B2, which are the RGB values of the second point).
reflectance distance|R2−R1|

When the attribute indicates reflectance as described above, the reflectance distance is measured based on the difference between the RGB values of the two points.

The CCT measurement method according to the embodiment is expressed as the following equations.

X=(-0.14282)⁢R+(1.54924)⁢G+(-0.95641)⁢BY=(-0.32466)⁢R+(1.57837)⁢G+(-0.73191)⁢B=illunimanceZ=(-0.68202)⁢R+(0.77073)⁢G+(0.56332)⁢Bx=X(X+Y+Z)⁢y=Y(X+Y+Z)

In the equations, X, Y, and Z denote CIE tristimulus values, and R, G, and B denote RBG values. (x,y) denotes colorimeter chromaticity coordinates. The CCT is calculated by changing RGB values to CIE (XYZ) values and normalizing the changed values to chromatic values. The constants included in the equations are elements in the correlation matrix. The CCT is determined based on the absolute error value less than 2 degree Kelvin and Mc Camy's formula for a specific (x,y) as shown in the following equation.

CCT=449⁢n3+3525⁢n2+6823.3n+5520.33n=(x-0.332)(0.1858-y)

The CIE94 (color difference model) measurement method according to the embodiments is expressed as the following equation.

Δ⁢E⁢94=(Δ⁢LKL⁢SL)2+(Δ⁢CabKC⁢SC)2+(Δ⁢HabKL⁢SL)2Δ⁢L=L1-L2C⁢1=a⁢12+b⁢12,C⁢2=a⁢22+b⁢22,Cab=C⁢1-C⁢2Δ⁢Hab=Δ⁢Eab2-Δ⁢L2-Δ⁢Cab2=Δ⁢a2-Δ⁢b2-Δ⁢Cab2Δ⁢a=a⁢1-a⁢2,Δ⁢b=b⁢1-b⁢2SL=1,Sc=1+K1⁢C1,SH=1+K2⁢C1

Here, E94 denotes the distance matrix delta E of the CIE94 model. Parameters L, a and b are parameters of the CIELAB color space representing represent the colors C1 and C2, respectively. Habcorresponds to the arithmetic mean of the code lengths of identical chroma circles of two colors.

A predictive tree according to embodiments may be generated by registering an N-th point (e.g., a point corresponding to a center index) as a root node based on the order of the sorted points and traversing the points in order of right and left. A predictive tree generation pseudo code representing a method of generating a predictive tree based on the center position corresponding to the center index or the N-th position is expressed as follows.ctrIdx: Indicates the center index or the N-th index of the sorted points.prevIdx: Indicates the previous index.pointCount: Indicates the total number of points.1. ctrIdx=pointCount/2: Indicates a point corresponding to the center index (half of the total number of points) or the N-th index.2. direction=start traversal from the center index to right.3. prevIdx=ctrIdx: Indicates that the previous index is the center index or an N-th index.4. for (i=ctrIdx; (i>=0) && (i<pointCount);) {A. P=Points[i]: Indicates the i-th point corresponding to ctrIdx, where i is greater than or equal to 0 and less than the total number of points.B. Search the Kd tree for a neighbor near the point P.C. If there is no search result, register the point p as a node in the Kd tree.D. If there is a search result, check the number of children of the original node connected to the corresponding node in the kd tree. If the number is less than or equal to 3, register the nodes as children of the original node. If the number is greater than 3, check the next nearest node. If the number of the nodes is less than or equal to 3, register the nodes as children of the node.E. Register the predicted results of point P (three points except the point) as nodes of the Kd tree.F. If direction=right:1. direction=left: traverse to left, i=prevIdx−1G. else1. direction=right: traverse to right, i=prevIdx+1H. prevIdx=i5.}

The predicted results of the point P correspond to the results predicted from a parent node, the results predicted from a grandparent node and the parent node, or the results predicted from a great-grandparent node, the grandparent node, and the parent node. Since the order of inputs to the Kd tree spreads to both side of the sorted points, the probability of finding closer points is high. Accordingly, the size of the bitstream may be reduced by decreasing the residual.

The geometry encoder1610(or the predictive tree generator) of the point cloud data transmission device (e.g., the point cloud data processing device ofFIG.15) according to the embodiments may receive the entire sequence of the bitstream (the points transmitted by the bitstream) and/or signaling information related to the predictive tree for a partial point cloud such as tiles or slices, and perform predictive tree coding (or predictive tree geometry coding). For example, the point cloud data transmission device may receive information about a point sorting method (e.g., Morton code order, Radius order, etc.) and sort the points. In addition, the point cloud data transmission device may receive information about the above-described predictive tree generation method, and generate a predictive tree using a coding algorithm (e.g., the above-described predictive tree generation pseudo code) according to the input information. The predictive tree generation method according to the embodiments corresponds to at least one of the above-described sorted order-based predictive tree generation, distance-based influence-based predictive tree generation, attribute similarity-based influence-based predictive tree generation, or predictive tree generation based on the N-th position or center position in the sorted order. The predictive tree generation method may be selected based on the characteristics of content of point cloud data, a service type, and the like. When a predictive tree is to be generated using the method of distance-based influence-based predictive tree generation, the point cloud data transmission device according to the embodiments receives an input of the maximum distance (e.g., max_distance) allowed for a parent node. The point cloud data transmission device may search for neighbor predicted points for selecting a parent node, and register only points having a distance from the searched points less than the maximum distance as child nodes. The point cloud data transmission device according to embodiments may automatically set or calculate the maximum distance by analyzing the content.

The point cloud data transmission device may receive information related to an attribute similarity measurement method (e.g., Euclidean color distance measurement, CCT measurement, CIE94 measurement, etc.) and calculate the attribute similarity. The point cloud data transmission device according to the embodiments may measure the attribute similarity to generate a predictive tree based on at least one of the method of distance-based influence-based predictive tree generation and the method of attribute similarity-based influence-based predictive tree generation.

To generate a predictive tree using the predictive tree generation method based on the N-th position or center position in the sorted order, the point cloud data transmission device receives information related to the value of N as an input.

In addition, the point cloud data transmission device according to the embodiments may generate signaling information related to the predictive tree and transmit a bitstream containing the same to the point cloud data reception device (e.g., the point cloud data reception device described with reference toFIGS.1,13,14and16). Based on the signaling information related to the predictive tree, the point cloud decoder according to the embodiments (e.g., the point cloud decoder described with reference toFIGS.1,13,14, and16) may perform predictive tree decoding (or predictive tree geometry decoding), which is a decoding operation of the predictive tree coding of the point cloud data processing device.

FIG.17illustrates a configuration of a bitstream according to embodiments.

A point cloud data processing device (e.g., the transmission device described with reference toFIGS.1,12and14) may transmit encoded point cloud data in the form of a bitstream. The bitstream is a sequence of bits that form a representation of point cloud data (or a point cloud frame).

The point cloud data (or point cloud frame) may be partitioned into tiles and slices.

The point cloud data may be partitioned into multiple slices and encoded within the bitstream. A slice is a set of points, and is expressed as a series of syntax elements representing the entirety or a part of the encoded point cloud data. A slice may or may not have a dependency on other slices. Also, a slice may include one geometry data unit, and may have one or more attribute data units or zero attribute data unit. As described above, the attribute encoding is performed based on the geometry encoding, and accordingly the attribute data units are based on the geometry data unit within the same slice. In other words, the point cloud data reception device (e.g., the reception device10004or the point cloud video decoder10006) may process the attribute data based on the decoded geometry data. Therefore, in a slice, the geometry data unit precedes the associated attribute data units. Data units within a slice are necessarily consecutive, and the order of slices is not specified.

A tile is a (three-dimensional) rectangular cuboid within a bounding box (e.g., the bounding box described with reference toFIG.5). The bounding box may contain one or more tiles. A tile may completely or partially overlap another tile. A tile may include one or more slices.

Accordingly, the point cloud data processing device may provide high-quality point cloud content by processing data corresponding to tiles according to importance. That is, the point cloud data processing device according to the embodiments may perform point cloud compression coding with better compression efficiency and appropriate latency on data corresponding to a region important to the user.

A bitstream according to embodiments contains signaling information and a plurality of slices (slice 0, . . . , slice n). As shown in the figure, the signaling information precedes the slices in the bitstream. Accordingly, the point cloud data reception device may first obtain the signaling information and sequentially or selectively process the plurality of slices based on the signaling information. As shown in the figure, slice0 contains one geometry data unit (Geom00) and two attribute data units (Attr00and Attr10). The geometry data unit precedes the attribute data units within the same slice. Accordingly, the point cloud data reception device first processes (decodes) the geometry data unit (or geometry data), and processes the attribute data units (or attribute data) based on the processed geometry data. The signaling information according to the embodiments may be referred to as signaling data, metadata, or the like, and is not limited to the examples. In addition, the signaling information according to the embodiments may be generated by the point cloud data transmission device (e.g., the metadata processor12007described with reference toFIG.12), and may be parsed and used for decoding by the point cloud data reception device (e.g., the metadata parser13006or the like described with reference toFIG.13).

According to embodiments, the signaling information includes a sequence parameter set (SPS), a geometry parameter set (GPS), and one or more attribute parameter sets (APSs). The SPS is encoding information about the entire sequence, such as a profile or a level, and may include comprehensive information (sequence level) about the entire sequence, such as a picture resolution and a video format. The GPS is information about geometry encoding applied to geometry included in the sequence (bitstream). The GPS may include information about an octree (e.g., the octree described with reference toFIG.6) and information about an octree depth. The APS is information about attribute encoding applied to an attribute contained in the sequence (bitstream). As shown in the figure, the bitstream contains one or more APSs (e.g., APS0, APS1, . . . shown in the figure) according to an identifier for identifying the attribute.

According to embodiments, the signaling information may further include information about a tile (e.g., tile inventory). The information about the tile may include a tile identifier and information about a tile size. According to embodiments, the signaling information is applied to a corresponding bitstream as information about a sequence, that is, a bitstream level. In addition, the signaling information has a syntax structure including a syntax element and a descriptor describing the same. A pseudo code may be used to describe the syntax. In addition, the point cloud reception device (e.g., the reception device10004ofFIG.1, the point cloud decoder ofFIGS.10and11, or the reception device ofFIG.13) may sequentially parse and process syntax elements presented in the syntax.

Although not shown in the figure, the geometry data unit and the attribute data unit include a geometry header and an attribute header, respectively. The geometry header and the attribute header are signaling information applied at a corresponding slice level and have the above-described syntax structure.

The geometry header contains information (or signaling information) for processing a corresponding geometry data unit. Therefore, the geometry header is at the leading position in the geometry data unit. The point cloud reception device may process the geometry data unit by parsing the geometry header first. The geometry header has an association with the GPS, which contains information about the entire geometry. Accordingly, the geometry header contains information specifying gps_geom_parameter_set_id included in the GPS. The geometry header also contains tile information (e.g., tile_id), a tile identifier, and the like related to a slice to which the geometry data unit belongs.

The attribute header contains information (or signaling information) for processing a corresponding attribute data unit. Accordingly, the attribute header is at the leading position in the attribute data unit. The point cloud reception device may process the attribute data unit by parsing the attribute header first. The attribute header has an association with the APS, which contains information about all attributes. Accordingly, the attribute header contains information specifying aps_attr_parameter_set_id included in the APS. As described above, attribute decoding is based on geometry decoding. Accordingly, the attribute header contains information specifying a slice identifier contained in the geometry header in order to determine a geometry data unit associated with the attribute data unit.

In the case where the point cloud transmission device performs predictive tree coding based on the predictive tree generation method described with reference toFIGS.15and16, the signaling information in the bitstream may further include signaling information related to a predictive tree (e.g., information about a point sorting method, a predictive tree generation method, etc.). The signaling information related to the predictive tree according to the embodiments may be included in the signaling information (e.g., SPS, APS, etc.) of a sequence level, a slice level (e.g., the attribute header, etc.), an SEI message, or the like. The point cloud reception device according to the embodiments (e.g., the reception device10004ofFIG.1, the point cloud decoder ofFIGS.10and11, or the reception device ofFIG.13) may receive a bitstream, and perform predictive tree decoding, which is a decoding operation of predictive tree coding, based on the signaling information related to the predictive tree. Specifically, when the signaling information related to the predictive tree is signaled at the sequence level, the point cloud reception device performs predictive tree decoding on the point cloud data corresponding to the sequence of the bitstream based on the signaling information related to the predictive tree. When the signaling information related to the predictive tree is signaled at the tile level, the point cloud reception device performs predictive tree decoding on the point cloud data corresponding to the tile based on the signaling information related to the predictive tree. When the signaling information related to the predictive tree is signaled at the slice level, the point cloud reception device performs predictive tree decoding on the point cloud data corresponding to the slice based on the signaling information related to the predictive tree. Accordingly, the point cloud reception device may perform predictive tree decoding on all or some regions (e.g., tiles, slices, etc.) of the point cloud data based on the signaling information.

FIG.18shows an example of a syntax structure of signaling information related to a predictive tree.

FIG.18shows an example of a syntax structure of an SPS. In the example, the signaling information related to a predictive tree is included in an SPS of a sequence level.

profile_idc: Indicates a profile to which the current bitstream conforms.

profile_compatibility_flags: Indicates whether there is a profile compatible with the profile to which the bitstream conforms.

sps_num_attribute_sets: Indicates the number of encoded attributes contained in the bitstream. The value of sps_num_attribute_sets shall be in the range of 0 to 63.

attribute_dimension[i]: Indicates the number of components of the i-th attribute.

attribute_instance_id[i]: Indicates the instance id of the i-th attribute.

pred_geom_tree_sorting_type: Indicates a point sorting method to be applied in generating a predictive geometry tree in a corresponding sequence.

pred_geom_tree_sorting_type may have values from 0 to 6, each of which indicates the following sorting method.0=no sorting1=Sorting in Morton code order2=Sorting in radius order3=Sorting in azimuth order4=Sorting in elevation order5=Sorting in order of sensor ID6=Sorting in capture time order

The values of pred_geom_tree_sorting_type are changeable and are not limited to the above example.

When the value of pred_geom_tree_sorting_type is greater than 0, the syntax of the SPS further includes pred_geom_tree_sorting_ascending_flag.

pred_geom_tree_sorting_ascending_flag: Indicates whether to sort points in ascending order (TRUE) or in descending order (FALSE) when the points are sorted first in generating a predictive tree (also called a predictive geometry tree) in the corresponding sequence. When the value of pred_geom_tree_sorting_ascending_flag is TRUE, it indicates that the points are sorted in ascending order in generating the predictive tree. When the value of pred_geom_tree_sorting_ascending_flag is FALSE, it indicates that the points are sorted in descending order in generating the predictive tree. The values of pred_geom_tree_sorting_ascending_flag are not limited to this example and may be changed.

pred_geom_tree_build_method: Indicates a method of generating a predictive tree (or a predictive geometry tree) in the sequence.

pred_geom_tree_build_method may have values from 0 to 4, each of which indicates a predictive tree generation method:0=Sorted order-based predictive tree generation;1=Distance-based influence-based predictive tree generation;2=Attribute similarity-based influence-based predictive tree generation;3=Zigzag direction-based predictive tree generation based on the N-th position in sorted order;4=Zigzag direction-based predictive tree generation based on the center position in sorted order.

The values of pred_geom_tree_build_method are changeable and are not limited to the above example. Details of the method of generating a predictive tree according to the embodiments is the same as those described with reference toFIGS.15to16, and thus a description thereof will be omitted.

When pred_geom_tree_build_method indicates the method of distance-based influence-based predictive tree generation for generating a predictive tree based on distance-based influence or attribute similarity-based influence-based predictive tree generation, the syntax of the SPS further includes neighbor_attr_different_method.

neighbor_attr_different_method: Indicates an attribute similarity measurement method to be applied in the corresponding sequence. neighbor_attr_different_method has values from 1 to 3, and each value indicates a measurement method as follows:1=Euclidean color distance measurement;2=Correlated color temperature (CCT) measurement;3=CIE94 (color difference model) measurement.

Since the attribute similarity measurement method is the same as the above-described equation, a detailed description thereof will be omitted.

When pred_geom_tree_build_method indicates the method of zigzag direction-based predictive tree generation based on the N-th position in sorted order, the syntax of the SPS includes pred_geom_tree_N_idx.

pred_geom_tree_N_idx: Indicates the position (e.g., the N-th index) of a root node to be applied in the sequence.

The SPS syntax according to the embodiments is not limited to the above example, and may further include additional elements or may exclude some of the elements shown in the figure for efficiency of signaling. Some elements may be signaled through signaling information (e.g., APS, attribute header, etc.) other than the SPS or through an attribute data unit.

FIG.19shows an example of a syntax structure of signaling information related to a predictive tree.

FIG.19shows an example of a syntax structure of a GPS. In the example, signaling information related to a predictive tree is included in a sequence level GPS. The syntax of the GPS includes the following elements.

gps_geom_parameter_set_id: Indicates an identifier for the GPS for reference by other syntax elements. The value of gps_geom_parameter_set_id shall be in the range of 0 to 15, inclusive.

gps_seq_parameter_set_id: Specifies sps_seq_parameter_set_id for the active SPS. The value of gps_seq_parameter_set_id shall be in the range of 0 to 15, inclusive.

The syntax of the GPS further includes signaling information related to the predictive tree described with reference toFIG.18. Details of the signaling information related to the predictive tree is the same as those described with reference toFIG.18, and thus a description thereof will be omitted. The GPS syntax according to the embodiments is not limited to the above example, and may further include additional elements or may exclude some of the elements shown in the figure for efficiency of signaling. Some elements may be signaled through signaling information (e.g., APS, attribute header, etc.) other than the GPS or through an attribute data unit.

FIG.20shows an example of a syntax structure of signaling information related to a predictive tree.

FIG.20shows an example of a syntax structure of a tile parameter set (TPS). In the example, signaling information related to a predictive tree is included in a tile level TPS. The syntax of the TPS includes the following elements.num_tiles: Indicates the number of tiles signaled for the bitstream. When this information is not present, num_tiles is 0.

The following is information signaled for each tile (i) of tiles as many as the number indicated by num_tiles.tile_bounding_box_offset_x[i]: Indicates the x-axis offset of the i-th tile in the Cartesian coordinates. When this information is not present, the offset of the 0th tile, tile_bounding_box_offset_x[0], is inferred to be sps_bounding_box_offset_x, which indicates the x-axis offset of the sequence level bounding box.tile_bounding_box_offset_y[i]: Indicates the y-axis offset of the i-th tile in the Cartesian coordinates. When this information is not present, the offset of the 0th tile, tile_bounding_box_offset_y[0], is inferred to be sps_bounding_box_offset_y, which indicates the y-axis offset of the sequence level bounding box.

The syntax of the tile parameter set further includes signaling information related to the predictive tree described with reference toFIG.18. Details of the signaling information related to the predictive tree is the same as those described with reference toFIG.18, and thus a description thereof will be omitted. However, the signaling information has a difference in that the signaling information related to the predictive tree included in the syntax of the tile parameter set is signaled for each tile. Accordingly, the point cloud data reception device (e.g., the point cloud data reception device described with reference toFIGS.1,13,14, and16) may perform predictive tree decoding for each tile based on the signaling information related to the predictive tree.

The syntax of the TPS according to the embodiments is not limited to the above example, and may further include additional elements or may exclude some of the elements shown in the figure for efficiency of signaling. Some elements may be signaled through signaling information (e.g., APS, attribute header, etc.) other than the TPS or through an attribute data unit.

FIG.21shows an example of a syntax structure of signaling information related to a predictive tree.

FIG.21shows an example of a syntax structure of a geometry slice header. In the example, signaling information related to a predictive tree is included in a geometry slice header of a slice level. The syntax of the geometry slice header includes the following elements.gsh_geometry_parameter_set_id: Specifies the value of gps_geom_parameter_set_id of the active GPS.gsh_tile_id: Specifies the value of the tile_id that is referred to by the geometry slice header. The value of gsh_tile_id shall be in the range of 0 to any value.gsh_slice_id: Identifies the slice header for reference in other syntaxes. The value of gsh_slice_id falls within the range from 0 to any value.

The syntax of the geometry slice header further includes signaling information related to the predictive tree described with reference toFIG.18. Details of the signaling information related to the predictive tree is the same as those described with reference toFIG.18, and thus a description thereof will be omitted. However, the signaling information has a difference in that the signaling information related to the predictive tree included in the syntax of the geometry slice header is signaled for each slice. Accordingly, the point cloud data reception device (e.g., the point cloud data reception device described with reference toFIGS.1,13,14, and16) may perform predictive tree decoding for each slice based on the signaling information related to the predictive tree.

The syntax of the geometry slice header according to the embodiments is not limited to the above example, and may further include additional elements or may exclude some of the elements shown in the figure for efficiency of signaling. Some elements may be signaled through signaling information (e.g., attribute header, etc.) other than the geometry slice header or through an attribute data unit.

FIG.22is an exemplary block diagram of a point cloud data processing device according to embodiments.

The point cloud data processing device2400shown inFIG.22is an example of the reception device10004described with reference toFIG.1, the point cloud decoder described with reference toFIGS.10and11, or the reception device described with reference toFIG.13. The point cloud data processing device2400shown inFIG.22performs a decoding operation corresponding to the operation of the point cloud data processing device1500described with reference toFIG.15. The point cloud data processing device2400according to the embodiments includes one or more processors2410and one or more memories2420electrically and communicatively coupled with the one or more processors. Although the one or more processors2410are represented as a single block in the figure for simplicity, they may be configured as one or more physically separated hardware processors, or may be configured as a combination of software/hardware or a single hardware processor. The one or more processors2420according to the embodiments may be electrically and communicatively coupled with each other. Although the one or more memories2420are represented as a single block in the figure for simplicity, they may be configured as one or more physically separated memories or as one memory. The one or more memories according to the embodiments may store one or more programs for processing point cloud data.

The one or more processors2410according to the embodiments receive the bitstream described with reference toFIGS.1to21, and secure signaling information (SPS, GPS, APS, etc.) contained in the bitstream. In addition, the one or more processors2410perform geometry decoding for decoding a geometry (or a geometry bitstream), and decode an attribute (or an attribute bitstream) contained in the bitstream based on the decoded geometry. The geometry decoding according to the embodiments corresponds to the geometry encoding described with reference toFIGS.1to23, and the attribute decoding according to the embodiments corresponds to the attribute encoding described with reference toFIGS.1to23. The geometry decoding and attribute decoding according to embodiments may be performed in the unit of the entire sequence or per tile or slice according to a level at which the signaling information is signaled. Also, the geometry decoding and attribute decoding may be performed using a coding algorithm (e.g., the coding algorithm described with reference toFIGS.15and16).

The one or more processors2410according to the embodiments may include processors that perform geometry decoding and attribute decoding, respectively.

The geometry decoding of the one or more processors2410according to the embodiments may include one or more of the operations of the arithmetic decoder11000, the octree synthesizer11001, the surface approximation synthesizer11002, the geometry reconstructor11003, and the coordinate inverse transformer11004described with reference toFIG.11. The attribute decoding of the one or more processors2410according to the embodiments may include one or more of the operations of the arithmetic decoder11005, the inverse quantizer11006, the RAHT transformer11007, the LOD generator11008, the inverse lifter11009, and/or the color inverse transformer11010described with reference toFIG.11, and one or more of the operations of the color transform processor12008, the attribute transform processor12009, the prediction/lifting/RAHT transform processor12010, and the arithmetic coder12011. In addition, the one or more processors2410according to the embodiments may perform predictive tree decoding based on the above-described predictive tree generation pseudo code.

At least one of the elements of the point cloud encoder described with reference toFIGS.10to11and the elements of the reception device described with reference toFIG.13may correspond to the process of at least one of the one or more processors1510. Although not shown in the figure, the decoded geometry and decoded attributes are transferred to the renderer. The content of the point cloud data may be displayed on the display based on the rendered geometry and attributes.

FIG.23is a flowchart illustrating a point cloud data processing method according to embodiments.

The flowchart2500ofFIG.23illustrates an example of a data processing method for the point cloud data processing device2400described with reference toFIG.22. For simplicity, the flowchart2500is expressed based on the elements of the point cloud data processing device described with reference toFIG.22. At least one element disclosed inFIG.23may correspond to the elements of the at least one processor described with reference toFIG.22, the reception device10004described with reference toFIG.1, the point cloud decoder described with reference toFIGS.10and11, and the reception device described with reference toFIG.13, and the names or functions thereof are not limited to this example.

The point cloud data processing device may process a geometry bitstream and an attribute bitstream, respectively. As described with reference toFIGS.1to22, the geometry bitstream and the attribute bitstream may be multiplexed into one bitstream and transmitted/received. The point cloud data processing device (e.g., the reception processor130010) may secure the geometry bitstream and the attribute bitstream from the received bitstream. The point cloud data processing device (e.g., the metadata parser13006) according to the embodiments may secure the signaling information contained in the bitstream described with reference toFIGS.17to21. The point cloud data processing device may process the geometry bitstream and the attribute bitstream simultaneously, or may process the geometry bitstream first, and then process the attribute bitstream based on the reconstructed geometry.

The geometry decoder2510of the point cloud data processing device may decode the geometry bitstream. The geometry decoder2510performs the geometry decoding described with reference toFIGS.1to22. The geometry decoder2510and the elements of the geometry decoder2510may include a processor, a hardware encoder, software, or a combination of hardware and software as described with reference toFIG.22.

The geometry entropy decoder (e.g., the arithmetic decoder11000ofFIG.11, the arithmetic decoder13002ofFIG.13) included in the geometry decoder2510may decode the geometry in the geometry bitstream based on arithmetic coding (2511). The operation of the geometry entropy decoder is the same as that described with reference toFIGS.11and13, and thus a detailed description thereof will be omitted.

The geometry decoder2510according to the embodiments may check the geometry coding type applied to the geometry based on the signaling information described with reference toFIGS.19to22(2512). As described above, the geometry coding includes octree geometry coding, predictive tree coding, and trisoup geometry encoding. When the geometry coding type indicates octree geometry coding, the octree reconstructor performs octree-based coding (decoding), which is a decoding operation of the octree geometry coding (2513-1). When the geometry coding type indicates predictive tree coding, the predictive tree reconstructor generates the predictive tree described with reference toFIGS.15to22and performs predictive tree coding (decoding) (2513-2). The predictive tree reconstructor according to the embodiments performs predictive tree decoding (or predictive tree geometry decoding), which is a decoding operation of predictive tree coding. The predictive tree reconstructor according to the embodiments may perform the same operation as the operation of the predictive tree generator described with reference toFIG.16. For example, the predictive tree reconstructor may sort the points using the coding algorithm described with reference toFIGS.15and16based on the signaling information related to the predictive tree described with reference toFIGS.17to21, and generate a predictive tree according to a predictive tree generation method to perform predictive tree decoding. The signaling information related to the predictive tree and the predictive tree generation are the same as those described with reference toFIGS.15to22, and thus a description thereof will be omitted. Also, the name of the predictive tree decoding according to the embodiments is not limited to this example.

The geometry position reconstructor according to the embodiments (e.g., the geometry reconstructor11003described with reference toFIG.11) reconstructs a geometry position. The operation of the geometry position reconstruction is the same as that described with reference toFIGS.10,11and13, and thus a detailed description thereof will be omitted.

The geometry predictor outputs a reconstructed geometry by performing predictive coding on the geometry whose position is reconstructed (2515). The reconstructed geometry is transmitted to the attribute decoder2520.

The geometry transform inverse quantization processor (e.g., the inverse quantization processor13005described with reference toFIG.13) inversely quantizes the geometry. The operation of the geometry transform inverse quantization processor is the same as that described with reference toFIGS.10,11and13, and thus a detailed description thereof will be omitted.

The coordinate inverse transformer (e.g., the coordinate inverse transformer11004described with reference toFIG.11) may acquire positions of points by transforming the coordinates of the geometry. The operation of the coordinate inverse transformer is the same as that described with reference toFIGS.10,11and13, and thus a detailed description thereof will be omitted.

The attribute decoder2520performs the attribute decoding described with reference toFIGS.1to22. The attribute decoder2520may include a processor, a hardware encoder, software, or a combination of hardware and software as described with reference toFIG.22. The attribute decoding is the same as that described with reference toFIGS.1,10,11, and13, and thus a detailed description thereof will be omitted. The color inverse transform processor (e.g., the color inverse transformer11010ofFIG.11) performs inverse transform coding to inversely transform color values (or textures) included in decoded attributes. The color inverse transform processor may be included in the attribute decoder2520. The operation of the color inverse conversion processor is the same as that described with reference toFIGS.10,11and13, and thus a detailed description thereof will be omitted.

The order of operations/processes of the point cloud data processing device illustrated in the flowchart2500ofFIG.23may be changed, and is not limited to this example.

FIG.24illustrates a point cloud data processing method according to embodiments.

The flowchart2600ofFIG.24illustrates a processing method for a device for processing point cloud data (e.g., the point cloud data processing device1500ofFIG.15).

The device according to the embodiments includes one or more processors for processing point cloud data and one or more memories coupled with the one or more processors.

The device (or one or more processors) according to the embodiments may encode the geometry of the point cloud data (2610). The geometry represents the positions of points in the point cloud data. The encoding of the geometry according to the embodiments may be performed based on any one of an octree, a predictive tree, and a trisoup structure. The method of encoding the geometry according to the embodiments is the same as the geometry encoding described with reference toFIGS.1to23, and thus a detailed description thereof will be omitted.

The device according to the embodiments may encode an attribute of the point cloud data based on the decoded geometry (2620). The attribute indicates at least one of reflectance and color of the points. The method of encoding the attribute according to the embodiments is the same as the attribute encoding described with reference toFIGS.1to23, and thus a detailed description thereof will be omitted.

The encoded geometry and encoded attributes are output as a bitstream (e.g., the bitstream described with reference toFIG.19). The bitstream according to the embodiments contains the signaling information described with reference toFIGS.17to21.

The signaling information according to the embodiments may include information related to a predictive tree. The information related to the predictive tree may include type information (e.g., pred_geom_tree_sorting_type described with reference toFIGS.18to21) indicating the sorting type of points and information indicating a method for generating a predictive tree (e.g., pred_geom_tree_building_method described with reference toFIGS.18to21). The information indicating the method for generating the predictive tree corresponds to at least one of a method of generating the predictive tree based on a sorting order of the points, a method of generating the predictive tree based on an influence based on a distance between the points, a method of generating the predictive tree based on an influence based on attribute similarity between the points, a method of generating the predictive tree based on an N-th position in the sorting order of the points, or a method of generating the predictive tree based on a center position in the sorting order of the points, as described with reference toFIG.16. The information related to the predictive tree according to the embodiments may be signaled at at least one of a sequence level, a tile level, and a slice level of the bitstream as described with reference toFIGS.18to21, according to the level at which the predictive tree generation-based geometry coding (or the predictive tree geometry coding) described with reference toFIG.16is applied. Details of the bitstream and the signaling information according to the embodiments are the same as those described with reference toFIGS.17to21, and thus a description thereof will be omitted.

The order of the operations of the point cloud data processing method illustrated in the flowchart2600ofFIG.24may be changed and is not limited to this example.

FIG.25illustrates a point cloud data processing method according to embodiments.

The flowchart2700ofFIG.25shows a processing method for a device for processes point cloud data (e.g., the point cloud data processing device2200ofFIG.15).

The device according to embodiments includes one or more processors for processing point cloud data and one or more memories coupled with the one or more processors.

The device according to the embodiments may decode the geometry of the point cloud data (2710). The geometry represents the positions of points in the point cloud data. The decoding of the geometry according to the embodiments may be performed based on any one of an octree, a predictive tree, and a trisoup structure. The method of decoding the geometry according to the embodiments is the same as the geometry decoding described with reference toFIGS.1to23, and thus a detailed description thereof will be omitted.

The device according to the embodiments decodes an attribute of the point cloud data based on the decoded geometry (2720). The attribute indicates at least one of reflectance and color of the points. The method of decoding the attribute according to the embodiments is the same as the attribute decoding described with reference toFIGS.1to23, and thus a detailed description thereof will be omitted.

The point cloud data according to the embodiments is received in the form of a bitstream described with reference toFIG.17, and the bitstream contains signaling information. The information related to the predictive tree according to the embodiments may include type information (e.g., pred_geom_tree_sorting_type described with reference toFIGS.18to21) indicating the sorting type of points and information indicating a method for generating a predictive tree (e.g., pred_geom_tree_building_method described with reference toFIGS.18to21). The information indicating the method for generating the predictive tree corresponds to at least one of a method of generating the predictive tree based on a sorting order of the points, a method of generating the predictive tree based on an influence based on a distance between the points, a method of generating the predictive tree based on an influence based on attribute similarity between the points, a method of generating the predictive tree based on an N-th position in the sorting order of the points, or a method of generating the predictive tree based on a center position in the sorting order of the points, as described with reference toFIG.16. The information related to the predictive tree according to the embodiments may be signaled at at least one of a sequence level, a tile level, and a slice level of the bitstream as described with reference toFIGS.18to21, according to the level at which the predictive tree generation-based geometry coding (or the predictive tree geometry coding) described with reference toFIG.16is applied. Details of the bitstream and the signaling information according to the embodiments are the same as those described with reference toFIGS.17to21, and thus a description thereof will be omitted. The flow of the point cloud data processing method shown in the flowchart2700ofFIG.25may be changed, and is not limited to this example.

The components of the point cloud data processing devices according to the embodiments described with reference toFIGS.1to25may be implemented as hardware, software, firmware, or a combination thereof including one or more processors coupled with a memory. The components of the devices according to the embodiments may be implemented as a single chip, for example, a single hardware circuit. Alternatively, the components of the point cloud data processing devices according to the embodiments may be implemented as separate chips. In addition, at least one of the components of the point cloud data processing devices according to the embodiments may include one or more processors capable of executing one or more programs, wherein the one or more programs may include are instructions that execute or are configured to execute one or more of the operations/methods of the point cloud data processing devices described with reference toFIGS.1to25.

Although embodiments have been described with reference to each of the accompanying drawings for simplicity, it is possible to design new embodiments by merging the embodiments illustrated in the accompanying drawings. If a recording medium readable by a computer, in which programs for executing the embodiments mentioned in the foregoing description are recorded, is designed by those skilled in the art, it may also fall within the scope of the appended claims and their equivalents. The devices and methods may not be limited by the configurations and methods of the embodiments described above. The embodiments described above may be configured by being selectively combined with one another entirely or in part to enable various modifications. Although preferred embodiments have been described with reference to the drawings, those skilled in the art will appreciate that various modifications and variations may be made in the embodiments without departing from the spirit or scope of the disclosure described in the appended claims. Such modifications are not to be understood individually from the technical idea or perspective of the embodiments.

Descriptions of methods and devices may be applied so as to complement each other. For example, the point cloud data transmission method according to the embodiments may be carried out by the point cloud data transmission device or components included in the point cloud data transmission device according to the embodiments. Also, the point cloud data reception method according to the embodiments may be carried out by the point cloud data reception device or components included in the point cloud data reception device according to the embodiments.

Various elements of the devices of the embodiments may be implemented by hardware, software, firmware, or a combination thereof. Various elements in the embodiments may be implemented by a single chip, for example, a single hardware circuit. According to embodiments, the components according to the embodiments may be implemented as separate chips, respectively. According to embodiments, at least one or more of the components of the device according to the embodiments may include one or more processors capable of executing one or more programs. The one or more programs may perform any one or more of the operations/methods according to the embodiments or include instructions for performing the same. Executable instructions for performing the method/operations of the device according to the embodiments may be stored in a non-transitory CRM or other computer program products configured to be executed by one or more processors, or may be stored in a transitory CRM or other computer program products configured to be executed by one or more processors. In addition, the memory according to the embodiments may be used as a concept covering not only volatile memories (e.g., RAM) but also nonvolatile memories, flash memories, and PROMs. In addition, it may also be implemented in the form of a carrier wave, such as transmission over the Internet. In addition, the processor-readable recording medium may be distributed to computer systems connected over a network such that the processor-readable code may be stored and executed in a distributed fashion.

In this document, the term “/“and”,” should be interpreted as indicating “and/or.” For instance, the expression “A/B” may mean “A and/or B.” Further, “A, B” may mean “A and/or B.” Further, “A/B/C” may mean “at least one of A, B, and/or C.” “A, B, C” may also mean “at least one of A, B, and/or C.” Further, in the document, the term “or” should be interpreted as “and/or.” For instance, the expression “A or B” may mean 1) only A, 2) only B, and/or 3) both A and B. In other words, the term “or” in this document should be interpreted as “additionally or alternatively.”

Terms such as first and second may be used to describe various elements of the embodiments. However, various components according to the embodiments should not be limited by the above terms. These terms are only used to distinguish one element from another. For example, a first user input signal may be referred to as a second user input signal. Similarly, the second user input signal may be referred to as a first user input signal. Use of these terms should be construed as not departing from the scope of the various embodiments. The first user input signal and the second user input signal are both user input signals, but do not mean the same user input signal unless context clearly dictates otherwise.

The terminology used to describe the embodiments is used for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments. As used in the description of the embodiments and in the claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. The expression “and/or” is used to include all possible combinations of terms. The terms such as “includes” or “has” are intended to indicate existence of figures, numbers, steps, elements, and/or components and should be understood as not precluding possibility of existence of additional existence of figures, numbers, steps, elements, and/or components. As used herein, conditional expressions such as “if” and “when” are not limited to an optional case and are intended to be interpreted, when a specific condition is satisfied, to perform the related operation or interpret the related definition according to the specific condition.

As described above, related contents have been described in the best mode for carrying out the embodiments.

It will be apparent to those skilled in the art that various changes or modifications can be made to the embodiments within the scope of the embodiments. Thus, it is intended that the embodiments cover the modifications and variations of the present disclosure provided they come within the scope of the appended claims and their equivalents.