LG Patent | Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

As shown in the middle of the upper part of FIG. 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 of FIG. 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 of FIG. 6 shows 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 in FIG. 6 is 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 video encoder (e.g., the arithmetic encoder 40004) according to the embodiments may perform entropy encoding on the occupancy codes. In order to increase the compression efficiency, the point cloud video encoder may perform intra/inter-coding on the occupancy codes. The reception device (e.g., the reception device 10004 or the point cloud video decoder 10006) according to the embodiments reconstructs the octree based on the occupancy codes.

The point cloud video encoder (e.g., the octree analyzer 40002) 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 video 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 video 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 video 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 video encoder (or the arithmetic encoder 40004) according to the embodiments may perform entropy coding on the positions (or position values) of the points.

The point cloud video encoder (e.g., the surface approximation analyzer 40003) 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 video 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 video encoder does not operate in the trisoup mode. In other words, the point cloud video 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 video 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 video encoder according to the embodiments (e.g., the geometry reconstructor 40005) 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 according to Equation 2 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.

$\begin{array}{cc}\mathrm{Equation}2& \\ \left[\begin{array}{c}{\mu }_x\\ {\mu }_y\\ {\mu }_z\end{array}\right]=\frac{1}{n}{\sum }_{i=1}^n\left[\begin{array}{c}{x}_i\\ y_i\\ z_i\end{array}\right]& \end{array}$$\begin{array}{cc}\left[\begin{array}{c}{\stackrel{\_}{x}}_i\\ {\stackrel{\_}{y}}_i\\ {\stackrel{\_}{z}}_i\end{array}\right]=\left[\begin{array}{c}{x}_i\\ y_i\\ z_i\end{array}\right]-\left[\begin{array}{c}{\mu }_x\\ {\mu }_y\\ {\mu }_z\end{array}\right]& \end{array}$$\begin{array}{cc}\left[\begin{array}{c}{\sigma }_x^2\\ {\sigma }_y^2\\ {\sigma }_z^2\end{array}\right]={\sum }_{i=1}^n\left[\begin{array}{c}\begin{array}{c}{\stackrel{\_}{x}}_i^2\\ {\stackrel{\_}{y}}_i^2\end{array}\\ {\stackrel{\_}{z}}_i^2\end{array}\right]& \end{array}$

Then, 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 a tan 2(bi, ai), and the vertices are ordered based on the value of θ. Table 1 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. Table 1 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 1] Triangles formed from vertices ordered 1, . . . , n

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 video encoder according to the embodiments may voxelize the refined vertices. In addition, the point cloud video encoder may perform attribute encoding based on the voxelized positions (or position values).

FIG. 7 shows 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 video encoder according to the embodiments may perform entropy coding based on context adaptive arithmetic coding. The point cloud video encoder may entropy encode based on a context adaptive arithmetic coding to enhance compression efficiency of the point cloud video.

As described with reference to FIGS. 1 to 6, the point cloud content providing system or the point cloud video encoder 10002 of FIG. 1, or the point cloud video encoder or arithmetic encoder 40004 of FIG. 4 may perform entropy coding on the occupancy code immediately. In addition, the point cloud content providing system or the point cloud video 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. 7 illustrates a process of obtaining an occupancy pattern based on the occupancy of neighbor nodes. The point cloud video 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 upper part of FIG. 7 shows 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 lower part of FIG. 7 shows 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 video 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 video encoder may reduce coding complexity by changing a neighbor node pattern value (based on, for example, a table by which 64 is changed to 10 or 6).

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

As described with reference to FIGS. 1 to 7, 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 video encoder (e.g., the LOD generator 40009) may classify (or reorganize) points by LOD. FIG. 8 shows the point cloud content corresponding to LODs. The leftmost picture in FIG. 8 represents original point cloud content. The second picture from the left of FIG. 8 represents distribution of the points in the lowest LOD, and the rightmost picture in FIG. 8 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 FIG. 8, the space (or distance) between points is narrowed.

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

As described with reference to FIGS. 1 to 8, the point cloud content providing system, or the point cloud video encoder (e.g., the point cloud video encoder 10002 of FIG. 1, the point cloud video encoder of FIG. 4, or the LOD generator 40009) 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 video encoder, but also by the point cloud video decoder.

The upper part of FIG. 9 shows examples (P0 to P9) of points of the point cloud content distributed in a 3D space. In FIG. 9, the original order represents the order of points P0 to P9 before LOD generation. In FIG. 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 in FIG. 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 to FIG. 4, the point cloud video encoder according to the embodiments may perform prediction transform coding based on LOD, lifting transform coding based on LOD, and RAHT transform coding selectively or in combination.

The point cloud video encoder according to the embodiments may generate a predictor for points to perform prediction transform coding based on LOD 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 video encoder according to the embodiments (e.g., the coefficient quantizer 40011) may quantize and inversely quantize the residual of each point (which may be called residual attribute, residual attribute value, attribute prediction residual value or prediction error attribute value and so on) obtained by subtracting a predicted attribute (or attribute value) each point from the attribute (i.e., original attribute value) of each point. The quantization process performed for a residual attribute value in a transmission device is configured as shown in table 2. The inverse quantization process performed for a residual attribute value in a reception device is configured as shown in Table 3.

When the predictor of each point has neighbor points, the point cloud video encoder (e.g., the arithmetic encoder 40012) according to the embodiments may perform entropy coding on the quantized and inversely quantized residual attribute values as described above. 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 video encoder (e.g., coefficient quantizer 40011) according to the embodiments quantizes the predicted attribute values. In addition, the point cloud video encoder (e.g., the arithmetic encoder 40012) performs entropy coding on the quantized attribute values.

The point cloud video encoder (e.g., the RAHT transformer 40008) 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 video 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.

Equation 3 below represents a RAHT transformation matrix. In Equation 3, g_lx,y,z denotes the average attribute value of voxels at level l. g_lx,y,z may be calculated based on g_l+12x,y,z and g_l+12x+1,y,z. The weights for g_l2x,y,z and g_l2x+1,y,z are w1=w_l2x,y,z and w2=w_l2x+1,y,z.

$\begin{array}{cc}\lceil \begin{array}{c}{g}_{l-1_{x,y,z}}\\ h_{l-1_{x,y,z}}\end{array}\rceil =T_{w1w2}=\lceil \begin{array}{c}{g}_{l_{2x,y,z}}\\ g_{l_{2x+1,y,z}}\end{array}\rceil & \mathrm{Equation}3\end{array}$$T_{w1w2}=\frac{1}{\sqrt{w1+w2}}\left[\begin{array}{cc}\sqrt{w1}& \sqrt{w2}\\ -\sqrt{w2}& \sqrt{w1}\end{array}\right]$

Here, g_l−1x,y,z is a low-pass value and is used in the merging process at the next higher level. h_l−1x,y,z denotes high-pass coefficients. The high-pass coefficients at each step are quantized and subjected to entropy coding (e.g., encoding by the arithmetic encoder 40012). The weights are calculated as w_{l−1 x,y,z}=w_l2x,y,z+w_l2x+1,y,z. The root node is created through the g_10,0,0 and g_10,0,1 as Equation 4.

$\begin{array}{cc}\lceil \begin{array}{c}\mathrm{gDC}\\ h_{0_{0,0,0}}\end{array}\rceil =T_{w1000w1001}\lceil \begin{array}{c}{g}_{1_{0,0,0z}}\\ g_{1_{0,0,1}}\end{array}\rceil & \mathrm{Equation}4\end{array}$

The value of gDC is also quantized and subjected to entropy coding like the high-pass coefficients.

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

The point cloud video decoder illustrated in FIG. 10 is an example of the point cloud video decoder 10006 described in FIG. 1, and may perform the same or similar operations as the operations of the point cloud video decoder 10006 illustrated in FIG. 1. As shown in the figure, the point cloud video decoder may receive a geometry bitstream and an attribute bitstream contained in one or more bitstreams. The point cloud video 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 on the attribute bitstream based on the decoded geometry, and outputs decoded attributes. The decoded geometry and decoded attributes are used to reconstruct point cloud content (a decoded point cloud).

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

The point cloud video decoder illustrated in FIG. 11 is an example of the point cloud video decoder illustrated in FIG. 10, and may perform a decoding operation, which is a reverse process of the encoding operation of the point cloud video encoder illustrated in FIGS. 1 to 9.

As described with reference to FIGS. 1 and 10, the point cloud video decoder may perform geometry decoding and attribute decoding. The geometry decoding is performed before the attribute decoding.

The point cloud video 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 transformer 11007, an LOD generator (Generate LOD) 11008, an inverse lifter (inverse lifting) 11009, and/or a color inverse transformer (Inverse transform colors) 11010.

The arithmetic decoder 11000, the octree synthesizer 11001, the surface approximation synthesizer 11002, and the geometry reconstructor 11003, and the coordinate inverse transformer 11004 may perform geometry decoding. The geometry decoding according to the embodiments may include direct decoding and trisoup geometry decoding. The direct decoding 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 to FIGS. 1 to 9.

The arithmetic decoder 11000 according to the embodiments decodes the received geometry bitstream based on the arithmetic coding. The operation of the arithmetic decoder 11000 corresponds to the inverse process of the arithmetic encoder 40004.

The octree synthesizer 11001 according 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 to FIGS. 1 to 9.

When the trisoup geometry encoding is applied, the surface approximation synthesizer 11002 according to the embodiments may synthesize a surface based on the decoded geometry and/or the generated octree.

The geometry reconstructor 11003 according to the embodiments may regenerate geometry based on the surface and/or the decoded geometry. As described with reference to FIGS. 1 to 9, direct coding and trisoup geometry encoding are selectively applied. Accordingly, the geometry reconstructor 11003 directly imports and adds position information about the points to which direct coding is applied. When the trisoup geometry encoding is applied, the geometry reconstructor 11003 may reconstruct the geometry by performing the reconstruction operations of the geometry reconstructor 40005, for example, triangle reconstruction, up-sampling, and voxelization. Details are the same as those described with reference to FIG. 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 transformer 11004 according to the embodiments may acquire positions of the points by transforming the coordinates based on the reconstructed geometry.

The arithmetic decoder 11005, the inverse quantizer 11006, the RAHT transformer 11007, the LOD generator 11008, the inverse lifter 11009, and/or the color inverse transformer 11010 may perform the attribute decoding described with reference to FIG. 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 decoder 11005 according to the embodiments decodes the attribute bitstream by arithmetic coding.

The inverse quantizer 11006 according 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 video encoder.

According to embodiments, the RAHT transformer 11007, the LOD generator 11008, and/or the inverse lifter 11009 may process the reconstructed geometry and the inversely quantized attributes. As described above, the RAHT transformer 11007, the LOD generator 11008, and/or the inverse lifter 11009 may selectively perform a decoding operation corresponding to the encoding of the point cloud video encoder.

The color inverse transformer 11010 according 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 transformer 11010 may be selectively performed based on the operation of the color transformer 40006 of the point cloud video encoder.

Although not shown in the figure, the elements of the point cloud video decoder of FIG. 11 may 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 content providing apparatus, 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 video decoder of FIG. 11 described 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 video decoder of FIG. 11.

FIG. 12 illustrates a transmission device according to embodiments.

The transmission device shown in FIG. 12 is an example of the transmission device 10000 of FIG. 1 (or the point cloud video encoder of FIG. 4). The transmission device illustrated in FIG. 12 may perform one or more of the operations and methods the same as or similar to those of the point cloud video encoder described with reference to FIGS. 1 to 9. The transmission device according to the embodiments may include a data input unit 12000, a quantization processor 12001, a voxelization processor 12002, an octree occupancy code generator 12003, a surface model processor 12004, an intra/inter-coding processor 12005, an arithmetic coder 12006, a metadata processor 12007, a color transform processor 12008, an attribute transform processor 12009, a prediction/lifting/RAHT transform processor 12010, an arithmetic coder 12011 and/or a transmission processor 12012.

The data input unit 12000 according to the embodiments receives or acquires point cloud data. The data input unit 12000 may perform an operation and/or acquisition method the same as or similar to the operation and/or acquisition method of the point cloud video acquisition unit 10001 (or the acquisition process 20000 described with reference to FIG. 2).

The data input unit 12000, the quantization processor 12001, the voxelization processor 12002, the octree occupancy code generator 12003, the surface model processor 12004, the intra/inter-coding processor 12005, and the arithmetic coder 12006 perform geometry encoding. The geometry encoding according to the embodiments is the same as or similar to the geometry encoding described with reference to FIGS. 1 to 9, and thus a detailed description thereof is omitted.

The quantization processor 12001 according to the embodiments quantizes geometry (e.g., position values of points). The operation and/or quantization of the quantization processor 12001 is the same as or similar to the operation and/or quantization of the quantizer 40001 described with reference to FIG. 4. Details are the same as those described with reference to FIGS. 1 to 9.

The voxelization processor 12002 according to embodiments voxelizes the quantized position values of the points. The voxelization processor 12002 may perform an operation and/or process the same or similar to the operation and/or the voxelization process of the quantizer 40001 described with reference to FIG. 4. Details are the same as those described with reference to FIGS. 1 to 9.

The octree occupancy code generator 12003 according to the embodiments performs octree coding on the voxelized positions of the points based on an octree structure. The octree occupancy code generator 12003 may generate an occupancy code. The octree occupancy code generator 12003 may perform an operation and/or method the same as or similar to the operation and/or method of the point cloud video encoder (or the octree analyzer 40002) described with reference to FIGS. 4 and 6. Details are the same as those described with reference to FIGS. 1 to 9.

The surface model processor 12004 according to the embodiments may perform trisoup 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 processor 12004 may perform an operation and/or method the same as or similar to the operation and/or method of the point cloud video encoder (e.g., the surface approximation analyzer 40003) described with reference to FIG. 4. Details are the same as those described with reference to FIGS. 1 to 9.

The intra/inter-coding processor 12005 according to the embodiments may perform intra/inter-coding on point cloud data. The intra/inter-coding processor 12005 may perform coding the same as or similar to the intra/inter-coding described with reference to FIG. 7. Details are the same as those described with reference to FIG. 7. According to embodiments, the intra/inter-coding processor 12005 may be included in the arithmetic coder 12006.

The arithmetic coder 12006 according 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 coder 12006 performs an operation and/or method the same as or similar to the operation and/or method of the arithmetic encoder 40004.

The metadata processor 12007 according 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 processor 12007 according 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 processor 12008, the attribute transform processor 12009, the prediction/lifting/RAHT transform processor 12010, and the arithmetic coder 12011 perform the attribute encoding. The attribute encoding according to the embodiments is the same as or similar to the attribute encoding described with reference to FIGS. 1 to 9, and thus a detailed description thereof is omitted.

The color transform processor 12008 according to the embodiments performs color transform coding to transform color values included in attributes. The color transform processor 12008 may perform color transform coding based on the reconstructed geometry. The reconstructed geometry is the same as described with reference to FIGS. 1 to 9. Also, it performs an operation and/or method the same as or similar to the operation and/or method of the color transformer 40006 described with reference to FIG. 4 is performed. A detailed description thereof is omitted.

The attribute transform processor 12009 according 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 processor 12009 performs an operation and/or method the same as or similar to the operation and/or method of the attribute transformer 40007 described with reference to FIG. 4. A detailed description thereof is omitted. The prediction/lifting/RAHT transform processor 12010 according to the embodiments may code the transformed attributes by any one or more combinations of RAHT coding, prediction transform coding, and lifting transform coding. The prediction/lifting/RAHT transform processor 12010 performs at least one of the operations the same as or similar to the operations of the RAHT transformer 40008, the LOD generator 40009, and the lifting transformer 40010 described with reference to FIG. 4. In addition, the prediction transform coding, the lifting transform coding, and the RAHT transform coding are the same as those described with reference to FIGS. 1 to 9, and thus a detailed description thereof is omitted.

The arithmetic coder 12011 according to the embodiments may encode the coded attributes based on the arithmetic coding. The arithmetic coder 12011 performs an operation and/or method the same as or similar to the operation and/or method of the arithmetic encoder 40012.

The transmission processor 12012 according to the embodiments may transmit each bitstream containing encoded geometry and/or encoded attributes and metadata, or transmit one bitstream configured with the encoded geometry and/or the encoded attributes and the metadata. When the encoded geometry and/or the encoded attributes and the metadata 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 or tile inventory) 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. The TPS (or tile inventory) according to the embodiments may include information about each tile (e.g., 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 processor 12007 according to the embodiments may generate and/or process the signaling information and transmit the same to the transmission processor 12012. 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 processor 12012 according 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 transmitter 10003. Details are the same as those described with reference to FIGS. 1 and 2, and thus a description thereof is omitted.

FIG. 13 illustrates a reception device according to embodiments.

The reception device illustrated in FIG. 13 is an example of the reception device 10004 of FIG. 1 (or the point cloud video decoder of FIGS. 10 and 11). The reception device illustrated in FIG. 13 may perform one or more of the operations and methods the same as or similar to those of the point cloud video decoder described with reference to FIGS. 1 to 11.

The reception device according to the embodiment may include a receiver 13000, a reception processor 13001, an arithmetic decoder 13002, an occupancy code-based octree reconstruction processor 13003, a surface model processor (triangle reconstruction, up-sampling, voxelization) 13004, an inverse quantization processor 13005, a metadata parser 13006, an arithmetic decoder 13007, an inverse quantization processor 13008, a prediction/lifting/RAHT inverse transform processor 13009, a color inverse transform processor 13010, and/or a renderer 13011. Each element for decoding according to the embodiments may perform a reverse process of the operation of a corresponding element for encoding according to the embodiments.

The receiver 13000 according to the embodiments receives point cloud data. The receiver 13000 may perform an operation and/or reception method the same as or similar to the operation and/or reception method of the receiver 10005 of FIG. 1. A detailed description thereof is omitted.

The reception processor 13001 according to the embodiments may acquire a geometry bitstream and/or an attribute bitstream from the received data. The reception processor 13001 may be included in the receiver 13000.

The arithmetic decoder 13002, the occupancy code-based octree reconstruction processor 13003, the surface model processor 13004, and the inverse quantization processor 1305 may perform geometry decoding. The geometry decoding according to embodiments is the same as or similar to the geometry decoding described with reference to FIGS. 1 to 10, and thus a detailed description thereof is omitted.

The arithmetic decoder 13002 according to the embodiments may decode the geometry bitstream based on arithmetic coding. The arithmetic decoder 13002 performs an operation and/or coding the same as or similar to the operation and/or coding of the arithmetic decoder 11000.

The occupancy code-based octree reconstruction processor 13003 according 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 processor 13003 performs an operation and/or method the same as or similar to the operation and/or octree generation method of the octree synthesizer 11001. When the trisoup geometry encoding is applied, the surface model processor 13004 according to the embodiments may perform trisoup geometry decoding and related geometry reconstruction (e.g., triangle reconstruction, up-sampling, voxelization) based on the surface model method. The surface model processor 13004 performs an operation the same as or similar to that of the surface approximation synthesizer 11002 and/or the geometry reconstructor 11003.

The inverse quantization processor 13005 according to the embodiments may inversely quantize the decoded geometry.

The metadata parser 13006 according to the embodiments may parse metadata contained in the received point cloud data, for example, a set value. The metadata parser 13006 may pass the metadata to geometry decoding and/or attribute decoding. The metadata is the same as that described with reference to FIG. 12, and thus a detailed description thereof is omitted.

The arithmetic decoder 13007, the inverse quantization processor 13008, the prediction/lifting/RAHT inverse transform processor 13009 and the color inverse transform processor 13010 perform attribute decoding. The attribute decoding is the same as or similar to the attribute decoding described with reference to FIGS. 1 to 10, and thus a detailed description thereof is omitted.

The arithmetic decoder 13007 according to the embodiments may decode the attribute bitstream by arithmetic coding. The arithmetic decoder 13007 may decode the attribute bitstream based on the reconstructed geometry. The arithmetic decoder 13007 performs an operation and/or coding the same as or similar to the operation and/or coding of the arithmetic decoder 11005.

The inverse quantization processor 13008 according to the embodiments may inversely quantize the decoded attribute bitstream. The inverse quantization processor 13008 performs an operation and/or method the same as or similar to the operation and/or inverse quantization method of the inverse quantizer 11006.

The prediction/lifting/RAHT inverse transform processor 13009 according to the embodiments may process the reconstructed geometry and the inversely quantized attributes. The prediction/lifting/RAHT inverse transform processor 13009 performs one or more of operations and/or decoding which are the same as or similar to the operations and/or decoding of the RAHT transformer 11007, the LOD generator 11008, and/or the inverse lifter 11009. The color inverse transform processor 13010 according to the embodiments performs inverse transform coding to inversely transform color values (or textures) included in the decoded attributes. The color inverse transform processor 13010 performs an operation and/or inverse transform coding the same as or similar to the operation and/or inverse transform coding of the color inverse transformer 11010. The renderer 13011 according to the embodiments may render the point cloud data.

FIG. 14 shows an exemplary structure operatively connectable with a method/device for transmitting and receiving point cloud data according to embodiments.

The structure of FIG. 14 represents a configuration in which at least one of a server 17600, a robot 17100, a self-driving vehicle 17200, an XR device 17300, a smartphone 17400, a home appliance 17500, and/or a head-mount display (HMD) 17700 is connected to a cloud network 17000. The robot 17100, the self-driving vehicle 17200, the XR device 17300, the smartphone 17400, or the home appliance 17500 is referred to as a device. In addition, the XR device 17300 may correspond to a point cloud compressed data (PCC) device according to embodiments or may be operatively connected to the PCC device.

The cloud network 17000 may represent a network that constitutes part of the cloud computing infrastructure or is present in the cloud computing infrastructure. Here, the cloud network 17000 may be configured using a 3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 17600 may be connected to at least one of the robot 17100, the self-driving vehicle 17200, the XR device 17300, the smartphone 17400, the home appliance 17500, and/or the HMD 17700 over the cloud network 17000 and may assist in at least a part of the processing of the connected devices 17100 to 17700.

The HMD 17700 represents 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 devices 17100 to 17500 to which the above-described technology is applied will be described. The devices 17100 to 17500 illustrated in FIG. 14 may be operatively connected/coupled to a point cloud data transmission device and reception according to the above-described embodiments.

The XR/PCC device 17300 may 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 device 17300 may 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 device 17300 may acquire information about the surrounding space or a real object, and render and output an XR object. For example, the XR/PCC device 17300 may match an XR object including auxiliary information about a recognized object with the recognized object and output the matched XR object.

The self-driving vehicle 17200 may 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 vehicle 17200 to 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 vehicle 17200 which is a target of control/interaction in the XR image may be distinguished from the XR device 17300 and may be operatively connected thereto.

The self-driving vehicle 17200 having 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 vehicle 17200 may 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 vehicle 17200 may 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 compression 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.

As described above, once the transformation and quantization is performed on a residual three-dimensional block including a residual attribute value (also referred to as residual or residual attribute information) by the coefficient quantizer 40011 of FIG. 4 and/or the prediction/lifting/RAHT transform processor 12010 of FIG. 12, the quantized transformation coefficients are output to an arithmetic coder (e.g., the arithmetic coder 40012 of FIG. 4 and/or the arithmetic coder 12011 of FIG. 12) as a result.

Here, the residual attribute value is a value obtained by subtracting the predicted attribute (i.e., the predicted attribute value) of a point from the attribute (i.e., the original attribute value) of the point. The transformation may be of the following types: Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), Shape Adaptive Discrete Cosine Transform (SADCT), RAHT, etc.

The quantized transformation coefficients are entropy-coded using zero run-length coding and an arithmetic coder.

According to embodiments, when the predictive transformation technique and the lifting transformation technique are used, the residual value (i.e., the residual attribute value) between the attribute value (i.e., the original attribute value) of a point and the predicted attribute value of the point predicted by the predictor may be transmitted to the receiver by applying entropy coding, for example, zero-run-length coding and arithmetic coding thereto.

According to embodiments, the quantized conversion coefficients are composed of coefficients equal to zero or a non-zero value. In the present disclosure, coefficients equal to a non-zero value are referred to as non-zero coefficients.

According to embodiments, the zero run-length coding may count the number of zeros between non-zeros. The count value may be inserted in place of the zeros between non-zero coefficients. According to embodiments, the count value representing the number of consecutive zeros between non-zero coefficients is referred to as a zero run, and the value of the non-zero coefficient is referred to as a level.

FIG. 15 is a diagram illustrating an example of zero run-length coding according to embodiments. According to embodiments, the number of zeros between non-zero coefficients in a bitstream consisting of quantized transformation coefficients corresponding to residual attribute values may be counted via zerorun. The count value may be 0 when there are no zeros between non-zero coefficients. As shown in FIG. 15, when a non-zero coefficient equal to 1 is followed by three consecutive zeros and then the next non-zero coefficient equal to 1, 3 counted through the zerorun is placed between the non-zero coefficient equal to 1 and the next non-zero coefficient equal to 1, that is, in place of the three zeros. Since there are no zeros between the non-zero coefficient equal to 1 and the next non-zero coefficient equal to 2, a count value of 0 is placed between the non-zero coefficient equal to 1 and the non-zero coefficient equal to 2. When this process is applied in FIG. 15, the bitstream consisting of quantized transformation coefficients is composed of one or more runs (i.e., count values) and one or more levels (i.e., values of non-zero coefficients), and the size of the bitstream is reduced from ‘100012200000’ to ‘13102025’.

That is, by encoding the residual attribute values using zero-run-length coding, the size of the bitstream containing the residual attribute values (i.e., the quantized transformation coefficients) may be reduced. Furthermore, since the attribute bitstream is reduced in size with no change in the peak signal-to-noise ratio (PSNR), attribute compression efficiency may increase.

In another embodiment, zero run-length coding may also be applied to prediction mode information including a prediction mode of points. According to embodiments, quantization is not applied to the prediction mode of the points.

According to embodiments, the arithmetic coder performs entropy coding (zero-run-length coding and arithmetic coding) on a slice-by-slice basis.

An arithmetic coding technique is a lossless compression technique that does not allocate bits of the same length for one or more runs and one or more levels to be encoded, but instead allocates bits of variable length based on their probability of occurrence. For example, shorter bits may be allocated for levels with a higher probability of occurrence and longer bits to levels with a lower probability of occurrence to reduce the average amount of information that needs to be sent as an output.

However, the above-mentioned entropy coding method has a low compression efficiency because the number of zeros (i.e., count value) is transmitted to the receiving side.

The present disclosure proposes a method for changing the coding unit of entropy coding to increase the compression efficiency of zero run-length coding.

In particular, by changing the coding unit of entropy, the zero count number of zerorun repeatedly signaled by the zero run-length coding may be reduced, which may increase the compression efficiency. In other words, by reducing unnecessary zero bits in the zero run-length coding by changing the coding unit of entropy coding, the present disclosure may increase the compression efficiency.

In addition, for attributes with multiple channels, the probability of matching the attribute channel value with the previous point may be increased by separating the attribute channel (or channel) and applying zero run-length coding. Thereby, compression efficiency may be increased.

In the present disclosure, an attribute refers to a color, reflectance, normal vectors, transparency, or the like of a point.

In addition, a color may have three channels (or components or dimensions). For example, when a color is composed of RGB, attribute channel X may be R, G, or B. Alternatively, it may be referred to as the R channel, G channel, or B channel.

For example, when a color is composed of YCbCr, attribute channel X may be Y, Cb, or Cr. In this case, it is referred to as the Y channel, the Cb channel, or the Cr channel. As another example, channel X may be Y or CbCr. In this case, it is referred to as the Y channel (or luminance component channel) and the CbCr channel (or color component channel). Here, Y is a brightness component and CbCr is a chrominance component.

For example, when a color is composed of YCoCg, attribute channel X may be Y, Co, or Cg. In this case, it is referred to as the R channel, Co channel, or Cg channel. As another example, attribute channel X may be Y or CoCg. In this case, it is referred to as the Y channel (or luminance component channel) or the CoCg channel (or color component channel). Here, Y is the brightness component and CoCg is the chrominance component.

The colors described above are embodiments for the understanding of those skilled in the art, and may be equally applicable to other colors not disclosed above.

In another example, each of the color, reflectance, normals, transparency, or the like constituting an attribute may be a channel.

According to embodiments, the point cloud data transmission method/device may determine the entropy coding unit of the attribute information as a tree structure-based entropy coding unit, a Morton code-based entropy coding unit, or a level of detail (LOD)-based entropy coding unit.

According to embodiments, the point cloud data transmission method/device may perform entropy coding (e.g., zero run-length coding and arithmetic coding) of the attribute information in a determined entropy coding unit. In one embodiment, the attribute information includes residual attribute information. In another example, the attribute information may include a prediction mode.

According to embodiments, the attribute information entropy encoder of the point cloud data transmission method/device may perform entropy encoding upon receiving the transformed quantized residual attribute information as input. In this case, the attribute information entropy encoder may determine an entropy coding unit for coding the attribute information based on the reconstructed geometry information. That is, by performing entropy coding by dividing the attribute bitstream in a slice into units having similar attributes (i.e., entropy coding units) based on the geometry information, the number of zeros (zerorun) repeatedly signaled due to zero run-length coding may be reduced, thereby increasing the compression efficiency.

According to embodiments, the entropy coding unit determined based on the reconstructed geometry information may be a tree structure-based entropy coding unit, a Morton code-based entropy coding unit, or a LOD-based entropy coding unit.

The attribute information entropy encoder (also referred to as an encoder) may be the arithmetic coder 40012 of FIG. 4, the arithmetic coder 12011 of FIG. 12, or the attribute information entropy encoder 61006 of FIG. 23.

According to embodiments, the attribute information entropy decoder of the point cloud data reception method/device may determine an entropy coding unit for attribute decoding based on the reconstructed geometry information, and may entropy decode the input attribute information bitstream in the determined entropy coding unit to generate transformed quantized attribute information.

According to embodiments, the determined entropy coding unit may be a tree structure-based entropy coding unit, a Morton code-based entropy coding unit, or a LOD-based entropy coding unit.

The attribute information entropy decoder (or referred to as a decoder) may be the arithmetic decoder 11005 of FIG. 11, the arithmetic decoder 13007 of FIG. 13, or the attribute information entropy decoder 66001 of FIG. 25.

Next, a process of determining an entropy coding unit based on the reconstructed geometry information will be described.

FIG. 16 is a diagram illustrating an example of determining an entropy coding unit based on a tree structure of reconstructed geometry information according to embodiments.

As shown in FIG. 16, the encoder may determine points in a region to be one entropy coding unit based on one specific depth in the tree in which the geometry information is partitioned, and may perform entropy coding on the attribute information in the determined entropy coding units. Accordingly, the attribute bitstream in the slice may be divided into units with similar attributes (i.e., entropy coding units), thereby reducing the count number of zeros (zerorun) repeatedly signaled due to zero run-length coding, which may increase compression efficiency.

In this case, all entropy coding units may be determined at the same depth. Alternatively, partitioning information may be signaled by the encoder of the point cloud data transmission device based on depth n, and may be parsed by the decoder of the point cloud data reception device to determine the entropy coding unit.

According to embodiments, the signaling of the partitioning information by the encoder may be omitted in the case where the entropy coding unit is the same as the prediction and transformation unit. In this case, the decoder may implicitly derive the entropy coding unit (i.e., the partitioning information) from the partitioning information about the prediction and transformation unit.

According to embodiments, a tree structure for partitioning the geometry information may be a binary tree, and/or a quadtree, and/or an octree

FIG. 17 is a diagram illustrating an example of determining an entropy coding unit based on Morton code of reconstructed geometry information according to embodiments.

As shown in FIG. 17, the encoder may determine an entropy coding unit based on the 3D Morton code calculated based on the reconstructed geometry information and perform entropy coding on the attribute information in the determined entropy coding unit.

Accordingly, the attribute bitstream in the slice may be divided into units with similar attributes (i.e., entropy coding units), thereby reducing the count number of zeros (zerorun) repeatedly signaled due to zero run-length coding, which may increase compression efficiency.

Determining the Molton code-based entropy coding unit according to the embodiments may be performed by the encoder of the point cloud data transmission device and/or the decoder of the point cloud data reception device.

In this case, the entropy coding unit may be determined by dividing the Morton code into N intervals of equal size (see FIG. 17). Alternatively, the entropy coding unit may be determined implicitly by the encoder and/or decoder based on the difference between the Molton codes. Alternatively, the size of each entropy coding unit may be signaled by the encoder and parsed by the decoder to determine the entropy coding unit.

According to embodiments, the Morton code is generated by representing the coordinate values (e.g., (x, y, z)) representing the three-dimensional positions of all points as bit values, and mixing the bits. For example, when the coordinate values representing the position of a point are (5, 9, 1), the bit values of the coordinate values are (0101, 1001, 0001). When the bit values are mixed to match the bit indexes in the order z, y, and x, the result is 010001000111. This value, when expressed in decimal, is 1095, which means that the Morton code value of the point with coordinates (5, 9, 1) is 1095.

According to embodiments, the signaling of the partitioning information by the encoder may be omitted in the case where the entropy coding unit is the same as the prediction and transformation unit. In this case, the decoder may implicitly derive the entropy coding unit (i.e., the partitioning information) from the partitioning information about the prediction and transformation unit.

FIGS. 18 and 19 are diagrams illustrating examples of determining an entropy coding unit based on a LoD level generated based on reconstructed geometry information according to embodiments.

As shown in FIGS. 18 and 19, the encoder may determine an entropy coding unit based on the LoD level generated based on the reconstructed geometry information and perform entropy coding on attribute information in the determined entropy coding unit.

Accordingly, the attribute bitstream in the slice may be divided into units with similar attributes (i.e., entropy coding units), thereby reducing the count number of zeros (zerorun) repeatedly signaled due to zero run-length coding, which may increase compression efficiency.

According to embodiments, when a LOD the LOD may be defined to increase in a direction in which the detail increases, that is, in a direction in which the octree depth level increases. In the present disclosure, layer can be used interchangeably with level, depth, and depth level.

For example, in the case of a LoD-based PCC technique, a lower LoD is included in a higher LoD. That is, the higher LoD includes all points of the lower LoD. In addition, information on points included in the current LoD but not included in the previous LoD, that is, newly added points for each LoD may be defined as R (rest or retained).

For example, in FIG. 18, LoD1 contains LoD0 (i.e., R(0)) and information R1, and LoDN−1 contains LoD1 and information R(N−1), where R(N) denotes the set of points in LoD(N) excluding the points in LoD(N−1).

According to embodiments, the entropy coding unit may be determined based on the LoD level. According to embodiments, entropy encoding and decoding may be performed, taking R(N) as one entropy coding unit.

Referring to FIG. 18, R(0), (R1), and R(N) represent entropy coding units, respectively.

In another embodiment, R(N) may be divided into a plurality of equal intervals and each of the intervals may be determined as an entropy coding unit.

FIG. 19 illustrates an example in which R(N) is divided into a plurality of equal intervals and each interval is determined as an entropy coding unit according to embodiments

If n>m (n, m is an integer), the points in R(N) may be partitioned into equally spaced or unequally spaced regions, and the points in a partitioned region may be determined to be one entropy coding unit. The size of the entropy coding unit may be implicitly or explicitly determined according to n. Here, n and m represent R(n) and R(m), and R(n)=LoD(n)−LoD(n−1).

FIG. 19 shows an example in which the first to third entropy coding units n0 to n2 contain the same number of points (i.e., 64), and the last entropy coding unit n3 contains a different number of points (i.e., 28) than the first to third entropy coding units.

Once the entropy coding units are determined as in FIGS. 16 to 19, entropy coding is performed by the encoder of the transmission device in the entropy coding units, and entropy decoding is performed by the decoder of the reception device in the entropy coding units.

In this case, the entropy coding of the attribute information may be performed on a per-channel basis or on a multi-channel basis. That is, the attribute information may be separated into channels and the entropy coding may be performed on a per-channel basis.

FIGS. 20-(a) to 20-(c) are diagrams illustrating examples of entropy coding of attribute information (e.g., quantized residual attribute information) according to embodiments.

In some embodiments, when the attribute is a color, the color consists of three channels. For example, when the color attribute is in the RGB color space, the color consists of an R channel, a G channel, and a B channel. When the color attribute is in the YCbCr color space, the color consists of a Y channel, a Cb channel, and a Cr channel. When the color attribute is in the YCoCg color space, the color consists of a Y channel, a Co channel, and a Cg channel. Here, Y is referred to as a brightness or luminance component, and CbCr or CoCg is referred to as a chrominance component. For simplicity, the R channel or Y channel is referred to as a first channel, the G channel, Cb channel, or Co channel is referred to as a second channel, and the B channel, Cr channel, or Co channel is referred to as a third channel.

FIG. 20-(a) illustrates an example in which color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) is sequentially entropy coded for each point by the encoder for each channel per entropy coding unit. For example, when the color attribute is in the YCbCr color space, the attribute of the Y channel (i.e., luminance component), the attribute of the Cb channel (i.e., chrominance component), and the attribute of the Cr channel (i.e., chrominance component) are sequentially entropy coded per entropy coding unit. In this case, the decoder also entropy decodes the color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) sequentially for each channel per entropy coding unit.

FIG. 20-(b) illustrates an example in which color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) is sequentially entropy coded for each point for every one or more channels per entropy coding unit by the encoder. For example, when the color attribute is in the YCbCr color space, the entropy coding of the attributes of the Y channel (i.e., the luminance component) is performed in entropy coding units, followed by the entropy coding of the attribute of the CbCr channel (i.e., the chrominance component). In this case, the decoder also performs entropy decoding on the attribute of the Y channel (i.e., luminance component) in entropy coding units, and then performs entropy decoding on the attribute of the CbCr channel (i.e., chrominance component).

FIG. 20-(c) illustrates an example in which all channels of color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) are entropy coded by the encoder at once per entropy coding unit for each point. For example, when the color attribute is in the YCbCr color space, the entropy coding of the attributes of the YCbCr channels (i.e., the luminance component and the chrominance component) is performed in entropy encoding units. In this case, entropy decoding of the attributes of the YCbCr channels (i.e., luminance and chrominance components) is also performed by the decoder in entropy coding units. That is, the attribute information of all channels may be entropy encoded/decoded point by point.

According to embodiments, in the present disclosure, for entropy encoding of residual attribute information, one or more of FIGS. 16 to 19 may be combined to determine an entropy coding unit, and a difference signal omit flag may be signaled in the entropy coding unit. In the case where the difference signal omit flag is not signaled in the entropy coding unit, entropy coding (e.g., zero run-length coding) may be performed by separating the residual attribute information by channel as shown in FIG. 20-(a) or FIG. 20-(b).

FIGS. 21-(a) to 21-(c) are diagrams illustrating examples of entropy decoding of attribute information (e.g., received attribute bitstream) to output quantized residual attribute information according to embodiments. In this case, the entropy coding unit may be determined based on the reconstructed geometry information for entropy decoding. The determination of the entropy coding unit may be omitted.

FIG. 21-(a) illustrates an example in which color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) is sequentially entropy decoded for each point by the decoder for each channel per entropy coding unit. For example, when the color attribute is in the YCbCr color space, the attribute of the Y channel (i.e., luminance component), the attribute of the Cb channel (i.e., chrominance component), and the attribute of the Cr channel (i.e., chrominance component) are sequentially entropy decoded per entropy coding unit. In the case of FIG. 21-(a), the difference signal omit flag (e.g., ash_attr_sign_omit_flag) is parsed for each channel of attribute information per entropy coding unit to determine whether to entropy-decode the difference signal (e.g., residual attribute information).

FIG. 21-(b) illustrates an example in which color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) is sequentially entropy decoded for each point for every one or more channels per entropy coding unit by the decoder. For example, when the color attribute is in the YCbCr color space, the entropy decoding of the attributes of the Y channel (i.e., the luminance component) is performed in entropy coding units, followed by the entropy decoding of the attribute of the CbCr channel (i.e., chrominance component). In the case of FIG. 21-(b), the difference signal omit flag of the luminance component and the difference signal omit flag of the color difference component are parsed per entropy coding unit to determine whether to decode the difference signal entropy.

FIG. 21-(c) illustrates an example in which all channels of color attribute information such as (R, G, B) or (Y, Cb, Cr) or (Y, Co, Cg) are entropy decoded by the decoder at once per entropy coding unit for each point. For example, when the color attribute is in the YCbCr color space, the entropy decoding of the attributes of the YCbCr channels (i.e., the luminance component and the chrominance component) is performed in entropy encoding units. That is, the attribute information of all channels may be entropy decoded point by point. In the case of FIG. 21-(c), one difference signal omit flag is parsed per entropy coding unit to determine whether to entropy-decode the difference signal.

When entropy coding/decoding of attribute information is performed for each channel as shown in FIGS. 20-(a) and 21-(a), the signaling/parsing structure of the YCbCr format is given as follows.

(Y₀, Y₁, . . . , Y_(n-1), Cb₀, Cb₁, . . . , Cb_(n-1), Cr₀, Cr₁, . . . , Cr_(n-1))

When entropy coding/decoding of attribute information is performed for every one or more channels as shown in FIGS. 20-(b) and 21(b), the signaling/parsing structure of the YCbCr format is given as follows.

(Y₀, Y₁, . . . , Y_(n-1), Cb₀, Cr₀, Cb₁, Cr₁, . . . , Cb_(n-1), Cr_(n-1))

When entropy coding/decoding is performed on attribute information of all channels as shown in FIGS. 20-(c) and 21-(c), the signaling/parsing structure of the YCbCr format is given as follows.

(Y₀, Cb₀, Cr₀, Y₁, Cb₁, Cr₁, . . . , Y_(n-1), Cb_(n-1), Cr_(n-1))

FIG. 22 is a diagram illustrating another example of a point cloud transmission device according to embodiments.

A point cloud transmission device according to the embodiments may include a data input module 60001, a spatial partitioner 60002, a signaling processor 60003, a geometry encoder 60004, an attribute encoder 60005, and a transmission processor 60006. In some embodiments, the spatial partitioner 60002, geometry encoder 60004, and attribute encoder 60005 may be referred to as a point cloud video encoder.

The data input module 60001 may perform some or all of the operations of the point cloud video acquisition unit 10001 of FIG. 1, or may perform some or all of the operations of the data input unit 12000 of FIG. 12.

Point cloud data input to the data input module 60001 may include geometry information and/or attribute information about each point.

The geometry information may be a coordinate vector of (x, y) in a 2-dimensional Cartesian coordinate system, (γ, θ) in a cylindrical coordinate system, (x, y, z) in a 3-dimensional Cartesian coordinate system, (γ, θ, z) in a cylindrical coordinate system, or (γ, θ, ϕ) in a spherical coordinate system.

The attribute information may be texture information, color (RGB or YCbCr or YCoCg), reflectance (r), transparency, or the like for each point. A point may have one or more attributes. In other words, the attribute information may be a vector of values acquired from one or more sensors, such as a vector representing the color of a point, and/or a brightness value, and/or a reflection coefficient of LiDAR, and/or a temperature obtained from a thermal imaging camera. The spatial partitioner 60002 may spatially partition the point cloud data input through the data input module 60001 into one or more 3D blocks based on a bounding box and/or a sub-bounding box. In this case, the 3D block may represent a tile group, a tile, a slice, a coding unit (CU), a prediction unit (PU), or a transformation unit (TU). The partitioning may be performed based on at least one of an octree, a quadtree, a binary tree, a triple tree, or a k-d tree. Alternatively, the data may be partitioned into blocks of predetermined width and height. Alternatively, the partitioning may be performed by selectively determining various positions and sizes of blocks. That is, input point cloud data may be partitioned into voxel groups such as slices, tiles, bricks, or subframes. In addition, the input point cloud data may be equally or unequally partitioned by one or more axes in a Cartesian coordinate system (x, y, z), a cylindrical coordinate system (γ, θ, z), or a spherical coordinate system (γ, θ, ϕ). In addition, signaling information for the partitioning is entropy-encoded by the signaling processor 60003 and then transmitted in the form of a bitstream via the transmission processor 60006.

In one embodiment, the point cloud content may be one person such as an actor, or several people, one object or several objects. On a larger scale, it may be a map for self-driving or a map for indoor navigation of a robot. In such cases, the point cloud content may be a vast amount of locally linked data. In this case, the point cloud content cannot be encoded/decoded all at once, and therefore tile partitioning may be performed before compressing the point cloud content. For example, in a building, room #101 may be partitioned into one tile and room #102 may be partitioned into another tile. In order to support fast encoding/decoding by applying parallelization to the partitioned tiles, the tiles may be partitioned into slices again. This operation may be referred to as slice partitioning (or splitting).

That is, a tile may represent a partial region (e.g., a rectangular cuboid) of a 3D space occupied by point cloud data according to embodiments. According to embodiments, a tile may include one or more slices. The tile may be partitioned into one or more slices, and thus the point cloud video encoder may encode point cloud data in parallel.

A slice may represent a unit of data (or bitstream) that may be independently encoded by the point cloud video encoder according to the embodiments and/or a unit of data (or bitstream) that may be independently decoded by the point cloud video decoder. A slice may be a set of data in a 3D space occupied by point cloud data, or a set of some data among the point cloud data. A slice may represent a region or set of points included in a tile according to embodiments. According to embodiments, a tile may be partitioned into one or more slices based on the number of points included in the tile. For example, one tile may be a set of points partitioned by the number of points. According to embodiments, a tile may be partitioned into one or more slices based on the number of points, and some data may be split or merged in the partitioning process. That is, a slice may be a unit that may be independently coded in a corresponding tile. A tile obtained by spatial partitioning as described above may be partitioned into one or more slices for fast and efficient processing.

Positions of one or more 3D blocks (e.g., slices) spatially partitioned by the spatial partitioner 60002 are output to a geometry encoder 60004, and attribute information (or referred to as attributes) is output to the attribute encoder 60005. The positions may be position information about points included in a partitioned unit (a box, block, coding unit, prediction unit, transformation unit, tile, tile group, or slice), and is referred to as geometry information.

The geometry encoder 60004 may perform some or all of the operations of the point cloud video encoder 10002 of FIG. 1, the encoding 20001 of FIG. 2, the point cloud video encoder of FIG. 4, and the point cloud video encoder of FIG. 12.

The geometry encoder 60004 compresses the positions (i.e., geometry information) output from the spatial partitioner 60002 by intra-prediction or inter-prediction, performs entropy coding, and outputs a geometry bitstream. According to embodiments, THE encoding by the geometry encoder 60004 may be performed on the entire point cloud or in sub-point cloud units or coding units (CUs), and inter-prediction (i.e., inter-frame prediction) or intra-prediction (i.e., intra-frame prediction) may be selected for each CU. Also, an inter-prediction mode or an intra-prediction mode may be selected for each prediction unit. The geometry bitstream generated by the geometry encoder 60004 may be transmitted to the reception device via the transmission processor 60006. Also, the geometry information compressed by inter-prediction or intra-prediction is reconstructed for attribute compression. The reconstructed geometry information (or referred to as restored geometry information) is output to the attribute encoder 60005.

The attribute encoder 60005 compresses the attribute information output from the spatial partitioner 60002 using intra-prediction or inter-prediction based on the reconstructed geometry information, and performs entropy coding to output an attribute bitstream. The attribute bitstream generated by the attribute encoder 60005 may be transmitted to the reception device via the transmission processor 60006.

According to embodiments, the attribute encoder 6005 may determine an entropy coding unit based on a tree structure, a Morton code, or a LOD, and perform entropy coding (e.g., zero run-length coding and arithmetic coding) on attribute information (e.g., residual attribute information). For attributes with multiple channels, channels may be separated to perform entropy coding (e.g., zero run-length coding and arithmetic coding).

The transmission processor 60006 may perform an operation and/or transmission method identical or similar to the operation and/or transmission method of the transmission processor 12012 of FIG. 12, and perform an operation and/or transmission method identical or similar to the operation and/or transmission method of the transmitter 10003 of FIG. 1. For details, refer to the description of FIG. 1 or 12.

The transmission processor 60006 may transmit the geometry bitstream output from the geometry encoder 60004, the attribute bitstream output from the attribute encoder 60005, and the signaling bitstream output from the signaling processor 60003, respectively, or may transmit one bitstream into which the bitstreams are multiplexed.

The transmission processor 60006 may encapsulate the bitstream into a file or segment (e.g., a streaming segment) and then transmit the encapsulated bitstream over various networks such as a broadcasting network and/or a broadband network.

The signaling processor 60003 may generate and/or process signaling information and output the same to the transmission processor 60006 in the form of a bitstream. The signaling information generated and/or processed by the signaling processor 60003 may be provided to the geometry encoder 60004, the attribute encoder 60005, and/or the transmission processor 60006 for geometry encoding, attribute encoding, and transmission processing. Alternatively, the signaling processor 60003 may receive signaling information generated by the geometry encoder 60004, the attribute encoder 60005, and/or the transmission processor 60006.

In the present disclosure, the signaling information may be signaled and transmitted on a per parameter set (sequence parameter set (SPS), geometry parameter set (GPS), attribute parameter set (APS), tile parameter set (TPS), or the like) basis. Alternatively, it may be signaled and transmitted on the basis of a coding unit of each image, such as slice or tile. In the present disclosure, the signaling information may include metadata (e.g., set values) related to point cloud data, and may be provided to the geometry encoder 60004, the attribute encoder 60005, and/or the transmission processor 60006 for geometry encoding, attribute encoding, and transmission processing. Depending on the application, the signaling information may also be defined at the system side, such as a file format, dynamic adaptive streaming over HTTP (DASH), or MPEG media transport (MMT), or at the wired interface side, such as high definition multimedia interface (HDMI), Display Port, Video Electronics Standards Association (VESA), or CTA.

Although not shown in the figure, elements of the point cloud transmission device of FIG. 15 may be implemented as hardware including one or more processors or integrated circuits configured to communicate with one or more memories, software, firmware, or combinations thereof. The one or more processors may perform at least one of the operations and/or functions of the elements of the point cloud transmission device of FIG. 15 described above. Additionally, the one or more processors may operate or execute a set of software programs and/or instructions for execution of the operations and/or functions of the elements of the point cloud transmission device of FIG. 15. The one or more memories 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. 23 is a detailed block diagram of an attribute encoder according to embodiments. The attribute encoder of FIG. 23 may include one or more processors and one or more memories electrically or communicatively coupled with the one or more processors for compression of attribute information. In addition, the one or more processors may be configured as one or more physically separated hardware processors, a combination of software/hardware, or a single hardware processor. The one or more processors may be electrically and communicatively coupled with each other. Also, the one or more memories may be configured as one or more physically separated memories or a single memory. The one or more memories may store one or more programs for compression of the attribute information.

The elements of the attribute encoder illustrated in FIG. 23 may be implemented as hardware, software, processors, and/or combinations thereof.

In FIG. 23, the attribute encoder 60005 may include an attribute information transformer 61001, a geometry information mapper 61002, a subtractor 61003, a residual attribute information transformer 61004, a residual attribute information quantizer 61005, an attribute information entropy encoder 61006, a residual attribute information inverse quantizer 61007, a residual attribute information inverse transformer 61008, an adder 61009, a switching part 61010, an attribute information intra-frame (i.e., intra) predictor 61011, a filter 61012, a buffer 61013, and an attribute information inter-frame (i.e., inter) predictor 61014. The buffer 61013 may be referred to as a memory or a reconstructed point cloud buffer.

The geometry information reconstructed by the above geometry encoder 60004 is output to the geometry information mapper 61002 and the attribute information entropy encoder 61006 of the attribute encoder 60005.

When the input attribute information represents a color space, the attribute information transformer 61001 may transform the color space of the attribute information. The attribute information whose color space is transformed or not transformed by the attribute information transformer 61001 is output to the geometry information mapper 61002.

The geometry information mapper 61002 maps the attribute information received from the attribute information transformer 61001 and the reconstructed geometry information received from the geometry encoder 60004 to reconstruct attribute information. According to embodiments, in the attribute information reconstruction, an attribute value may be derived based on the attribute information about one or more points according to the reconstructed geometry information. The reconstructed attribute information is output to the adder 61003.

The subtractor 61003 outputs a difference between the attribute information partitioned into the nodes and the intra-predicted or inter-predicted attribute information (which is referred to as residual attribute information) to the residual attribute information transformer 61004.

The residual attribute information transformer 61004 may or may not transform a residual 3D block including the received residual attribute information using a transform type such as discrete cosine transform (DCT), discrete sine transform (DST), shape adaptive discrete cosine transform (SADCT), or RAHT. The transform type applied by the residual attribute information transformer 61004 to transform the residual 3D block may be entropy-encoded by the attribute information entropy encoder 61006 and then transmitted to the reception device via the transmission processor 60006.

The residual attribute information quantizer 61005 may quantize the transformed or untransformed residual attribute information with a quantization value (or referred to as a quantization parameter), and output the quantized residual attribute information to the attribute information entropy encoder 61006 and the residual attribute information inverse quantizer 61007.

The attribute information entropy encoder 61006 entropy-encodes the quantized residual attribute information (i.e., quantized transformation coefficients) using zero run-length coding and an arithmetic coder.

According to embodiments, the attribute information entropy encoder 61006 may determine an entropy coding unit based on the reconstructed geometry information as described with reference to FIGS. 15 to 21, and perform entropy coding (e.g., zero run-length coding and arithmetic coding) on the attribute information (e.g., residual attribute information) in the determined entropy coding unit. The entropy coding unit determined based on the geometry information may be a tree structure-based entropy coding unit, a Morton code-based entropy coding unit based on geometry information, or an LOD-based entropy coding unit. In this case, for an attribute having multiple channels, entropy coding (eg, zero run-length coding and arithmetic coding) may be performed by separating the channels. The above process of determining the entropy coding unit and separating the channels has been described in detail with reference to FIGS. 15 to 21, and thus a detailed description thereof is omitted.

The attribute information entropy encoder 61006 also performs entropy encoding on prediction information (also referred to as attribute prediction information or prediction mode information). The prediction information may be predicted attribute information output from the attribute information intra-predictor 61014 or the attribute information inter-predictor 61011, or may be prediction mode information corresponding to the predicted attribute information.

The attribute information entropy encoder 61006 may use various encoding methods such as, for example, exponential Golomb, context-adaptive variable length coding (CAVLC), and context-adaptive binary arithmetic coding (CABAC).

As a result of entropy encoding by the attribute information entropy encoder 61006, an attribute bitstream is generated. The attribute bitstream is transmitted to the reception device via the transmission processor 60006.

The residual attribute inverse quantizer 61007 performs inverse quantization based on whether the residual attribute information has been quantized by the residual attribute information quantizer 61005, and outputs the processed information to the residual attribute information inverse transformer 61008. For example, the residual attribute inverse quantizer 61007 may restore the residual attribute information by scaling the quantized residual attribute information by a quantization value (also referred to as a quantization parameter).

The residual attribute inverse transformer 61008 performs an inverse transform based on whether transformation has been performed by the residual attribute information inverse transformer 61004. For example, the residual attribute inverse transformer 61008 may inversely transform a residual 3D block including the restored residual attribute information using a transform type such as DCT, DST, SADCT, or RAHT. Residual attribute information that has been inversely transformed or not inversely transformed by the residual attribute inverse transformer 61008 is provided to the adder 61009.

The adder 61009 reconstructs attribute information by adding the residual attribute information and inter-predicted or intra-predicted attribute information. The reconstructed attribute information is output to the filter 61012 and the attribute information intra-predictor 61011.

The filter 61012 performs filtering on the reconstructed attribute information. The filter 61012 may include a deblocking filter, an offset corrector, and an adaptive loop filter (ALF) for filtering the reconstructed attribute information.

Attribute information calculated by the filter 61012 or attribute information prior to the filtering may be stored in the buffer 61013 so as to be used as reference information. The attribute information stored in the buffer 61013 is provided to the attribute information inter-predictor 61014 when prediction is performed.

The attribute information intra-frame (i.e., intra) predictor 61011 predicts attribute information based on the attribute information and/or geometry information about points in the same frame that has been previously reconstructed, and outputs the predicted attribute information to the subtractor 61003 and the adder 61009 via the switching part 61010. The prediction information used for the intra-frame (i.e., intra) prediction of the attribute information is entropy-encoded by the entropy encoder 61006.

The attribute information inter-frame (i.e., inter) predictor 61014 predicts current attribute information based on the attribute information and/or geometry information about points of another previously reconstructed frame stored in the buffer 61013, and outputs the predicted attribute information to the subtractor 61003 and the adder 61009 via the switching part 61010. The predicted information used for inter-frame (i.e., inter) prediction of the attribute information is entropy-encoded by the entropy encoder 61006.

The switching part 61010 may provide the attribute information intra-predicted by the attribute information intra-frame predictor 61011 or the attribute information inter-predicted by the attribute information inter-frame predictor 61014 to the subtractor 61003 and the adder 61009 according to a signal (e.g., provided by a controller (not shown)) indicating whether the prediction is inter-frame prediction or intra (i.e., intra-frame) prediction.

FIG. 24 is a diagram illustrating another example of a point cloud reception device according to embodiments. The elements of the point cloud reception device shown in FIG. 24 may be implemented as hardware, software, processors, and/or combinations thereof.

According to embodiments, the point cloud reception device may include a reception processor 65001, a signaling processor 65002, a geometry decoder 65003, an attribute decoder 65004, and a post-processor 65005. According to embodiments, the geometry decoder 65003 and the attribute decoder 65004 may be referred to as a point cloud video decoder. According to embodiments, the point cloud video decoder may be referred to as a PCC decoder, a PCC decoding unit, a point cloud decoder, a point cloud decoding unit, or the like.

According to embodiments, the point cloud video decoder may perform the reverse processes of the operations of the geometry encoder and attribute encoder of the transmission device based on signaling information for a compressed geometry bitstream and attribute bitstream to reconstruct geometry information and attribute information. The point cloud video decoder may perform some or all of the operations described in relation to the point cloud video decoder of FIG. 1, the decoding of FIG. 2, the point cloud video decoder of FIG. 11, and the point cloud video decoder of FIG. 13.

The reception processor 65001 may receive one bitstream or receive a geometry bitstream, an attribute bitstream, and a signaling bitstream, respectively. When a file and/or segment are received, the reception processor 65001 may decapsulate the received file and/or segment and output a bitstream for the same.

When one bitstream is received (or decapsulated), the reception processor 65001 may demultiplex a geometry bitstream, an attribute bitstream, and a signaling bitstream from the bitstream, and output the demultiplexed signaling bitstream to the signaling processor 65003, the demultiplexed geometry bitstream to the geometry decoder 65003, and the demultiplexed attribute bitstream to the attribute decoder 65004.

When a geometry bitstream, an attribute bitstream, and a signaling bitstream are received (or decapsulated), respectively, the reception processor 65001 may deliver the signaling bitstream to the signaling processor 65003, and the geometry bitstream and the attribute bitstream to the point cloud video decoder 65005.

The signaling processor 65002 may parse and process information included in the signaling information, for example, information included in the SPS, GPS, APS, TPS, or metadata, from the input signaling bitstream, and provide the same to the geometry decoder 65003, the attribute decoder 65004, and the post-processor 65005. In another embodiment, the signaling information included in the geometry slice header and/or the attribute slice header may also be parsed by the signaling processor 65002 before decoding of the corresponding slice data.

According to embodiments, the signaling processor 65002 may parse and process geometry-related prediction information, attribute-related prediction information, and entropy coding-related information signaled in at least one of the SPS, GPS, APS, TPS, geometry slice header, geometry slice data, attribute slice header, or attribute slice data, and provide the processed information to the geometry decoder 65003, the attribute decoder 65004, and the post-processor 65005.

The geometry-related prediction information, which is used for inter-prediction and/or intra-prediction of geometry information, and the attribute-related prediction information, which is used for inter-prediction and/or intra-prediction of the attribute information, may be collectively referred to as information related to point cloud data prediction. According to embodiments, information for determining an entropy coding unit, information for determining whether to perform entropy coding/decoding, and the like may be referred to as entropy coding-related information.

When the point cloud data is partitioned into tiles and/or slices at the transmitting side according to the embodiments, the TPS may include the number of slices included in each tile. Accordingly, the point cloud video decoder according to the embodiments may check the number of slices, and quickly parse information for parallel decoding.

Accordingly, the point cloud video decoder according to the present disclosure may receive an SPS having a reduced amount of data, and may thus quickly parse a bitstream containing point cloud data. Upon receiving tiles, the reception device may perform decoding slice by slice based on the GPS and APS included in each tile. Thereby, decoding efficiency may be maximized.

That is, the geometry decoder 65003 may reconstruct the compressed geometry information by performing a reverse process of the operations of the geometry encoder 60004 of FIG. 22 for the geometry bitstream based on the signaling information (e.g., geometry-related parameters including geometry-related prediction information, or information related to point cloud data prediction). According to embodiments, the geometry decoder 65003 may reconstruct the geometry information based on the signaling information during inter-prediction or intra-prediction. The geometry decoder 65003 may perform geometry decoding per sub-point cloud or encoding/decoding unit (CU), and may reconstruct geometry information by performing intra-frame prediction (i.e., intra-prediction) or inter-frame prediction (i.e., inter-prediction) for each encoding/decoding unit (CU) based on information (e.g., a flag) indicating whether the prediction is intra-prediction or inter-prediction.

The geometry information restored (or reconstructed) by the geometry decoder 65003 is provided to the attribute decoder 65004.

The attribute decoder 65004 may reconstruct the attribute information by performing the reverse process of the operations of the attribute encoder 60005 of FIG. 22 for the compressed attribute bitstream based on the signaling information (e.g., attribute-related parameters including attribute-related prediction information, or information related to point cloud data prediction, and/or entropy coding-related information) and the reconstructed geometry information. According to embodiments, the attribute decoder 65004 may reconstruct the attribute information based on the signaling information during inter-prediction or intra-prediction. The attribute decoder 65004 may perform attribute decoding on the entire point cloud or per sub-point cloud or encoding/decoding unit (CU), and reconstruct the attribute information by performing intra-frame prediction (i.e., intra-prediction) or inter-frame prediction (i.e., inter-prediction) for each encoding/decoding unit (CU) based on information (e.g., a flag) indicating whether the prediction is intra-prediction or inter-prediction. According to embodiments, the attribute decoder 65004 may be omitted.

According to embodiments, the attribute decoder 65004 may determine an entropy coding unit for attribute decoding based on the reconstructed geometry information and/or signaling information, and perform entropy (i.e, arithmetic decoding and zero run-length decoding) on decoding on the input attribute bitstream in the determined entropy coding unit. In this case, for attributes with multiple channels, the channels may be separated to perform entropy decoding (e.g., arithmetic decoding and zero run-length decoding).

According to embodiments, the determined entropy coding unit may be a tree structure-based entropy coding unit, a Morton code-based entropy coding unit, or a LOD-based entropy coding unit.

According to embodiments, once the point cloud data has been partitioned into tiles and/or slices on the transmitting side, the geometry decoder 65003 and attribute decoder 65004 may perform geometry decoding and attribute decoding on a per tile and/or slice basis.

The post-processor 65005 may match the geometry information (i.e., positions) reconstructed and output by the geometry decoder 65003 to the reconstructed attribute information (i.e., one or more reconstructed attributes) reconstructed and output by the attribute decoder 65004 to reconstruct and display/render the point cloud data.

According to embodiments, the reception device of FIG. 24 may further include a spatial reconstructor, which may be disposed before the geometry decoder 65003. For example, when the received point cloud data is configured in units of tiles and/or slices, a reverse process of spatial partitioning performed at the transmitting side may be performed based on signaling information. For example, when a bounding box is partitioned into tiles and slices, the bounding box may be reconstructed by combining the tiles and/or slices based on the signaling information. In another embodiment, the spatial reconstructor may spatially partition received point cloud data. For example, the received point cloud data may be partitioned according to parsed partition information such as a sub-point cloud, and/or an encoding/decoding unit (CU), a prediction unit (PU), or a transformation unit (TU) determined by the point cloud video encoder of the transmission device. The CU, the PU, and the TU may have the same partition structure or different partition structures according to embodiments.

FIG. 25 is a detailed block diagram illustrating another example of the attribute decoder 65004 according to embodiments.

According to embodiments, in the attribute decoding process in FIG. 25, some or all of the operations of the point cloud video decoder of FIG. 1, 2, 11, or 13 may be performed.

The attribute decoder of FIG. 25 may include one or more processors and one or more memories electrically and communicatively coupled with the one or more processors to decompress attribute information. Also, the one or more processors may be composed of one or more physically separated hardware processes, or may be composed of a software/hardware combination or a single hardware processor. The one or more processors according to the embodiments may be electrically and communicatively coupled with each other. Also, the one or more memories may be composed of one or more physically separate memories or a single memory. The one or more memories according to the embodiments may store one or more programs for decompression of the attribute information.

The elements of the attribute decoder illustrated in FIG. 25 may be implemented as hardware, software, processors, and/or combinations thereof. In FIG. 25, the attribute decoder 65004 may include an attribute information entropy decoder 66001, a geometry information mapper 66002, a residual attribute information inverse quantizer 66003, a residual attribute information inverse transformer 66004, an adder 66005, a filter 66006, an attribute information inverse transformer 66007, a buffer 66008, an attribute information inter-frame (i.e., inter) predictor 66009, an attribute information intra-frame (i.e., intra) predictor 66010, and a switching part 66011. The buffer 66008 may be referred to as a memory or a reconstructed point cloud buffer.

The geometry information reconstructed by the geometry encoder 65003 is provided to the attribute information entropy decoder 66001 and the geometry information mapper 66002.

The attribute information entropy decoder 66001 may perform entropy decoding (i.e., arithmetic decoding and zero run-length decoding) on the input attribute bitstream to output transformed and/or quantized residual attribute information.

According to embodiments, the attribute information entropy decoder 66001 may determine an entropy coding unit for attribute decoding based on the reconstructed geometry information and/or signaling information, and may perform entropy decoding (i.e., arithmetic decoding and zero run-length decoding) on the input attribute bitstream in the determined entropy coding unit. For attributes having multiple channels, the channels may be separated to perform entropy decoding (e.g., arithmetic decoding and zero run-length decoding).

According to embodiments, the determined entropy coding unit may be a tree structure-based entropy coding unit, a Morton code-based entropy coding unit, or an LOD-based entropy coding unit.

Also, for entropy decoding, various methods such as exponential Golomb, CAVLC, and CABAC may be applied.

According to embodiments, the attribute information entropy decoder 66001 may entropy-decode attribute-related prediction information (or information related to attribute information prediction) provided from the transmission device. Then, the transformed and/or quantized residual attribute information is output to the geometry information mapper 66002.

The geometry information mapper 66002 maps the transformed and/or quantized residual attribute information output from the attribute information entropy decoder 66001 and the reconstructed geometry information output from the geometry decoder 65003. The residual attribute information mapped to the geometry information may be output to the residual attribute information inverse quantizer 66004.

The residual attribute information inverse quantizer 66003 scales the input transformed and/or quantized residual attribute information with a quantization value (or quantization parameter). The scaled residual attribute information is output to the adder portion 66005 after being inversely transformed by the residual attribute information inverse transformer 66004. For example, the residual attribute information inverse transformer 66004 may inversely transform the residual three-dimensional block containing the input residual attribute information using a transform type such as DCT, DST, SADCT, or RAHT.

The adder 66005 reconstructs attribute information by adding the inversely quantized and/or inversely transformed residual attribute information to the predicted attribute information. The reconstructed attribute information is output to the attribute information intra-frame (i.e., intra) predictor 66010 and/or the filter 66006.

The predicted attribute information is attribute information intra-predicted by the attribute information intra-predictor 66010 or attribute information inter-predicted by the attribute information inter-predictor 66009.

The filter 66006 may filter the reconstructed attribute information using neighbor attribute information based on the reconstructed geometry information. The filter 66006 may include a deblocking filter, an offset corrector, and an ALF.

The attribute information filtered by the filter 66006 is output to the attribute information inverse transformer 66007 and the buffer 66008.

The attribute information inverse transformer 66007 may receive the type of the attribute information and transformation information in the attribute-related prediction information (or information related to point cloud data prediction) provided by the attribute information entropy decoder 66001 and perform the color space inverse transformation in the reverse process of the operation on the transmitting side.

According to embodiments, the attribute information inter-predictor 66009 and the attribute information intra-predictor 66010 may be collectively referred to as an attribute information predictor.

The attribute information inter-predictor 66009 and the attribute information inter-predictor 66010 included in the attribute information predictor may generate predicted attribute information based on the information related to generation of predicted attribute information in the attribute-related prediction information (or information related to point cloud data prediction) provided by the attribute information entropy decoder 66001, and attribute information about previously decoded points provided from the buffer 66008. That is, the attribute information inter-predictor 66009 and the attribute information inter-predictor 66010 may use attribute information or geometry information about points in the same frame or different frames stored in the buffer 66008 in predicting attribute information.

For example, the attribute information inter-predictor 66009 may use information necessary for inter-prediction of the current prediction unit in the attribute-related prediction information (or information related to point cloud data prediction) provided from the attribute information entropy decoder 66001 in inter-predicting the current prediction unit based on the information included in at least one of frames before or after the current frame including the current prediction unit.

As another example, the attribute information intra-predictor 66010 may generate predicted attribute information based on the reconstructed attribute information about a point in the current frame. According to embodiments, when a prediction unit is subjected to intra-prediction, intra-prediction is performed on the current prediction unit based on information (e.g., mode information) necessary for intra-prediction of the prediction unit in the attribute-related prediction information (or information related to the point cloud data prediction) provided from the attribute information entropy decoder 66001.

The attribute information intra-predicted by the attribute information intra-predictor 66010 or the attribute information inter-predicted by the attribute information inter-predictor 66009 is output to the adder 66005 via the switching part 66011.

The adder 66005 generates reconstructed attribute information by adding the intra-predicted or inter-predicted attribute information to the reconstructed residual attribute information output from the residual attribute inverse transformer 66004.

Although not shown in the figure, the elements of the attribute decoder of FIG. 25 may be implemented as hardware including one or more processors or integrated circuits configured to communicate with one or more memories, software, firmware, or combinations thereof. The one or more processors may perform at least one of the operations and/or functions of the elements of the attribute decoder of FIG. 25 described above. Additionally, the one or more processors may operate or execute a set of software programs and/or instructions for execution of the operations and/or functions of the elements of the attribute decoder of FIG. 25. The one or more memories 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. 26 is a flow diagram illustrating an example of an attribute information entropy decoding method according to embodiments, that is, an example in which the attribute information entropy decoder 66001 determines an entropy coding unit, and performs entropy decoding in the determined entropy coding unit.

Specifically, in operation 68001, an entropy coding unit is determined based on reconstructed geometry information. In some embodiments, operation 68001 may be omitted.

Once the entropy coding unit is determined in operation 68001, a difference signal omit flag (e.g., ash_attr_sign_omit_flag) is parsed in the determined entropy coding unit (68002). In one embodiment, the difference signal omit flag (e.g., ash_attr_sign_omit_flag) is signaled in an attribute slice header. The attribute slice header is used interchangeably with the attribute data unit header.

In operation 68001, it is checked whether the value of the parsed difference signal omit flag is 1 (68003). When the value of the difference signal omit flag is 1, the operation of entropy decoding (i.e., entropy decoding of the residual attribute information for the entropy coding unit) is omitted and the residual attribute information for the entropy coding unit is derived to be 0.

When the value of the omit difference signal flag is not 1 (i.e., 0), the attribute information entropy decoder 66001 performs entropy decoding (e.g., arithmetic decoding and zero run-length decoding) on the attribute bitstream in the entropy coding unit determined in operation 68001 (68004).

In other words, the reason for determining the entropy coding in entropy coding units used herein (see FIGS. 16 to 19) rather than in slices is to ensure that all possible residual attribute values are adjusted to 0. Therefore, after grouping together points that do not need to be transmitted, the residual attribute values are not sent (i.e., entropy coding is not performed), and only the remaining transmitted residual attribute values may be restored by entropy decoding in the entropy coding units.

FIG. 27 is a flowchart illustrating an example of an entropy decoding process according to embodiments. That is, the figure illustrates the detailed operation of operation 68004 of performing entropy decoding when the value of the difference signal omit flag is not 1 (i.e., 0) in operation 68003 of FIG. 26.

First, an isK flag (e.g., isK_flag) is parsed from the signaling information (69001). In one embodiment, the isK flag (e.g., isK_flag) is signaled in attribute slice data. The attribute slice data is used interchangeably with the attribute data unit data.

Then, in operation 69001, it is checked (69002) whether the value of the parsed isK flag (e.g., isK_flag) is 1.

When the value of the isK flag is 1, the value of the quantized residual attribute is derived to be K (69003).

When the value of the isK flag is 0, a comparison is made to determine if the values of K and N are equal (69004). That is, since there may be N isK flags according to embodiments, K is compared with N. Here, N denotes the number of points in the entropy coding unit.

When the value of K is not equal to the value of N, i.e., the value of K is less than the value of N in operation 69004, then the process proceeds to operation 69005 to increment the value of K by 1, and then proceeds to operation 69001 of parsing the isK flag. This process is repeated until the value of K equals the value of N.

When it is determined in operation 69004 that K and N are equal, i.e., all isK flags are equal to 0, A from the attribute bitstream is entropy-decoded using entropy decoding(69006). Here, A is a residual attribute value to be entropy-coded. According to embodiments, the entropy decoding includes arithmetic decoding and zero run-length decoding. Then, K is added to the entropy-decoded value of A to derive the final quantized residual attribute value (69007). That is, depending on the value of the isK flag, the residual attribute value output to the transmission processor 60006 may be K or A+K.

According to embodiments, entropy decoding may use various decoding methods, such as exponential Golomb, CAVLC, CABAC, and truncated rice coding.

According to embodiments, entropy decoding may be performed by dividing the sign and absolute value of the residual attribute information, and the sign and absolute value may be decoded using different entropy decoding methods.

The difference signal omit flag (e.g., ash_attr_sign_omit_flag) and the isK flag are referred to herein as entropy coding related information.

FIG. 28 illustrates an example of a bitstream structure of point cloud data for transmission/reception according to embodiments. According to embodiments, the bitstream output from the point cloud video encoder in one of FIGS. 1, 2, 4, 12, and 22 may be in the form shown in FIG. 28.

According to embodiments, the bitstream of point cloud data provides tiles or slices such that the point cloud data may be divided and processed region by region. Each region of the bitstream according to the embodiments may have different importance. Accordingly, when the point cloud data is divided into tiles, a different filter (encoding method) and a different filter unit may be applied to each tile. In addition, when the point cloud data is divided into slices, a different filter and a different filter unit may be applied to each slice.

When compressing point cloud data by partitioning the data into regions, the transmission device and the reception device according to the embodiments may transmit and receive the bitstream in a high-level syntax structure to selectively transmit attribute information in the partitioned regions.

The transmission device according to embodiments transmits the point cloud data according to the structure of the bitstream as illustrated in FIG. 28, such that different encoding operations may be applied according to importance and an encoding method with good quality may be used in an important region. In addition, efficient encoding and transmission according to the characteristics of the point cloud data may be supported and attribute values according to the demand of a user may be provided.

The reception device according to the embodiments receives the point cloud data according to the structure of the bitstream as illustrated in FIG. 28, such that a different filtering (decoding method) may be applied to each region (region divided into tiles or slices) according to the processing capability of the reception device, instead of using a complicated decoding (filtering) method for the entire point cloud data. Accordingly, better picture quality in a region important to the user may be provided and appropriate system latency may be ensured.

When a geometry bitstream, an attribute bitstream, and/or a signaling bitstream (or signaling information) according to the embodiments are composed of one bitstream (or G-PCC bitstream) as illustrated in FIG. 28, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments includes an SPS for sequence level signaling, a GPS for signaling of geometry information coding, one or more APSs (APS₀ and APS₁) for signaling of attribute information coding, a tile inventory (also referred to as a TPS) for tile level signaling, and one or more slices (slice 0 to slice n). That is, the bitstream of the point cloud data according to the embodiments may include one or more tiles, and each tile may be a slice group including one or more slices (slice 0 to slice n). The tile inventory (i.e., TPS) according to the embodiments may include information about each tile of one or more tiles (e.g., coordinate value information and height/size information of a tile bounding box). Each slice may include one geometry bitstream Geom0 and/or one or more attribute bitstreams Attr0 and Attrn. For example, slice 0 may include one geometry bitstream Geom0⁰ and one or more attribute bitstreams Attr0⁰ and Attr1⁰.

The geometry bitstream within each slice may include a geometry slice header (geom_slice_header) and geometry slice data (geom_slice_data). According to embodiments, the geometry bitstream within each slice may be referred to as a geometry data unit, the geometry slice header may be referred to as a geometry data unit header, and the geometry slice data may be referred to as geometry data unit data.

Each attribute bitstream in each slice may include an attribute slice header (attr_slice_header) and attribute slice data (attr_slice_data). According to embodiments, the attribute slice header in each slice may be referred to as an attribute data unit, the attribute slice header may be referred to as an attribute data unit header, and the attribute slice data may be referred to as attribute data unit data.

According to embodiments, parameters required for encoding and/or decoding of the point cloud data may be newly defined in parameter sets of the point cloud data (e.g., an SPS, a GPS, an APS, and a TPS (also referred to as a tile inventory) and/or in a header of a corresponding slice. For example, when encoding and/or decoding of geometry information is performed, the parameters may be added to the GPS and, when tile-based encoding and/or decoding is performed, the parameters may be added to a tile (TPS) and/or a slice header.

According to embodiments, information related to entropy coding (or referred to as entropy coding-related information) may be signaled in at least one of a sequence parameter set, a geometry parameter set, an attribute parameter set, a tile parameter set, or an SEI message. Further, information related to entropy coding (or referred to as entropy coding-related information) may be signaled in at least one of an attribute slice header (or referred to as an attribute data unit header) or attribute slice data (or referred to as attribute data unit data).

According to embodiments, entropy coding-related information may be defined in a corresponding position or a separate position depending on an application or system such that the range and method to be applied may be used differently. A field, which is a term used in syntaxes that will be described later in the present disclosure, may have the same meaning as a parameter or a syntax element.

That is, a signal (e.g., entropy coding-related information) may have different meanings depending on the position where the signal is transmitted. If the signal is defined in the SPS, it may be equally applied to the entire sequence. If the signal is defined in the GPS, this may indicate that the signal is used for position reconstruction. If the signal is defined in the APS, this may indicate that the signal is applied to attribute reconstruction. If the signal is defined in the TPS, this may indicate that the signaling is applied to only points within a tile. If the signal is delivered in a slice, this may indicate that the signaling is applied only to the slice. In addition, when the fields (or referred to as syntax elements) are applicable to multiple point cloud data streams as well as the current point cloud data stream, they may be carried in a higher-level parameter set or the like.

According to embodiments, parameters (which may be referred to as metadata, signaling information, or the like) may be generated by the metadata processor (or metadata generator), signaling processor, or processor of the transmission device, and transmitted to the reception device so as to be used in the decoding/reconstruction process. For example, the parameters generated and transmitted by the transmission device may be acquired by the metadata parser of the reception device.

FIG. 29 shows an embodiment of a syntax structure of a sequence parameter set (SPS) (seq_parameter_set( )) according to the present disclosure. The SPS may contain sequence information about a point cloud data bitstream.

The SPS according to the embodiments may include a main_profile_compatibility_flag field, a unique_point_positions_constraint_flag field, a level_idc field, an sps_seq_parameter_set_id field, an sps_bounding_box_present_flag field, an sps_source_scale_factor_numerator_minus1 field, an sps_source_scale_factor_denominator_minus1 field, an sps_num_attribute_sets field, log2_max_frame_idx field, an axis_coding_order field, an sps_bypass_stream_enabled_flag field, and an sps_extension_flag field.

The main_profile_compatibility_flag field may indicate whether the bitstream conforms to the main profile. For example, main_profile_compatibility_flag equal to 1 may indicate that the bitstream conforms to the main profile. For example, main_profile_compatibility_flag equal to 0 may indicate that the bitstream conforms to a profile other than the main profile.

When unique_point_positions_constraint_flag is equal to 1, in each point cloud frame that is referred to by the current SPS, all output points may have unique positions. When unique_point_positions_constraint_flag is equal to 0, in any point cloud frame that is referred to by the current SPS, two or more output points may have the same position. For example, even when all points are unique in the respective slices, slices in a frame and other points may overlap. In this case, unique_point_positions_constraint_flag is set to 0.

level_idc indicates a level to which the bitstream conforms.

sps_seq_parameter_set_id provides an identifier for the SPS for reference by other syntax elements.

The sps_bounding_box_present_flag field indicates whether a bounding box is present in the SPS. For example, sps_bounding_box_present_flag equal to 1 indicates that the bounding box is present in the SPS, and sps_bounding_box_present_flag equal to 0 indicates that the size of the bounding box is undefined.

According to embodiments, when sps_bounding_box_present_flag is equal to 1, the SPS may further include an sps_bounding_box_offset_x field, an sps_bounding_box_offset_y field, an sps_bounding_box_offset_z field, an sps_bounding_box_offset_log2_scale field, an sps_bounding_box_size_width field, an sps_bounding_box_size_height field, and an sps_bounding_box_size_depth field.

sps_bounding_box_offset_x indicates the x offset of the source bounding box in Cartesian coordinates. When the x offset of the source bounding box is not present, the value of sps_bounding_box_offset_x is 0.

sps_bounding_box_offset_y indicates the y offset of the source bounding box in Cartesian coordinates. When the y offset of the source bounding box is not present, the value of sps_bounding_box_offset_y is 0.

sps_bounding_box_offset_z indicates the z offset of the source bounding box in Cartesian coordinates. When the z offset of the source bounding box is not present, the value of sps_bounding_box_offset_z is 0.

sps_bounding_box_offset_log2_scale indicates a scale factor for scaling quantized x, y, and z source bounding box offsets.

sps_bounding_box_size_width indicates the width of the source bounding box in Cartesian coordinates. When the width of the source bounding box is not present, the value of sps_bounding_box_size_width may be 1.

sps_bounding_box_size_height indicates the height of the source bounding box in Cartesian coordinates. When the height of the source bounding box is not present, the value of sps_bounding_box_size_height may be 1.

sps_bounding_box_size_depth indicates the depth of the source bounding box in Cartesian coordinates. When the depth of the source bounding box is not present, the value of sps_bounding_box_size_depth may be 1.

sps_source_scale_factor_numerator_minus1 plus 1 indicates the scale factor numerator of the source point cloud.

sps_source_scale_factor_denominator_minus1 plus 1 indicates the scale factor denominator of the source point cloud.

sps_num_attribute_sets indicates the number of coded attributes in the bitstream.

The SPS according to the embodiments includes an iteration statement repeated as many times as the value of the sps_num_attribute_sets field. In an embodiment, i is initialized to 0, and is incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the value of i becomes equal to the value of the sps_num_attribute_sets field. The iteration statement may include an attribute_dimension_minus1[i] field and an attribute_instance_id[i] field. attribute_dimension_minus1[i] plus 1 indicates the number of components of the i-th attribute.

The attribute_instance_id[i] field specifies the instance ID of the i-th attribute.

According to embodiments, when the value of the attribute_dimension_minus1[i] field is greater than 1, the iteration statement may further include an attribute_secondary_bitdepth_minus1[i] field, an attribute_cicp_colour_primaries[i] field, an attribute_cicp_transfer_characteristics[i] field, an attribute_cicp_matrix_coeffs[i] field, and an attribute_cicp_video_full_range_flag[i] field.

attribute_secondary_bitdepth_minus1 [i] plus 1 specifies the bitdepth for the secondary component of the i-th attribute signal(s).

attribute_cicp_colour_primaries[i] indicates the chromaticity coordinates of the color attribute source primaries of the i-th attribute.

attribute_cicp_transfer_characteristics[i] either indicates the reference opto-electronic transfer characteristic function of the color attribute as a function of a source input linear optical intensity with a nominal real-valued range of 0 to 1 or indicates the inverse of the reference electro-optical transfer characteristic function as a function of an output linear optical intensity

attribute_cicp_matrix_coeffs[i] describes the matrix coefficients used in deriving luma and chroma signals from the green, blue, and red, or Y, Z, and X primaries of the i-th attribute.

attribute_cicp_video_full_range_flag[i] specifies the black level and range of the luma and chroma signals as derived from E′Y, E′PB, and E′PR or E′R, E′G, and E′B real-valued component signals of the i-th attribute.

The known_attribute_label_flag[i] field indicates whether a know_attribute_label[i] field or an attribute_label_four_bytes[i] field is signaled for the i-th attribute. For example, when known_attribute_label_flag[i] equal to 0 indicates the known_attribute_label[i] field is signaled for the i-th attribute. known_attribute_label_flag[i] equal to 1 indicates that the attribute_label_four_bytes[i] field is signaled for the i-th attribute.

known_attribute_label[i] specifies the type of the i-th attribute. For example, known_attribute_label[i] equal to 0 may specify that the i-th attribute is color. known_attribute_label[i] equal to 1 may specify that the i-th attribute is reflectance. known_attribute_label[i] equal to 2 may specify that the i-th attribute is frame index. Also, known_attribute_label[i] equal to 4 specifies that the i-th attribute is transparency. known_attribute_label[i] equal to 5 specifies that the i-th attribute is normals.

attribute_label_four_bytes[i] indicates the known attribute type with a 4-byte code.

According to embodiments, attribute_label_four_bytes[i] equal to 0 may indicate that the i-th attribute is color. attribute_label_four_bytes[i] equal to 1 may indicate that the i-th attribute is reflectance. attribute_label_four_bytes[i] equal to 2 may indicate that the i-th attribute is a frame index. attribute_label_four_bytes[i] equal to 4 may indicate that the i-th attribute is transparency. attribute_label_four_bytes[i] equal to 5 may indicate that the i-th attribute is normals.

log2_max_frame_idx indicates the number of bits used to signal a syntax variable frame_idx.

axis_coding_order specifies the correspondence between the X, Y, and Z output axis labels and the three position components in the reconstructed point cloud RecPic [pointidx] [axis] with and axis=0 . . . 2.

sps_bypass_stream_enabled_flag equal to 1 specifies that the bypass coding mode may be used in reading the bitstream. As another example, sps_bypass_stream_enabled_flag equal to 0 specifies that the bypass coding mode is not used in reading the bitstream.

sps_extension_flag indicates whether the sps_extension_data syntax structure is present in the SPS syntax structure. For example, sps_extension_present_flag equal to 1 indicates that the sps_extension_data syntax structure is present in the SPS syntax structure. sps_extension_present_flag equal to 0 indicates that this syntax structure is not present.

When the value of the sps_extension_flag field is 1, the SPS according to the embodiments may further include an sps_extension_data_flag field.

sps_extension_data_flag may have any value.

FIG. 30 shows an embodiment of a syntax structure of the GPS (geometry_parameter_set( )) according to the present disclosure. The GPS may include information on a method of encoding geometry information of point cloud data included in one or more slices.

According to embodiments, the GPS may include a gps_geom_parameter_set_id field, a gps_seq_parameter_set_id field, gps_box_present_flag field, a unique_geometry_points_flag field, a geometry_planar_mode_flag field, a geometry_angular_mode_flag field, a neighbour_context_restriction_flag field, a inferred_direct_coding_mode_enabled_flag field, a bitwise_occupancy_coding_flag field, an adjacent_child_contextualization_enabled_flag field, a log2_neighbour_avail_boundary field, a log2_intra_pred_max_node_size field, a log2_trisoup_node_size field, a geom_scaling_enabled_flag field, a gps_implicit_geom_partition_flag field, and a gps_extension_flag field.

The gps_geom_parameter_set_id field provides an identifier for the GPS for reference by other syntax elements.

The gps_seq_parameter_set_id field specifies the value of sps_seq_parameter_set_id for the active SPS.

The gps_box_present_flag field specifies whether additional bounding box information is provided in a geometry slice header that references the current GPS. For example, the gps_box_present_flag field equal to 1 may specify that additional bounding box information is provided in a geometry slice header that references the current GPS. Accordingly, when the gps_box_present_flag field is equal to 1, the GPS may further include a gps_gsh_box_log2_scale_present_flag field.

The gps_gsh_box_log2_scale_present_flag field specifies whether the gps_gsh_box_log2_scale field is signaled in each geometry slice header that references the current GPS. For example, the gps_gsh_box_log2_scale_present_flag field equal to 1 may specify that the gps_gsh_box_log2_scale field is signaled in each geometry slice header that references the current GPS. As another example, the gps_gsh_box_log2_scale_present_flag field equal to 0 may specify that the gps_gsh_box_log2_scale field is not signaled in each geometry slice header and a common scale for all slices is signaled in the gps_gsh_box_log2_scale field of the current GPS.

When the gps_gsh_box_log2_scale_present_flag field is equal to 0, the GPS may further include a gps-gsh-box-log2-scale field.

The gps_gsh_box_log2_scale field indicates the common scale factor of the bounding box origin for all slices that refer to the current GPS.

unique_geometry_points_flag indicates whether all output points have unique positions in one slice in all slices currently referring to GPS. For example, unique_geometry_points_flag equal to 1 indicates that in all slices that refer to the current GPS, all output points have unique positions within a slice. unique_geometry_points_flag field equal to 0 indicates that in all slices that refer to the current GPS, the two or more of the output points may have same positions within a slice.

The geometry_planar_mode_flag field indicates whether the planar coding mode is activated. For example, geometry_planar_mode_flag equal to 1 indicates that the planar coding mode is active. geometry_planar_mode_flag equal to 0 indicates that the planar coding mode is not active.

When the value of the geometry_planar_mode_flag field is 1, that is, TRUE, the GPS may further include a geom_planar_mode_th_idcm field, a geom_planar_mode_th[1] field, and a geom_planar_mode_th[2] field.

The geom_planar_mode_th_idcm field may specify the value of the threshold of activation for the direct coding mode.

geom_planar_mode_th[i] specifies, for i in the range of 0 . . . 2, specifies the value of the threshold of activation for planar coding mode along the i-th most probable direction for the planar coding mode to be efficient.

geometry_angular_mode_flag indicates whether the angular coding mode is active. For example, geometry_angular_mode_flag field equal to 1 may indicate that the angular coding mode is active. geometry_angular_mode_flag field equal to 0 may indicate that the angular coding mode is not active.

When the value of the geometry_angular_mode_flag field is 1, that is, TRUE, the GPS may further include an lidar_head_position[0] field, a lidar_head_position[1] field, a lidar_head_position[2] field, a number_lasers field, a planar_buffer_disabled field, an implicit_qtbt_angular_max_node_min_dim_log2_to_split_z field, and an implicit_qtbt_angular_max_diff_to_split_z field.

The lidar_head_position[0] field, lidar_head_position[1] field, and lidar_head_position[2] field may specify the (X, Y, Z) coordinates of the lidar head in the coordinate system with the internal axes.

number_lasers specifies the number of lasers used for the angular coding mode.

The GPS according to the embodiments includes an iteration statement that is repeated as many times as the value of the number_lasers field. In an embodiment, i is initialized to 0, and is incremented by 1 each time the iteration statement is executed. The iteration statement is repeated until the value of i becomes equal to the value of the number_lasers field. This iteration statement may include a laser_angle[i] field and a laser_correction[i] field.

laser_angle[i] specifies the tangent of the elevation angle of the i-th laser relative to the horizontal plane defined by the 0-th and the 1st internal axes.

laser_correction[i] specifies the correction, along the second internal axis, of the i-th laser position relative to the lidar_head_position[2].

planar_buffer_disabled equal to 1 indicates that tracking the closest nodes using a buffer is not used in process of coding the planar mode flag and the plane position in the planar mode. planar_buffer_disabled equal to 0 indicates that tracking the closest nodes using a buffer is used.

implicit_qtbt_angular_max_node_min_dim_log2_to_split_z specifies the log2 value of a node size below which horizontal split of nodes is preferred over vertical split.

implicit_qtbt_angular_max_diff_to_split_z specifies the log2 value of the maximum vertical over horizontal node size ratio allowed to a node.

neighbour_context_restriction_flag equal to 0 indicates that geometry node occupancy of the current node is coded with the contexts determined from neighbouring nodes which is located inside the parent node of the current node. neighbour_context_restriction_flag equal to 1 indicates that geometry node occupancy of the current node is coded with the contexts determined from neighbouring nodes which is located inside or outside the parent node of the current node.

The inferred_direct_coding_mode_enabled_flag field indicates whether the direct_mode_flag field is present in the geometry node syntax. For example, the inferred_direct_coding_mode_enabled_flag field equal to 1 indicates that the direct_mode_flag field may be present in the geometry node syntax. For example, the inferred_direct_coding_mode_enabled_flag field equal to 0 indicates that the direct_mode_flag field is not present in the geometry node syntax.

The bitwise_occupancy_coding_flag field indicates whether geometry node occupancy is encoded using bitwise contextualization of the syntax element occupancy map. For example, the bitwise_occupancy_coding_flag field equal to 1 indicates that geometry node occupancy is encoded using bitwise contextualisation of the syntax element ocupancy_map. For example, the bitwise_occupancy_coding_flag field equal to 0 indicates that geometry node occupancy is encoded using the dictionary encoded syntax element occupancy_byte.

The adjacent_child_contextualization_enabled_flag field indicates whether the adjacent children of neighboring octree nodes are used for bitwise occupancy contextualization. For example, the adjacent_child_contextualization_enabled_flag field equal to 1 indicates that the adjacent children of neighboring octree nodes are used for bitwise occupancy contextualization. For example, adjacent_child_contextualization_enabled_flag equal to 0 indicates that the children of neighbouring octree nodes are not used for the occupancy contextualization. The log2_neighbour_avail_boundary field specifies the value of the variable NeighbAvailBoundary that is used in the decoding process.

MaxNodeSizeX=1<MaxNodeSizeYLog2=gsh_log2_max_nodesize_y_minus_x+MaxNodeSizeXLog2.