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]& 1\end{array}$$\begin{array}{cc}\left[\begin{array}{c}{\stackrel{\_}{x}}_i\\ {\stackrel{\_}{y}}_i\\ {\stackrel{\_}{z}}_i\end{array}\right]=\left[\begin{array}{c}{x}_i\\ y_i\\ z_i\end{array}\right]-\left[\begin{array}{c}{\mu }_x\\ {\mu }_y\\ {\mu }_z\end{array}\right]& 2\end{array}$$\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}{\stackrel{\_}{x}}_i\\ {\stackrel{\_}{y}}_i\\ {\stackrel{\_}{z}}_i\end{array}\right]& 3\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 0 is estimated through a tan 2(bi, ai), and the vertices are ordered based on the value of 0. 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.

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.

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_ix,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.

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

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.

The point cloud data transmission method/device according to the embodiments is construed as a term referring to the transmission device 10000 of FIG. 1, the point cloud video encoder 10002 of FIG. 1, the transmitter 10003 of FIG. 1, the acquisition 20000/encoding 20001/transmission 20002 of FIG. 2, the point cloud video encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the transmission method of FIG. 49, and the like.

The point cloud data reception method/device according to the embodiments is construed as a term referring to the reception device 10004, the receiver 10005 of FIG. 1, the point cloud video decoder 10006 of FIG. 1, the transmission 20002/decoding 20003/rendering 20004 of FIG. 2, the decoder of FIG. 10, the point cloud video decoder of FIG. 11, the reception device of FIG. 13, the device of FIG. 14, the reception device of FIG. and the like.

The method/device for transmitting or receiving point cloud data according to the embodiments may be referred to simply as a method/device.

According to embodiments, geometry data, geometry information, and position information constituting point cloud data are to be construed as having the same meaning. Attribute data, attribute information, and attribute information constituting the point cloud data are to be construed as having the same meaning.

The method/device according to the embodiments may process point cloud data in consideration of scalable transmission.

Regarding the method/device according to the embodiments, disclosed herein is a method for efficiently supporting selective decoding of a part of data according to receiver performance or transmission speed in transmitting and receiving point cloud data. In particular, it is proposed in the present disclosure that geometry and attribute data delivered in data units be divided into semantic units such as geometry octree and Level of Detail (LoD), such that necessary information may be selected or unnecessary information may be removed in bitstream units.

According to embodiments, the present disclosure addresses techniques for constructing a data structure composed of a point cloud. Specifically, the present disclosure describes a method for packing and processing of related signaling information for effectively delivering point cloud data configured based on layers, and proposes a method of applying the same to a scalable point cloud compression (PCC) based service. In particular, the present disclosure proposes a method of constructing slice segments to be more suitable for the scalable PCC service and transmitting/receiving the same when a direct compression mode is used for position compression.

Referring to the point cloud data transmission/reception device (which may be referred to simply as an encoder/decoder) according to the embodiments shown in FIGS. 4 and 11, point cloud data is composed of a set of points. Each of the points includes geometry information (or geometry or geometry data) and attribute information (or an attribute or attribute data). The geometry information is three-dimensional position information (xyz) about each point. That is, the position of each point is represented by parameters in a coordinate system representing a three-dimensional space (e.g., parameters (x, y, z) of three axes representing the space, such as the X-axis, Y-axis, and Z-axis). The attribute information represents the color (RGB, YUV, etc.), reflectance, normal vectors, transparency, and the like of the points. In point cloud compression (PCC), octree-based compression is performed to efficiently compress non-uniform distribution in a three-dimensional space, and attribute information is compressed based on the octree-based compression. The point cloud video encoder and the point cloud video decoder shown in FIGS. 4 and 11 may process the operation(s) according to embodiments through respective components.

According to embodiments, the transmission device compresses the geometry information (e.g., position) and attribute information (e.g., color/brightness/reflectance, etc.) about the point cloud data and transmits the compressed information to the reception device. The point cloud data may be configured according to an octree structure that has layers according to the degree of detail or levels of detail (LoDs). Then, scalable point cloud data coding and representation may be performed based the configuration. In this case, only a part of the point cloud data may be decoded or represented according to the performance of the reception device or the transfer rate. However, there is currently no method for removing unnecessary data in advance.

That is, in the case where only a part of the scalable point cloud compression bitstream needs to be transmitted (e.g., only a part of the layers are decoded in scalable decoding), the necessary part cannot be selected and sent. Accordingly, the transmission device is required to re-encode the necessary part after decoding as shown in FIG. 15, or the reception device is required to selectively apply the necessary data after decoding when the entire data is delivered to the reception device.

However, in the case of FIG. 15, a delay may occur due to the time for decoding and re-encoding. In the case of FIG. 16, bandwidth efficiency may be degraded due to transmission of unnecessary data to the reception device. Further, when a fixed bandwidth is used, data quality may need to be lowered to transmit data.

Accordingly, the methods/devices according to the embodiments may provide slices such that the point cloud may be divided into regions to be processed.

In particular, in the case of octree-based position compression, entropy-based compression and direct compression may be used together. In this regard, proposed herein is a slice configuration for efficiently utilizing scalability.

Also, regarding the methods/devices according to the embodiments, a slice segmentation structure of point cloud data may be defined, and a scalable layer and slice structure for scalable transmission may be signaled.

The methods/devices according to embodiments may divide and process a bitstream in specific units for efficient bitstream delivery and decoding.

A method/device according to embodiments may enable selective transmission and decoding of layered point cloud data in a bitstream unit.

The unit according to the embodiments may be referred to as an LOD, a layer, a slice, or the like. LOD is the same term as LOD in attribute data coding, but may mean a data unit for a layered structure of a bitstream. The LOD may be a concept corresponding to one depth or a bundle of two or more depths based on the layer structure of point cloud data, for example, depths (levels) of an octree or multiple trees. Similarly, a layer is provided to generate a unit of a sub-bitstream, and is a concept that corresponds to one depth or a bundle of two or more depths, and may correspond to one LOD or two or more LODs. Also, a slice is a unit for configuring a unit of a sub-bitstream, and may correspond to one depth, a part of one depth, or two or more depths. Also, a slice may correspond one LOD, a part of one LOD, or two or more LODs. According to embodiments, the LOD, the layer, and the slice may correspond to each other or one of the LOD, the layer, and the slice may be included in another one. Also, a unit according to embodiments may include an LOD, a layer, a slice, a layer group, or a subgroup, and may be referred to interchangeably. According to embodiments, in an octree structure, a layer, a depth, a level, and a depth level may have the same meaning.

FIG. 17 is a diagram illustrating an example of a layer-based point cloud data configuration according to embodiments. FIG. 17 illustrating an example of an octree structure in which the depth level of a root node is set to 0 and the depth level of a leaf node is set to 7.

A method/device according to embodiments may configure layer-based point cloud data as shown in FIG. 17 to encode and decode the point cloud data.

Layering of point cloud data according to embodiments may have a layer structure in terms of SNR, spatial resolution, color, temporal frequency, bit depth, or the like depending on the application field, and may construct layers in a direction in which data density increases based on the octree structure or LOD structure.

That is, when the LOD is generated based on the octree structure, the LOD may be defined to increase in a direction in which the detail is increased, that is, in a direction in which the octree depth level is increased. In the present disclosure, a layer may have the same meaning as a level, a depth, and a depth level.

Referring to FIG. 17, for example, in an octree structure having 7 depth levels except for the root node level (or referred to as a root level), LOD 0 is configured to include levels from the root node level to the octree depth level 4, and LOD 1 is configured to include levels from the root node level to octree depth level 5, and LOD 2 is configured to include levels from root node level to octree depth level 7.

FIG. 18-(a) shows a bitstream structure of geometry data according to embodiments, and FIG. 18-(b) shows a bitstream structure of attribute data according to embodiments.

A method/device according to the embodiments may generate LODs based on the layering of the octree structure as shown in FIG. 17 and configure a geometry bitstream and an attribute bitstream as shown in FIGS. 18-(a) and 18-(b).

The transmission device according to the embodiments may divide a bitstream acquired through point cloud compression into a geometry bitstream and an attribute bitstream according to data types to transmit the bitstream.

In this case, each bitstream may be composed of slices and transmitted. According to embodiments, the geometry bitstream (e.g. FIG. 18-(a)) and the attribute bitstream (e.g. FIG. 18-(b)) may each be configured as one slice and delivered regardless of layer information or LoD information. In this case, in order to use only some of the layers or LoDs, an operation of decoding the bitstream, an operation of selecting only the part to be used and removing unnecessary parts, and an operation of re-encoding based on only necessary information should be performed.

The present disclosure proposes a method of dividing a bitstream into layers (or LoDs) to avoid such unnecessary intermediate operations.

FIG. 19 is a diagram showing an example of a bitstream configuration for delivering a bitstream divided into layers (or LoDs) according to embodiments.

For example, in the case of LoD-based PCC technology, a lower LoD is included in a higher LoD. That is, the higher LoD includes all points of the lower LoD. In addition, when information on points included in the current LoD but not included in the previous LoD, that is, points newly added to each LoD is defined as R (rest or retained), the transmission device may divide the initial LoD information and the information R newly included in each LoD into independent units (e.g. slices) and transmit the same, as shown in FIG. 19.

In other words, a set of points newly added to configure each LoD compared to the previous LoD may be defined as information R. FIG. 19 shows an example in which LoD1 includes LoD0 and information R1, and LoD2 includes LoD1 and information R2.

According to an embodiment, points sampled for one or more octree depth levels may be determined as data belonging to information R. That is, a set of points sampled for one or more octree depth levels (that is, points matched to an occupied node) may be defined as information R. According to another embodiment, points sampled for one octree depth level may be divided into a plurality of pieces of information R according to a predetermined criterion. In this case, various criteria to for dividing one octree depth level into a plurality of pieces of information R may be considered as follows. For example, when one octree depth level is divided into M pieces of information R, the M pieces of information R may be configured such that the data in the information R may have consecutive Morton codes, or may be grouped such that the data in the information R have the same remainder obtained by dividing the Morton code order index by M. Alternatively, M pieces of information R may be configured by grouping data in the same position when grouped as sibling nodes. According to another embodiment, if necessary, some of sampled points of a plurality of octree depth levels may be determined as information R.

In the example of FIG. 19, a geometry bitstream and an attribute bitstream are each divided into three slices according to embodiments. Each slice includes a header and a payload (also called a data unit) containing and actual data (e.g., geometry data, attribute data). The header may contain information on a corresponding slice. In addition, the header may further contain reference information related to a previous slice, a previous LoD, or a previous layer for LoD configuration.

For example, referring to FIG. 19, the geometry bitstream is divided into a slice carrying geometry data belonging to LoD0, a slice carrying geometry data belonging to information R1, and a slice carrying geometry data belonging to information R2. The attribute bitstream is divided into a slice carrying attribute data belonging to LoD0, a slice carrying attribute data belonging to information R1, and a slice carrying attribute data belonging to information R2.

The reception method/device according to the embodiments may receive a bitstream divided into LODs or layers, and may efficiently decode only data to be used without a complicated intermediate process.

In this regard, various embodiments may be applied to transmit the bitstream.

For example, the geometry bitstream and the attribute bitstream may be delivered respectively. Alternatively, the geometry bitstream and the attribute bitstream may be multiplexed into one bitstream to be delivered.

Also, when each bitstream contains LoD0 and one or more pieces of information R, the order of delivery of LoD0 and the one or more pieces of information R may be different.

In the example of FIG. 19, the geometry bitstream and the attribute bitstream are delivered, respectively. In this case, LoD0 including the geometry bitstream and two pieces of information R (R1, R2) are delivered sequentially, and LoD0 including the attribute bitstream and two pieces of information R (R1, R2) are delivered sequentially.

FIG. 20 illustrates an example of a bitstream sorting method used when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

The transmission method/device according to embodiments may serially transmit geometry data and attribute data as shown in FIG. 20 in transmitting a bitstream. In this operation, depending on the type of data, the entire geometry data (or referred to as geometry information) may be transmitted first, and then attribute data (or referred to as attribute information) may be transmitted. In this case, the geometry data may be quickly reconstructed based on the transmitted bitstream information.

Referring to FIG. 20, for example, layers (LODs) containing the geometry data may be positioned first in the bitstream, and layers (LODs) containing the attribute data may be positioned after the geometry layers. Since the attribute data is dependent on the geometry data, the layers (LODs) containing the geometry data may be positioned before the layers (LODs) containing the attribute data. FIG. 20 shows an example in which LoD0 containing geometry data and two pieces of information R (R1, R2) are sequentially delivered, and then LoD0 containing attribute data and two pieces of information R (R1, R2) are sequentially delivered. In this regard, the positions may be changed in various ways according to embodiments. In addition, reference may be made between the geometry headers, and also may be made between the attribute headers and the geometry headers.

FIG. 21 illustrates another example of the bitstream sorting method used when a geometry bitstream and an attribute bitstream are multiplexed into one bitstream according to embodiments.

In transmitting a bitstream, the transmission method/device according to embodiments may serially transmit geometry data and attribute data as shown in FIG. 21. In this case, bitstreams constituting the same layer containing geometry data and attribute data may be bundled and transmitted. In this case, when a compression technique for parallel decoding of geometry and attributes is used, the decoding execution time may be shortened. In this regard, information that needs to be processed first (low LoD and geometry should precede attributes) may be positioned first.

FIG. 21 show an example of transmitting a sequence of LoD0 containing geometry data, LoD0 containing attribute data, information R1 containing geometry data, information R1 containing attribute data, information R2 containing geometry data, and information R2 containing attribute data. In this case, the positions may be changed in various ways according to embodiments. In addition, reference may be made between the geometry headers and between the attribute headers and the geometry headers.

The transmission/reception method/device according to the embodiments may efficiently select a layer (or LoD) desired in the application field at a bitstream level when transmitting and receiving a bitstream. In grouping and transmitting geometry according to the bitstream sorting method according to the embodiments as shown in FIG. 20, there may be an empty part in the middle after selecting the bitstream level. In this case, the bitstream may need to be re-positioned.

By bundling and transmitting the geometry data and attribute data according to layers as shown in FIG. 21, necessary information may be selectively delivered and/or unnecessary information may be selectively removed according to an application field, as shown in FIG. 22-(a) to 22-(c) or 23-(a) to 23-(c).

FIGS. 22-(a) to 22-(c) are diagrams illustrating an example of symmetric geometry-attribute selection according to embodiments.

For example, referring to FIGS. 22-(a) to 22-(c), when a part of the bitstream needs to be selected according to embodiments, the transmission device selects and transmits data only up to LoD1 (i.e., LoD0+R1), and information R2 corresponding to the upper layer (i.e., the new part of LoD2) is removed from the bitstream and not transmitted. In the case of symmetric geometry-attribute selection, geometry data and attribute data of the same layer are simultaneously selected and transmitted, or simultaneously selected and removed.

FIGS. 22-(a) to 22-(c) are diagrams illustrating an example of symmetric geometry-attribute selection according to embodiments. In the case of asymmetric geometry-attribute selection, only one of the geometry data and the attribute data of the same layer is selected and transmitted or removed.

For example, referring to FIGS. 23-(a) to 23-(c), when a part of the bitstream needs to be selected according to embodiments, the transmission device selects and transmits LoD1 (LoD0+R1) containing geometry data and attribute data, LoD1 (LoD0+R1) containing attribute data, and R2 containing geometry data, and removes R2 containing attribute data from the bitstream so as not to be transmitted. That is, for the attribute data, data other than the data of the upper layer (R2) is selected and transmitted. For the geometry data, data of all layers (from level 0 (root level) to level 7 (leaf level) of the octree structure is transmitted.

When a part of a bitstream needs to be selected according to embodiments, a part of the bitstream may be selected by the symmetric geometry-attribute selection method of FIGS. 22-(a) to 22-(c), the asymmetric geometry-attribute selection method of FIGS. 23-(a) to 23-(c), or the combination of the symmetric geometry-attribute selection method and the asymmetric geometry-attribute selection method.

The segmentation of a bitstream, selection of a part of the bitstream, and the like described above are intended to support scalability of point cloud data.

Referring to FIG. 17, when point cloud data is expressed in an octree structure and classified by LOD (or layer), scalable encoding/decoding (scalability) may be supported.

The scalability function according to embodiments may include slice-level scalability and/or octree-level scalability.

LoD according to embodiments may be used as a unit for indicating a set of one or more octree layers. Also, LoD may mean a bundle of octree layers to be configured on a slice-by-slice basis.

LOD according to embodiments may be used in a broad sense, such as a unit for dividing data in detail by extending the meaning of LOD in attribute encoding/decoding.

That is, spatial scalability by an actual octree layer (or scalable attribute layer) may be provided for each octree layer. However, if scalability is configured in a slice level before bitstream parsing, it may be selected in LoD units.

For example, referring to FIG. 17, in an octree structure, the root level to level 4 correspond to LoD0, and the root level to level 5 correspond to LoD1. Also, the root level to level 8 (i.e., leaf level) correspond to LoD2.

In other words, in the example of FIG. 17, when scalability is used in units of slices, the provided scalable operation corresponds to three steps of LoD0, LoD1, and LoD2, and a scalable operation that may be provided in the decoding operation by the octree structure corresponds to 8 steps from the root level to the leaf level.

According to embodiments, when LoD0 to LoD2 are composed of respective slices, the transcoder (see FIG. 15) of the receiver or transmitter may select only LoD0, select LoD0 and LoD1, or select LoD0, LoD1, and LoD2 for scalable processing.

For example, when only LoD0 is selected, the maximum octree level is 4, and one scalable layer may be selected from among octree layers 0 to 4 in the decoding operation. In this case, the reception device may consider a node size obtainable through the maximum octree level (or depth) as a leaf node, and may transmit the node size as signaling information.

For example, when LoD0 and LoD1 are selected, layer 5 may be added. Thus, the maximum octree level may be 5, and one scalable layer may be selected from among octree layers 0 to 5 during the decoding operation. In this case, the reception device may consider a node size obtainable through the maximum octree level (or depth) as a leaf node, and may transmit the node size as signaling information. According to embodiments, an octree depth, an octree layer, and an octree level may be units into which data is divided in detail.

For example, when LoD0, LoD1, and LoD2 are selected, layers 6 and 7 may be added. Thus, the maximum octree level may be 7, and one scalable layer may be selected from among octree layers 0 to 7 during the decoding operation. In this case, the reception device may consider a node size obtainable through the maximum octree level (or depth) as a leaf node, and may transmit the node size as signaling information.

FIGS. 24-(a) to 24-(c) illustrate examples of methods of configuring slices including point cloud data according to embodiments.

The transmission method/device/encoder according to the embodiments may configure a G-PCC bitstream by segmenting the bitstream in a slice structure. A data unit for detailed data representation may be a slice.

For example, one or more octree layers may be matched to a slice.

The transmission method/device according to the embodiments, for example, the encoder, may configure a bitstream based on slices 51001 by scanning nodes (points) included in an octree in the direction of a scan order 51000. A slice may include nodes of one or more levels in the octree structure, may include only nodes of a specific level, or may include only some nodes of a specific level. Alternatively, it may include only some nodes of one or more levels.

FIG. 24-(a) shows an exemplary octree structure composed of 7 slices. In this example, a slice 51002 may include nodes of level 0 to level 4, and a slice 51003 may include some nodes of level 5. A slice 51004 may include some nodes of level 5, and a slice 51005 may include some other nodes of level 5. That is, in FIG. 24-(a), level 5 is divided into three slices. Similarly, in FIG. 24-(a), level 6 (i.e., the leaf level) is also divided into three slices. In other words, a slice may be composed of some nodes of a specific level.

FIG. 24-(b) shows an exemplary octree structure composed of four slices. In this example, a slice includes the nodes of level 0 to level 3 and some nodes of level 4, and another slice includes the remaining nodes of level 4 and some nodes of level 5. In addition, another slice includes the remaining nodes of level 5 and some nodes of level 6, and the other slice includes the remaining nodes of level 6.

FIG. 24-(c) shows an exemplary octree structure composed of five slices. A slice is composed of the nodes of level 0 to level 3, and four slices are composed of the nodes of level 4 to level 6. That is, a slice is composed of some nodes of level 4, some nodes of level 5, and some nodes of level 6.

In other words, as shown in FIGS. 24-(b) and 24-(c), when multiple octree layers are matched to a slice, only some nodes of each of the layers may be included in the slice. When multiple slices constitute a geometry/attribute frame in this way, information necessary for the reception device to configure layers may be transmitted to the reception device through signaling information. For example, the signaling information may include information about layers included in each slice and information about nodes included in each layer.

The encoder and the device corresponding to the encoder according to the embodiments may encode the point cloud data, and generate and transmit a bitstream containing the encoded data and signaling information (or parameter information) about the point cloud data.

Furthermore, in generating a bitstream, the bitstream may be generated based on the bitstream structure (e.g., see FIGS. 18 to 24, etc.) according to the embodiments. Accordingly, the reception device, the decoder, a corresponding device, or the like according to the embodiments may receive and parse the bitstream configured to be suitable for selective decoding of some data, thereby decoding and efficiently providing only a part of the point cloud data (see FIG. 16).

Next, scalable transmission of point cloud data will be described.

The point cloud data transmission method/device according to the embodiments may scalably transmit a bitstream containing point cloud data, and the point cloud data reception method/device according to the embodiments may scalably receive the bitstream and decode the same.

When the bitstream having the structure illustrated in FIGS. 15 to 24 is used for scalable transmission, signaling information for selecting a slice required by the reception device may be transmitted to the reception device. The scalable transmission may not mean transmitting or decoding the entire bitstream, but may mean transmitting or decoding only a part of the bitstream. Accordingly, low-resolution point cloud data may be provided.

When the scalable transmission is applied to an octree-based geometry bitstream according to embodiments, point cloud data may be required to be configured using information only up to a specific octree layer for the bitstream of each octree layer (FIG. 17) from a root node to a leaf node.

To this end, a target octree layer should not have a dependency on lower octree layer information. This may be a constraint applied to geometry/attribute coding in common.

In addition, in scalable transmission, a scalable structure allowing the transmission/reception device to select a scalable layer needs to be transmitted to the reception device. Considering the octree structure according to the embodiments, all octree layers may support scalable transmission, or scalable transmission may be allowed only for a specific octree layer and lower layers. For example, when some of the octree layers are included, signaling information may be delivered to the reception device to indicate a scalable layer in which a slice is included. Thus, the reception device may determine whether the slice is necessary/unnecessary in the bitstream step. In the example of FIG. 24-(a), level 0 (i.e., root level) to level 4 51002 may constitute one scalable layer without supporting scalable transmission, and the lower octree layers may be matched to scalable layers in a one-to-one correspondence manner. In general, scalability may be supported for a part corresponding to the leaf node. As shown in FIG. 24-(c), when multiple octree layers are included in a slice, it may be defined that one scalable layer shall be configured for the layers.

In this case, scalable transmission and scalable decoding may be used separately depending on the purpose. According to embodiments, the scalable transmission may be used in order for the transmitting/reception device to select information up to a specific layer without operation of the decoder. According to embodiments, scalable decoding may be used to select a specific layer during coding. That is, the scalable transmission may support selection of necessary information without involving the decoder in a compressed state (i.e., in the bitstream stage), such that the transmission or reception device may determine a specific layer. On the other hand, in the case of scalable decoding, encoding/decoding may be supported for information only up to a necessary part in the encoding/decoding process. Therefore, scalable decoding may be used in an operation such as scalable representation.

In this case, the layer configuration for scalable transmission may be different from the layer configuration for scalable decoding. For example, the lower three octree layers including the leaf node may constitute one layer in terms of scalable transmission. On the other hand, in terms of scalable decoding, when all layer information is included, scalable decoding may be enabled for each of the leaf node layers, leaf node layer-1 and leaf node layer-2.

The slice structure for layer configuration and the signaling method for scalable transmission described above will be described in more detail later.

As described above, the reception device may receive a bitstream based on scalable transmission and determine a scalable bitstream structure based on the signaling information (or referred to as parameter information) contained in the bitstream.

In addition, a geometry scalable layer may be estimated, and a geometry slice may be identified based on a geometry slice id. Also, a geometry slice may be selected and decoded based on the slice id.

When aligned_slice_structure_enabled_flag included in the bitstream is equal to 1, the attribute slice ID corresponding to the geometry slice may be checked, and an attribute slice may be accessed based on slice_id_offset. In addition, an attribute slice may be selected based on the slice_id, and the decoder may decode the selected attribute slice.

When aligned_slice_structure_enabled_flag is not equal to 1, an attribute scalable layer may be estimated. Also, an attribute slice may be identified based on the attribute slice id, and an attribute slice may be selected based on the slice id.

The transmission device and the reception device according to the embodiments have the following effects.

Point cloud compressed data may be divided according to a specific criterion and transmitted. In addition, when layered coding is used, point cloud compressed data may be divided and transmitted according to layers. Accordingly, the storage and transmission efficiency of the transmission device may increase, and the decoding efficiency of the reception device may increase.

According to the present disclosure, a bitstream may be delivered using a layer group and a subgroup, and slice segmentation may be further performed.

FIGS. 25-(a) is a diagram showing an example of a geometry tree structure contained in a single slice according to embodiments, and FIG. 25-(b) is a diagram showing an example of a geometry tree structure contained in segmented slices.

A method/device according to embodiments may configure one or more slices to deliver point cloud data, as shown in FIGS. 25-(a) and 25-(b).

That is, according to the G-PCC technology, the entire coded bitstream may be included in a single slice as shown in FIG. 25-(a). Also, in the case of multiple slices as shown in FIG. 25-(b), each slice may include sub-bitstreams. The order of slices may be the same as the order of sub-bitstreams. In other words, the bitstream may be accumulated in the width-first order of the geometry tree, and each slice may be matched to a group of tree layers as shown in FIG. 25-(b). Also, the segmented slices may inherit the layering structure of the G-PCC bitstream.

Also, the following slices may not affect previous slices, just as a higher layer does not affect lower layers in the geometry tree.

The segmented slices according to the embodiments are efficient in terms of error robustness, efficient transmission, support for a region of interest, and the like.

1) Error Resilience

Compared to the single slice structure, segmented slices may be more robust to errors. When a slice contains the entire bitstream of a frame, data loss may affect the entire frame data. On the other hand, when the bitstream is segmented into multiple slices, at least one slice that is not affected by the loss even when at least one slice is lost may be decoded.

2) Scalable Transmission

The present disclosure may consider a case where a plurality of decoders having different capabilities may be supported.

When coded point cloud data (i.e., a point cloud compression (PCC) bitstream) is included in a single slice, the LOD of the coded point cloud data may be determined prior to encoding. Accordingly, a plurality of pre-encoded bitstreams having different resolutions of point cloud data may be transmitted independently. This may be inefficient in terms of large bandwidth or storage space.

When coded point cloud data (i.e., a point cloud compression (PCC) bitstream) is included in segmented slices, a single bitstream may support decoders of different levels. From the decoder perspective, the reception device may select target layers and may deliver the partially selected bitstream to the decoder. Similarly, by using a single bitstream without partitioning the entire bitstream, a partial bitstream may be efficiently generated by the transmission device.

3) Region-Based Spatial Scalability

Regarding the G-PCC requirement according to embodiments, region-based spatial scalability may be defined as follows. That is, a compressed bitstream may be composed of one or more layers. Thus, a specific region of interest may have a higher density with additional layers, and layers may be predicted from lower layers.

To support this requirement, it is necessary to support region-wise different detailed representation. For example, in VR/AR applications, it is desirable to represent far objects with lower precision and to represent nearer objects with higher accuracy. Also, the decoder may increase the resolution of the region of interest at the request. This operation may be implemented using the scalable structure of G-PCC, such as the geometry octree and scalable attribute coding scheme.

According to embodiments, decoders should access the entire bitstream based on the current slice structure including the entire geometry or attributes. This may result in inefficiency in terms of bandwidth, memory, and decoder. On the other hand, when the bitstream is segmented into multiple slices, and each slice includes sub-bitstreams according to scalable layers, the decoder according to the embodiments may select slices as needed before efficiently parsing the bitstream.

FIG. 26-(a) is a diagram showing an example of a geometry tree structure and slice segments according to embodiments, and FIG. 26-(b) is a diagram showing an example of an attribute tree structure and slice segments according to embodiments. In particular, FIG. 26-(b) shows an example of an attribute tree structure aligned with the geometry tree structure of FIG. 26-(a).

A method/device according to embodiments may generate a slice layer group using a layer structure of point cloud data as shown in FIGS. 26-(a) and 26-(b).

The method/device according to the embodiments may apply segmentation of a geometry bitstream and an attribute bitstream included in different slices. In addition, in terms of tree depth, a coding tree structure of geometry and attribute coding and each slice included in the partial tree information may be used.

For an example, referring to FIG. 26-(a), there are 8 layers in an octree, and 5 slices are used to contain sub-bitstreams of one or more layers. According to embodiments, a group represents a group of geometry tree layers. For example, group 1 includes layers 0 to 4, group 2 includes layer 5, and group 3 includes layers 6 and 7.

In addition, a specific group may be divided into one or more subgroups. For example, referring to FIG. 26-(a), group 3 is divided into three subgroups (group 3-1, group 3-2, and group 3-3). In this case, parent and child pairs are present in each subgroup. That is, group 3-1 to group 3-3 are subgroups of group 3. In the layer group structure of the geometry coding tree as shown in FIG. 26-(a), slice 1 is used to contain a geometry sub-bitstream belonging to group 1, and slice 2 is used to contain a geometry sub-bitstream belonging to group 2. Also, slice 3 is used to contain a geometry sub-bitstream belonging to subgroup 3-1, slice 4 is used to contain a geometry sub-bitstream belonging to subgroup 3-2, and slice 5 is used to contain a geometry sub-bitstream belonging to subgroup 3-3.

In one environment, when scalable attribute coding is used and the attribute tree structure is the same as the geometry tree structure, the same octree-slice mapping may be used to generate attribute slice segments as shown in FIG. 26-(b). That is, in the aligned layer group structure of the attribute coding tree as shown in FIG. 26-(b), slice 6 is used to contain an attribute sub-bitstream belonging to group 1, and slice 7 is used to contain an attribute sub-bitstream belonging to group 2. Slice 8 is used to contain an attribute sub-bitstream belonging to subgroup 3-1, slice 9 is used to contain an attribute sub-bitstream belonging to subgroup 3-2, and slice 10 is used to contain an attribute sub-bitstream belonging to subgroup 3-3.

In the present disclosure, a layer group is referred to as a group and represents a group of layer structure units occurring in G-PCC coding, such as an octree layer and a LoD layer.

A subgroup according to the embodiments may be represented as a set of neighbor nodes based on position information for one layer group. Alternatively, a bundle may be configured based on the lowest layer (which may be a layer closest to the root, and may be layer 6 in the case of group 3 in FIG. 26). A subgroup may be configured as a bundle of nodes neighboring each other by Morton code order, as a bundle of distance-based neighbor nodes, or as a bundle of nodes neighboring each other according to coding order. Additionally, nodes in a parent-child relationship may be specified to be present in the same subgroup.

When a subgroup is defined, a boundary occurs in the middle of a layer, and signaling information (e.g., sps_entropy_continuation_enabled_flag, gsh_entropy_continuation_flag, etc.) is used to indicate whether continuity is maintained at the boundary. That is, the signaling information may be used to indicate whether entropy is used continuously, and ref_slice_id may be provided. Thereby, continuity with the previous slice may be continuously maintained.

FIG. 27-(a) is a diagram showing another example of a geometry tree structure and slice segments according to embodiments, and FIG. 27-(b) is a diagram showing another example of an attribute tree structure and slice segments according to embodiments. In particular, the layer group structure of the attribute coding tree (or referred to as an attribute tree) of FIG. 27-(b) is independent of the geometry layer group structure of the geometry coding tree (or referred to as a geometry tree) of FIG. 27-(a). That is, the attribute coding layers of the attribute coding tree is different from the geometry coding layers of the geometry coding tree. In other words, FIGS. 27-(a) and 27-(b) show a layer group structure of a geometry coding tree (or referred to as a geometry tree) and an independent layer group structure of an attribute coding tree (or referred to as an attribute tree) according to embodiments.

A method/device according to embodiments may generate a geometry coding tree based on a geometry bitstream and an attribute coding tree based on an attribute bitstream as shown in FIGS. 27-(a) and 27-(b). In this case, the attribute coding layers of the attribute coding tree may have a structure different from that of the geometry coding layers of the geometry coding tree. Referring to FIG. 27-(b), the groups in the attribute tree structure may be defined independently of the groups in the geometry tree structure.

For example, referring to FIG. 27-(a), for scalable geometry coding, there are 8 layers in an octree, and 5 slices are used to contain a geometry sub-bitstream of one or more layers. According to the embodiments, a group represents a group of geometry tree layers. For example, group 1 includes layers 0 to 4, group 2 includes layer 5, and group 3 includes layers 6 and 7. Also, a specific group may be divided into one or more subgroups. For example, referring to FIG. 27-(a), group 3 is divided into three subgroups (seg1, seg2, and seg3). In this case, parent and child pairs are present in each subgroup. In the layer group structure of the geometry coding tree as shown in FIG. 27-(a), slice 1 is used to contain a geometry sub-bitstream belonging to group 1, and slice 2 is used to contain a geometry sub-bitstream belonging to group 2. Also, slice 3 is used to contain a geometry sub-bitstream belonging to subgroup 1 (seg1), slice 4 is used to contain a geometry sub-bitstream belonging to subgroup 2 (seg2), and slice 5 is used to contain a geometry sub-bitstream belonging to subgroup 3 (seg3).

For example, referring to FIG. 27-(b), for scalable attribute coding, there are 6 layers in an octree, and 3 slices are used to contain attribute sub-bitstreams of one or more layers. According to the embodiments, a group represents a group of attribute tree layers. For example, group 1 includes layers 0 to 2, group 2 includes layers 3 and 4, and group 3 includes layer 6. In the layer group structure of the attribute coding tree as shown in FIG. 27-(b), slice 6 is used to contain an attribute sub-bitstream belonging to group 1, slice 7 is used to contain an attribute sub-bitstream belonging to group 2, and slice 8 is used to contain an attribute sub-bitstream belonging to group 3.

The present disclosure proposes segmentation of slices paired with geometry and attribute layer structures for efficient use of the G-PCC layering structure.

According to embodiments, for geometry slice segments, each slice segment may contain coded data from one layer group. Here, in one embodiment, the layer group may be defined as a group of consecutive tree layers, the start and end depths of the tree layers may be specific numbers in the tree depth, and the start depth may be less than the end depth.

According to embodiments, for attribute slice segments, each slice segment may contain coded data from one layer group. Here, the layers (or referred to as tree layers) may be tree depths (or referred to as levels) or LODs according to an attribute coding scheme.

In one embodiment, the order of coded data in slice segments may be the same as the order of coded data in a single slice.

According to embodiments, signaling information related to a layer group structure may be included in at least one of parameter sets. The parameter sets according to the embodiments may include a sequence parameter set, a geometry parameter set, an attribute parameter set, or a tile parameter set.

In one embodiment, a description of the layer group structure corresponding to the geometry tree layers is required. To this end, the geometry parameter set may include information on the number of groups (num_layer_groups_minus1), group identifier information (layer_group_id), information on the number of tree depth(s) in the group (num_tree_depth), and information on the number of subgroup(s) in the group (num_subgroups). According to embodiments, the signaling information related to the layer group structure may include at least one of num_layer_groups_minus1, layer_group_id, num_tree_depth, or num_subgroups.

num_layer_groups_minus1 plus 1 specifies the number of layer groups where the layer group represents a group of consecutive tree layers that are part of the geometry coding tree structure.

layer_group_id specifies the layer group identifier of the i-th layer group.

num_tree_depth specifies the number of tree depth(s) contained in the i-th layer group.

num_subgroups specifies the number of subgroups included in the i-th layer group.

In one embodiment, a description of the layer group structure corresponding to the attribute tree layers is required. To this end, the attribute parameter set may include information on the number of groups (num_layer_groups_minus1), group identifier information (layer_group_id), information on the number of tree depth(s) in a group (num_tree_depth_minus1), and information on the number of subgroup(s) in a group (num_subgroups_minus1). That is, num_layer_groups_minus1, layer_group_id, num_tree_depth_minus1, and num_subgroups_minus1 are defined in the attribute parameter set to describe the layer group structure. In addition, indication information indicating whether the attribute slice structure is aligned with the geometry slice structure (aligned_layer_group_structure_flag) is required, and the attribute parameter set may further include this indication information (aligned_layer_group_structure_flag). According to embodiments, the signaling information related to the layer group structure may include at least one of num_layer_groups_minus1, layer_group_id, num_tree_depth_minus1, num_subgroups_minus1, or aligned_layer_group_structure_flag.

aligned_layer_group_structure_flag equal to 1 specifies that the layer group and subgroup structure of the attribute slices is identical to the geometry layer group and subgroup structure. aligned_layer_group_structure_flag equal to 0 specifies that the layer group and subgroup structure of the attribute slices is not identical to the geometry layer group and subgroup structure.

According to embodiments, when aligned_layer_group_structure_flag is equal to 1, the attribute parameter set includes geometry parameter set identification information (geom_parameter_set_id). When aligned_layer_group_structure_flag is equal to 0, the attribute parameter set includes information on the number of groups (num_layer_groups_minus1), group identifier information (layer_group_id), information on the number of tree depth(s) in a group (num_tree_depth_minus1), and information on the number of subgroup(s) in a group (num_subgroups_minus1).

geom_parameter_set_id specifies the geometry parameter set identifier that contains information on the layer group and subgroup structure that is aligned with the attribute layer group structure.

num_layer_groups_minus1 plus 1 specifies the number of layer groups where the layer group represents a group of consecutive tree layers that are part of the attribute coding tree structure.

layer_group_id specifies the layer group identifier of the i-th layer group.

num_tree_depth_minus1 plus 1 specifies the number of tree depth(s) contained in the i-th layer group.

num_subgroups_minus1 plus 1 specifies the number of subgroups included in the i-th layer group.

According to embodiments, signaling information related to the layer group structure may be included in at least one of a geometry slice header and an attribute slice header. According to embodiments, the geometry slice header has the same meaning as a geometry data unit header, and the attribute slice header has the same meaning as an attribute data unit header.

In one embodiment, the geometry slice header includes group identification information (layer_group_id) and/or subgroup identification information (subgroup_id) for identification of groups and subgroups of each slice.

layer_group_id specifies a layer group identifier for identifying a group of a corresponding slice.

subgroup_id specifies a subgroup identifier for identifying a subgroup in a layer group identified by layer_group_id.

In one embodiment, when the attribute layer structure is not aligned with the geometry slice structure (or geometry group), layer_group_id and/or subgroup_id are included to identify groups and subgroups of each slice.

layer_group_id specifies a layer group identifier for identifying a group of a corresponding slice.

subgroup_id specifies a subgroup identifier for identifying a subgroup in a layer group identified by layer_group_id.

In the present disclosure, layer_group_id and subgroup_id may be used to indicate the order of slices and may be used to sort the slices in bitstream order.

In the present disclosure, for octree-based position compression, compression efficiency may be increased based on the correlation between nodes when the point density is high.

However, when the point density is low, the compression efficiency is lowered compared to the complicated operation because the correlation between nodes is lowered as the depth increases. To address this issue, direct compression of directly compressing the position of a point when the correlation between nodes is low may be used. FIG. 28 illustrates an example of combining tree coding and direct coding modes according to embodiments. That is, when octree-based compression is performed, the positions of points present at similar positions may be represented in a bundle, and therefore the number of required bits may be reduced. However, as shown in FIG. 28, when there is no sibling node among the descendent nodes of an occupied node (53000), the octree-based compression may not have a significant effect. Accordingly, in this case, by performing direct coding on the node (i.e., point) 53000 in a direct mode, coding efficiency and compression speed may be improved.

That is, referring to the octree structure, a maximum of 8 descendent nodes may be provided based on the current point (node). The 8 descendent nodes may include an occupied node and/or unoccupied nodes.

If there are no descendent nodes and/or sibling nodes based on the current point, it is highly likely that similar neighbor points are not present. Accordingly, when a residual is generated by generating a predicted value between nodes (points), the residual may become large, the accuracy may be lowered, or latency may occur. In this case, the position value of the point may be transmitted by performing direct coding on the point (e.g., 53000 in FIG. 28).

As a method to determine whether the direct coding is performed according to embodiments, the method/device according to the embodiments may determine use of the direct coding based on a correlation with neighbor nodes as follows. That is, the direct compression method may operate when the following specific conditions are satisfied.

2) Condition for 6N eligibility: From the perspective of the parent node, only the current node is an occupied child, and 6 neighbors (nodes contacting face to face) are unoccupied.

For example, in this case, it may be determined that direct coding is available. In this case, only when the number of included points is less than or equal to a threshold, the inferred direct coding mode (IDCM) may be applied. When the IDCM is executed, information indicating that the IDCM is executed, the number of points, and information indicating the XYZ values of the point positions (that is, the portion corresponding to the remaining depths that are not octree-coded) may be included in at least one of the parameter sets and delivered to the reception device.

FIG. 29 is a flowchart illustrating the overview of the IDCM according to embodiments.

The direct compression operation according to the embodiments may be performed through the transmission device 10000 of FIG. 1, the point cloud video encoder 10002 of FIG. 1, the encoding of FIG. 2, the point cloud video encoder of FIG. 4, the transmission device of FIG. 12, the device of FIG. 14, the point cloud data transmission device of FIG. 49, or one or more processors or integrated circuits configured to communicate with one or more memories corresponding thereto. The one or more memories may store programs for processing/controlling the operations according to the embodiments. Each component of the point cloud transmission device/method according to the embodiments may be implemented with hardware, software, processor, and/or a combination thereof. The one or more processors may control various operations described herein. The processor may be referred to as a controller or the like. The operations according to the embodiments may be performed by firmware, software, and/or a combination thereof. The firmware, software, and/or a combination thereof may be stored in a processor or a memory.

The direct decompression operation according to the embodiments may be performed through the reception device 10004 of FIG. 1, the point cloud video decoder 10006 of FIG. 1, the decoding of FIG. 2, the decoder of FIG. 10, the decoder of FIG. 11, the reception device of FIG. 13, the device of FIG. 14, the point cloud data reception device of FIG. 50, or one or more processors or integrated circuits configured to communicate with one or more memories corresponding thereto. The one or more memories may store programs for processing/controlling the operations according to the embodiments. Each component of the point cloud reception device/method according to the embodiments may be implemented with hardware, software, a processor, and/or a combination thereof. The one or more processors may control various operations described herein. The processor may be referred to as a controller or the like. The operations according to the embodiments may be performed by firmware, software, and/or a combination thereof. The firmware, software, and/or a combination thereof may be stored in a processor or a memory.

Referring to FIG. 29 as an example, a parent node 55000 may have up to 8 children according to the distribution of points, and a specific child 55001 may further have a child 55002. In this case, the child 55001 is the parent node of the child 55002. In addition, it may be determined whether to further perform octree splitting and predictive coding or direct mode coding at the child 55002.

When it is determined that direct mode coding is available (55003), it is checked whether the number of neighbor nodes (neighbor points) for the point 55002 is less than or equal to a threshold(th). When the number of neighbor nodes (neighbor points) for the point 55002 is less than or equal to the threshold(th), the direct mode may be enabled. In addition, x, y, and z coordinate information about the position of each of the one or more points may be direct-coded. According to embodiments, the position values may be represented based on a sub-cube for the octree.

In another embodiment, when the number of neighbor nodes (neighbor points) for the point 55002 exceeds the threshold th, the compression efficiency of prediction coding may be higher than that of direct coding, and thus the direct mode may be disabled and the node may be further split to generate an octree. That is, when it is determined that the direct mode coding is not available (55004), the octree may be further split into octree-based sub-cubes based on the point.

As described above, when direct compression is performed, the x, y, and z position information about the current depth and lower depths is directly transmitted to the reception device. In this case, since independent position information is compressed, the compression efficiency may not be high even when arithmetic entropy compression is used.

Therefore, in this case, the transmission device/method may perform direct compression (or referred to as coding) on the x, y, and z values. In this case, the direct coded bitstream (FIG. 30-(b)) may be transmitted independently of an arithmetic entropy coded bitstream (FIG. 30-(a)). FIG. 30-(a) is a diagram showing an example of an arithmetic entropy coded (AEC) bitstream, and FIG. 30-(b) is a diagram showing an example of a direct coded (DC) bitstream.

When the geometry tree structure is divided into layer group(s) and/or subgroup(s) for scalable transmission, slices may be configured by dividing the AEC bitstream according to each layer group and/or subgroup. In one embodiment, when multiple slices are configured, each slice includes a part of the AEC bitstream, that is, an AEC sub-bitstream. In addition, a separate slice may be configured for the DC bitstream. In this case, the type of a bitstream included in each slice may be divided into an AEC bitstream and a DC bitstream. Accordingly, when geometry partial decoding is performed, the reception device may select a slice including a required AEC bitstream, and select information on a DC point of a required depth in a slice including a DC bitstream.

In the present disclosure, for simplicity, a slice including an AEC bitstream is referred to as an AEC slice, and a slice including a DC bitstream is referred to as a DC slice.

FIG. 31-(a) is a diagram showing another example of a geometry tree structure and slice segments according to embodiments, and FIG. 31-(b) is a diagram showing an example of a plurality of AEC slices and one DC slice according to embodiments. That is, the DC bitstream is carried in one slice.

FIG. 31(a) illustrates a case where position compression is performed based on 7 octree depths. In this case, the geometry tree structure (i.e., the octree structure) includes 3 groups and is divided into 5 layer groups (e.g., one of the 3 groups is divided into 3 subgroups), and the AEC bitstreams of the 5 layer groups are included in 5 slices (e.g., slice 1 to slice 5), respectively. In this case, when direct coding is used, the DC bitstream may be transmitted in a separate slice (e.g., slice 6). According to embodiments, DC bitstreams included in slice 6 may be sequentially transmitted according to the octree depth. Referring to FIG. 31-(b), for example, a DC bitstream of depth 4, a DC bitstream of depth 5, a DC bitstream of depth 6, and a DC bitstream of depth 7 are included slice 6 in this order. This example corresponds to a case where direct coding is performed starting at depth 4.

When the reception device uses only information about depths up to octree depth (i.e., group 2), the reception device may select slices 1 and 2 to reconstruct the AEC bitstream corresponding to octree depth 5 through an AEC decoder. Additionally, the reception device may select slice 6, and reselect DC bitstreams corresponding to depths 4 and in slice 6 to reconstruct the bitstreams through a DC decoder. That is, the reception device may independently reconstruct the positions of direct-coded points through the DC decoder.

In this case, the DC decoder may select and use only necessary information for a corresponding layer from the x, y, and z position information.

In the example below, the octree depth A is an octree depth at which direct coding is performed, and the octree depth B is an octree depth after the octree depth A and may be a value obtained by subtracting the octree depth A from the full octree depth N. That is, when it is defined that octree depth B=octree depth (N-A), the position of the DC compressed leaf node may be defined as a DC position at the AEC occupied node position before DC as shown in Equation 5 below. That is, when direct coding is performed, if a preset condition is satisfied while performing coding toward the leaf node by octree coding, direct coding is performed. In this example, when the depth at which direct coding starts is depth B, the final node position is obtained by concatenating the position of the previous depth (e.g., depth A, octree-coded depth) with respect to depth B to the direct coded position. In other words, it is an equation for finding the position when decoding is fully performed.

Leaf node position (x or y or z)=occupied node position (from AEC bitstream of upper layer) at octree depth A <B+DC position after octree depth A  [Equation 5]

MaxNodeSize=1<43 shows an embodiment of a syntax structure of a geometry data unit header (or referred to as a geometry slice header) for information on segmented (separated) slices and/or information related to direct coding according to embodiments.

The geometry data unit header according to the embodiments may include a slice_id field and a bitstream_type field.

slice_id specifies an identifier for identifying a data unit (i.e., a slice). That is, slice_id specifies an indicator for distinguishing a slice or a data unit, and may deliver an indicator for a data unit (or called a slice) belonging to a slice layer.

bitstream_type indicates the type of a bitstream included in a slice. According to embodiments, bitstream_type may indicate the type of a bitstream included in the slice. bitstream_type equal to 0 may indicate that the bitstream is an arithmetic entropy coding (AEC) bitstream. bitstream_type equal to 1 may indicate that the bitstream is a direct coding (DC) bitstream. bitstream_type equal to 2 may indicate that an AEC bitstream and a DC bitstream are present together in the slice.

According to embodiments, when geom_scalable_transmission_enable_flag is equal to 1, the geometry data unit header may further include a scalable_layer_id field, a num_tree_depth_in_data_unit field, and a bitstream_type field.

scalable_layer_id may specify an identifier of a scalable layer related to a data unit (i.e., slice). That is, it specifies an indicator for a scalable layer constituting scalable transmission. According to embodiments, when a scalable layer is composed of multiple slices, common information may be carried in the scalable_layer_id field in a parameter set, and other individual information may be carried in the data unit header of FIG. 43 according to slices.

When multiple subgroups are present in the scalable layer corresponding to scalable_layer_id, the geometry data unit header may further include a sub_group_id field.

sub_group_id specifies an identifier of a subgroup belonging to the scalable layer specified by scalable_layer_id. That is, sub_group_id specifies an indicator of a subgroup in the layer group indicated by layer_group_id. The range of subgroup_id is 0 to num_subgroups_minus1 [layer_group_id], wherein subgroup_id indicates the order of slices in the same layer_group_id.

num_tree_depth_in_data_unit may indicate the number of tree depths including nodes belonging to a data unit (i.e., a slice).

According to embodiments, the geometry data unit header includes a loop iterated as many times as the value of num_tree_depth_in_data_unit. In an embodiment, i is initialized to 0, and is incremented by 1 each time the loop is executed. The loop is iterated until the value of i reaches the value of num_tree_depth_in_data_unit. This loop may include a tree_depth[i] field, a num_nodes field, and a num_nodes_in_subgroup1[i][sub_group_id] field.

tree_depth[i] may indicate the i-th tree depth. That is, tree_depth may indicate a tree depth.

num_nodes[i] may indicate the number of nodes included in the i-th tree depth. That is, num_nodes[i] may indicate the number of nodes belonging to the i-th tree depth (tree_depth) among the nodes belonging to a data unit.

num_nodes_in_subgroup[i][sub_group_id] indicates the number of nodes related to a data unit (i.e., a slice). That is, num_nodes_in_subgroup[i] indicates the number of nodes included in a subgroup indicated by sub_group=id. According to embodiments, the sum of all num_nodes in a geometry data unit specifies the total number of nodes in the geometry data unit.

According to embodiments, when bitstream_type is equal to 2, that is, an AEC bitstream and a DC bitstream are present together in the slice, the geometry data unit header may further include a dc_bitstream_offset field, a dc_bitstream_length field, and a dc_backward_enabled_flag field.

According to embodiments, dc_bitstream_offset and dc_bitstream_length may indicate the start/end positions of the DC bitstream and the total length of the DC bitstream in the slice. That is, when bitstream_type is equal to 2, an AEC bitstream and a DC bitstream may be present together in one slice. In this case, the start/end positions of the DC bitstream and the total length of the DC bitstream may be indicated.

dc_backward_enabled_flag equal to 1 may indicate that the DC bitstream is included in reverse order in the slice including both the AEC bitstream and the DC bitstream. In this case, the end of the bitstream of the slice may be the start of the DC bitstream, and dc_bitstream_offset may be the end of the DC bitstream. dc_backward_enabled_flag equal to 0 indicates that the DC bitstream is included in the same direction as the AEC bitstream in the slice including both the AEC bitstream and the DC bitstream. In this case, it may be seen that the DC bitstream starts from dc_bitstream_offset and ends at the end of the entire bitstream (i.e., dc_bitstream_offset+dc_bitstream_length).

According to embodiments, the geometry data unit header may further include a ref_slice_id field.

The ref_slice_id field may be used to indicate a slice that must precede the current slice for decoding of the current slice (see, for example, the headers of FIGS. 19 to 21).

According to embodiments, the information on the segmented (separated) slices and/or information related to direct coding of FIG. 43 may be included at any position in the geometry slice header (i.e., the geometry data unit header) of FIG. 42.

FIG. 44 is a diagram illustrating an embodiment of a syntax structure of geometry slice data (geometry_slice_data( )) according to the present disclosure. The geometry slice data (geometry_slice_data( )) according to embodiments may transmit a geometry bitstream belonging to a corresponding slice (or data unit).

The geometry_slice_data( ) according to the embodiments may include a first iteration statement repeated as many times as by the value of MaxGeometryOctreeDepth. In an embodiment, the depth is initialized to 0 and is incremented by 1 each time the iteration statement is executed, and the first iteration statement is repeated until the depth becomes equal to MaxGeometryOctreeDepth. The first iteration statement may include a second loop statement repeated as many times as the value of NumNodesAtDepth. In an embodiment, nodeidx is initialized to 0 and is incremented by 1 each time the iteration statement is executed. The second iteration statement is repeated until nodeidx becomes equal to NumNodesAtDepth. The second iteration statement may include xN=NodeX[depth][nodeIdx], yN=NodeY[depth][nodeIdx], zN=NodeZ[depth] [nodeIdx], and geometry_node(depth, nodeIdx, xN, yN, zN). MaxGeometryOctreeDepth indicates the maximum value of the geometry octree depth, and NumNodesAtDepth[depth] indicates the number of nodes to be decoded at the corresponding depth. The variables NodeX[depth][nodeIdx], NodeY[depth][nodeIdx], and NodeZ[depth] [nodeIdx] indicate the x, y, z coordinates of the idx-th node in decoding order at a given depth. The geometry bitstream of the node of the depth is transmitted through geometry_node(depth, nodeIdx, xN, yN, zN).

The geometry slice data (geometry_slice_data( ) according to the embodiments may further include geometry_trisoup_data( ) when the value of the log 2_trisoup_node_size field is greater than 0. That is, when the size of the triangle nodes is greater than 0, a geometry bitstream subjected to trisoup geometry encoding is transmitted through geometry_trisoup_data( ).

FIG. 45 shows an embodiment of a syntax structure of attribute_slice_bitstream( ) according to the present disclosure.

The attribute slice bitstream (attribute_slice_bitstream ( )) according to the embodiments may include an attribute slice header (attribute_slice_header( )) and attribute slice data (attribute_slice_data( )). According to the embodiments, the attribute slice bitstream 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 an attribute data unit data.

FIG. 46 shows an embodiment of a syntax structure of an attribute slice header (attribute_slice_header( )) according to the present disclosure.

The attribute slice header (attribute_slice_header( )) according to the embodiments may include an ash_attr_parameter_set_id field, an ash_attr_sps_attr_idx field, an ash_attr_geom_slice_id field, an ash_attr_layer_qp_delta_present_flag field, and an ash_attr_region_qp_delta_present_flag field.

When the value of the aps_slice_qp_delta_present_flag field of the APS is TRUE (e.g., 1), the attribute slice header (attribute_slice_header( )) according to the embodiments may further include a ash_attr_qp_delta_luma field. When the value of the attribute_dimension_minus1 [ash_attr_sps_attr_idx] field is greater than 0, the attribute slice header may further include an ash_attr_qp_delta_chroma field.

The ash_attr_parameter_set_id field specifies the value of the aps_attr_parameter_set_id field of the current active APS.

The ash_attr_sps_attr_idx field specifies an attribute set in the current active SPS.

The ash_attr_geom_slice_id field specifies the value of the gsh_slice_id field of the current geometry slice header.

The ash_attr_qp_delta_luma field specifies a luma delta quantization parameter qp derived from the initial slice qp in the active attribute parameter set.

The ash_attr_qp_delta_chroma field specifies the chroma delta qp derived from the initial slice qp in the active attribute parameter set.

The variables InitialSliceQpY and InitialSliceQpC are derived as follows.

InitialSliceQpY=aps_attrattr_initial_qp+ash_attr_qp_delta_luma

InitialSliceQpC=aps_attrattr_initial_qp+aps_attr_chroma_qp_offset+ash_attr_qp_delta_chroma

The ash_attr_layer_qp_delta_present_flag field specifies whether the ash_attr_layer_qp_delta_luma field and the ash_attr_layer_qp_delta_chroma field are present in the ASH for each layer. For example, when the value of the ash_attr_layer_qp_delta_present_flag field is 1, it indicates that the ash_attr_layer_qp_delta_luma field and the ash_attr_layer_qp_delta_chroma field are present in the ASH. When the value is 0, it indicates that the fields are not present.

When the value of the ash_attr_layer_qp_delta_present_flag field is TRUE, the ASH may further include an ash_attr_num_layer_qp_minus1 field.

ash_attr_num_layer_qp_minus1 plus 1 indicates the number of layers through which the ash_attr_qp_delta_luma field and the ash_attr_qp_delta_chroma field are signaled. When the ash_attr_num_layer_qp field is not signaled, the value of the ash_attr_num_layer_qp field will be 0. According to embodiments, NumLayerQp specifying the number of layers may be obtained by adding 1 to the value of the ash_attr_num_layer_qp_minus1 field (NumLayerQp=ash_attr_num_layer_qp_minus1+1).

According to embodiments, when the value of the ash_attr_layer_qp_delta_present_flag field is TRUE, the geometry slice header may include a loop iterated as many times as the value of NumLayerQp. In this case, in an embodiment, i may be initialized to 0 and incremented by 1 every time the loop is executed, and the loop is iterated until the value of i reaches the value of NumLayerQp. This loop contains an ash_attr_layer_qp_delta_luma[i] field. Also, when the value of the attribute_dimension_minus1[ash_attr_sps_attr_idx] field is greater than 0, the loop may further include an ash_attr_layer_qp_delta_chroma[i] field.

The ash_attr_layer_qp_delta_luma field indicates a luma delta quantization parameter qp from InitialSliceQpY in each layer.

The ash_attr_layer_qp_delta_chroma field indicates a chroma delta quantization parameter qp from InitialSliceQpC in each layer.

The variables SliceQpY[i] and SliceQpC[i] with i=0, . . . , NumLayerQPNumQPLayer−1 are derived as follows.

ash_attr_region_qp_delta_present_flag equal to 1 indicates that ash_attr_region_qp_delta, region bounding box origin, and size are present in the current the attribute slice header (attribute_slice_header( )) according to the embodiments. ash_attr_region_qp_delta_present_flag equal to 0 indicates that the ash_attr_region_qp_delta, region bounding box origin, and size are not present in the current attribute slice header.

That is, when the value of the ash_attr_layer_qp_delta_present_flag field is 1, the attribute slice header may further include an ash_attr_qp_region_box_origin_x field, an ash_attr_qp_region_box_origin_y field, an ash_attr_qp_region_box_origin_z field, an ash_attr_qp_region_box_width field, an ash_attr_qp_region_box_height field, an ash_attr_qp_region_box_depth field, and an ash_attr_region_qp_delta field.

The ash_attr_qp_region_box_origin_x field indicates the x offset of the region bounding box relative to slice_origin_x.

The ash_attr_qp_region_box_origin_y field indicates the y offset of the region bounding box relative to slice_origin_y.

The ash_attr_qp_region_box_origin_z field indicates the z offset of the region bounding box relative to slice_origin_z.

The ash_attr_qp_region_box_size_width field indicates the width of the region bounding box.

The ash_attr_qp_region_box_size_height field indicates the height of the region bounding box.

The ash_attr_qp_region_box_size_depth field indicates the depth of the region bounding box.

The ash_attr_region_qp_delta field indicates delta qp from SliceQpY[i] and SliceQpC[i] of a region specified by the ash_attr_qp_region_box field.

According to embodiments, the variable RegionboxDeltaQp specifying a region box delta quantization parameter is set equal to the value of the ash_attr_region_qp_delta field (RegionboxDeltaQp=ash_attr_region_qp_delta).

FIG. 43 shows an embodiment of a syntax structure of an attribute data unit header (or referred to as an attribute slice header) for information on segmented (separated) slices and/or information related to direct coding according to embodiments.

The attribute data unit header according to the embodiments may include a slice_id field and a ref_slice_id field.

slice_id specifies an identifier for identifying a data unit (i.e., a slice). That is, slice_id specifies an indicator for distinguishing a slice or a data unit, and may deliver an indicator for a data unit (or called a slice) belonging to a slice layer.

The ref_slice_id field may be used to indicate a slice that must precede the current slice for decoding of the current slice (see, for example, the headers of FIGS. 19 to 21).

According to embodiments, when attr_scalable_transmission_enable_flag field included in the APS is equal to 1 and aligned_slice_structure_enabled_flag is equal to 1, the attribute data unit header may further include an aligned_geom_data_unit_id field. When aligned_slice_structure_enabled_flag is equal to 0, the attribute data unit header may further include a scalable_layer_id field and a num_tree_depth_in_data_unit field.

When the attribute data unit conforms to the scalable transmission layer structure/slice structure of the geometry data unit, the aligned_geom_data_unit_id field may carry a corresponding geometry data unit id.

scalable_layer_id may specify an identifier of a scalable layer related to a slice (or data unit). That is, it specifies an indicator for a scalable layer constituting scalable transmission. According to embodiments, when a scalable layer is composed of multiple slices, common information may be carried in the scalable_layer_id field in a parameter set, and other individual information may be carried in the data unit header of FIG. 47 according to slices.

num_tree_depth_in_data_unit may indicate the number of tree depths including nodes belonging to a data unit (i.e., a slice).

According to embodiments, the attribute data unit header includes a loop iterated as many times as the value of num_tree_depth_in_data_unit. In an embodiment, i is initialized to 0, and is incremented by 1 each time the loop is executed. The loop is iterated until the value of i reaches the value of num_tree_depth_in_data_unit. This loop may include a tree_depth[i] field and a num_nodes[i] field.

tree_depth[i] may indicate the i-th tree depth. That is, tree_depth may indicate a tree depth.

num_nodes[i] may indicate the number of nodes included in the i-th tree depth. That is, num_nodes[i] may indicate the number of nodes belonging to the i-th tree depth (tree_depth) among the nodes belonging to a data unit.

According to embodiments, the information on the segmented (separated) slices and/or information related to direct coding of FIG. 47 may be included at any position in the attribute slice header (i.e., attribute data unit header) of FIG. 46.

FIG. 47 shows an embodiment of a syntax structure of attribute_slice_data( ) according to the present disclosure. attribute_slice_data( ) according to the embodiments may carry an attribute bitstream belonging to a corresponding slice. The attribute_slice_data( ) may include attributes or attribute-related data in relation to some or all of the point clouds.

In the attribute slice data (attribute_slice_data( )) of FIG. 48, dimension=attribute_dimension[ash_attr_sps_attr_idx] indicates the attribute_dimension of an attribute set identified by the ash_attr_sps_attr_idx field in the attribute slice header. The attribute_dimension indicates the number of components constituting an attribute. An attributes according to embodiments indicates reflectance, color, or the like. Therefore, the number of components differs among attributes. For example, an attribute corresponding to color may have three color components (e.g., RGB). Accordingly, an attribute corresponding to reflectance may be a mono-dimensional attribute, and an attribute corresponding to color may be a three-dimensional attribute.

The attributes according to the embodiments may be attribute-encoded per dimension.

For example, an attribute corresponding to reflectance and an attribute corresponding to color may be each attribute-encoded. Also, attributes according to the embodiments may be attribute-encoded together regardless of the dimensions. For example, an attribute corresponding to reflectance and an attribute corresponding to color may be attribute-encoded together.

In FIG. 48, zerorun specifies the number of 0′ prior to residual.

Also, in FIG. 48, according to an embodiment, i denotes the value of the i-th point for the attribute. In one embodiment, an attr_coding_type field and a lifting_adaptive_prediction_threshold field are signaled in the APS.

MaxNumPredictors in FIG. 48 is a variable used in the point cloud data decoding process, and may be obtained based on the value of the lifting_adaptive_prediction_threshold field signaled in the APS as follows.

MaxNumPredictors=lifting_max_num_direct_predicots+1