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

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 encoder (for example, 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 encoder may perform intra/inter-coding on the occupancy codes. The reception device (for example, 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 encoder (for example, the point cloud encoder of FIG. 4 or 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 encoder according to the embodiments may skip voxelization and perform direct coding to directly code the positions of points included in the specific region. The coordinates of a direct coding point according to the embodiments are referred to as direct coding mode (DCM). The point cloud encoder according to the embodiments may also perform trisoup geometry encoding, which is to reconstruct the positions of the points in the specific region (or node) based on voxels, based on a surface model. The trisoup geometry encoding is geometry encoding that represents an object as a series of triangular meshes. Accordingly, the point cloud decoder may generate a point cloud from the mesh surface. The direct coding and trisoup geometry encoding according to the embodiments may be selectively performed. In addition, the direct coding and trisoup geometry encoding according to the embodiments may be performed in combination with octree geometry coding (or octree coding).

To perform direct coding, the option to use the direct mode for applying direct coding should be activated. A node to which direct coding is to be applied is not a leaf node, and points less than a threshold should be present within a specific node. In addition, the total number of points to which direct coding is to be applied should not exceed a preset threshold. When the conditions above are satisfied, the point cloud encoder (or the arithmetic encoder 40004) according to the embodiments may perform entropy coding on the positions (or position values) of the points.

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

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

Once the vertex is detected, the point cloud encoder according to the embodiments may perform entropy encoding on the starting point (x, y, z) of the edge, the direction vector (Δx, Δy, Δz) of the edge, and the vertex position value (relative position value within the edge). When the trisoup geometry encoding is applied, the point cloud encoder according to the embodiments (for example, the geometry 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 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}\left[\begin{array}{c}{\mu }_x\\ {\mu }_y\\ {\mu }_z\end{array}\right]=\frac{1}{n}{\Sigma }_{i=1}^n\left[\begin{array}{c}{x}_i\\ y_i\\ z_i\end{array}\right];& i)\end{array}$$\begin{array}{cc}\left[\begin{array}{c}\begin{array}{c}{\stackrel{\_}{x}}_i\\ {\stackrel{\_}{y}}_i\end{array}\\ {\stackrel{\_}{z}}_i\end{array}\right]=\left[\begin{array}{c}\begin{array}{c}{x}_i\\ y_i\end{array}\\ z_i\end{array}\right]-\left[\begin{array}{c}{\mu }_x\\ {\mu }_y\\ {\mu }_z\end{array}\right];& \mathrm{ii})\end{array}$$\begin{array}{cc}\left[\begin{array}{c}{\sigma }_x^2\\ {\sigma }_y^2\\ {\sigma }_z^2\end{array}\right]={\Sigma }_{i=1}^n\left[\begin{array}{c}\begin{array}{c}{\stackrel{\_}{x}}_i^2\\ {\stackrel{\_}{y}}_i^2\end{array}\\ {\stackrel{\_}{z}}_i^2\end{array}\right]& \mathrm{iii})\end{array}$

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

Triangles formed from vertices ordered 1, . . . , n

n triangles

3 (1,2,3)

4 (1,2,3), (3,4,1)

5 (1,2,3), (3,4,5), (5,1,3)

6 (1,2,3), (3,4,5), (5,6,1), (1,3,5)

7 (1,2,3), (3,4,5), (5,6,7), (7,1,3), (3,5,7)

8 (1,2,3), (3,4,5), (5,6,7), (7,8,1), (1,3,5), (5,7,1)

9 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,1,3), (3,5,7), (7,9,3)

10 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,1), (1,3,5), (5,7,9), (9,1,5)

11 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,1,3), (3,5,7), (7,9,11), (11,3,7)

12 (1,2,3), (3,4,5), (5,6,7), (7,8,9), (9,10,11), (11,12,1), (1,3,5), (5,7,9), (9,11,1), (1,5,9)

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

FIG. 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 encoder according to the embodiments may perform entropy coding based on context adaptive arithmetic coding.

As described with reference to FIGS. 1 to 6, the point cloud content providing system or the point cloud encoder (for example, the point cloud video encoder 10002, the point cloud 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 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 encoder according to the embodiments determines occupancy of neighbor nodes of each node of the octree and obtains a value of a neighbor pattern. The neighbor node pattern is used to infer the occupancy pattern of the node. The left part 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 right 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 encoder may perform coding according to the neighbor node pattern value (for example, when the neighbor node pattern value is 63, 64 kinds of coding may be performed). According to embodiments, the point cloud encoder may reduce coding complexity by changing a neighbor node pattern value (for example, based on a table by which 64 is changed to 10 or 6).

FIG. 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 encoder (for example, the LOD generator 40009) may classify (reorganize) points by LOD. The figure shows the point cloud content corresponding to LODs. The leftmost picture in the figure represents original point cloud content. The second picture from the left of the figure represents distribution of the points in the lowest LOD, and the rightmost picture in the figure represents distribution of the points in the highest LOD. That is, the points in the lowest LOD are sparsely distributed, and the points in the highest LOD are densely distributed. That is, as the LOD rises in the direction pointed by the arrow indicated at the bottom of the figure, the space (or distance) between points is narrowed.

FIG. 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 encoder (for example, the point cloud video encoder 10002, the point cloud 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 encoder, but also by the point cloud 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 encoder according to the embodiments may perform prediction transform coding, lifting transform coding, and RAHT transform coding selectively or in combination.

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

The predicted attribute (or attribute value) according to the embodiments is set to the average of values obtained by multiplying the attributes (or attribute values) (e.g., color, reflectance, etc.) of neighbor points set in the predictor of each point by a weight (or weight value) calculated based on the distance to each neighbor point. The point cloud encoder according to the embodiments (for example, the coefficient quantizer 40011) may quantize and inversely quantize the residuals (which may be called residual attributes, residual attribute values, attribute residuals or attribute prediction residuals) obtained by subtracting a predicted attribute (attribute value) from the attribute (attribute value) of each point. The quantization process is configured as shown in the following table.

When the predictor of each point has neighbor points, the point cloud encoder (e.g., the arithmetic encoder 40012) according to the embodiments may perform entropy coding on the quantized and inversely quantized residual values as described above. When the predictor of each point has no neighbor point, the point cloud encoder according to the embodiments (for example, the arithmetic encoder 40012) may perform entropy coding on the attributes of the corresponding point without performing the above-described operation.

The point cloud encoder according to the embodiments (for example, the lifting transformer 40010) may generate a predictor of each point, set the calculated LOD and register neighbor points in the predictor, and set weights according to the distances to neighbor points to perform lifting transform coding. The lifting transform coding according to the embodiments is similar to the above-described prediction transform coding, but differs therefrom in that weights are cumulatively applied to attribute values. The process of cumulatively applying weights to the attribute values according to embodiments is configured as follows.

2) Lift prediction process: Subtract the value obtained by multiplying the attribute value of the point by the weight from the existing attribute value to calculate a predicted attribute value.

3) Create temporary arrays called updateweight and update and initialize the temporary arrays to zero.

4) Cumulatively add the weights calculated by multiplying the weights calculated for all predictors by a weight stored in the QW corresponding to a predictor index to the updateweight array as indexes of neighbor nodes. Cumulatively add, to the update array, a value obtained by multiplying the attribute value of the index of a neighbor node by the calculated weight.

5) Lift update process: Divide the attribute values of the update array for all predictors by the weight value of the updateweight array of the predictor index, and add the existing attribute value to the values obtained by the division.

6) Calculate predicted attributes by multiplying the attribute values updated through the lift update process by the weight updated through the lift prediction process (stored in the QW) for all predictors. The point cloud encoder (e.g., coefficient quantizer 40011) according to the embodiments quantizes the predicted attribute values. In addition, the point cloud encoder (e.g., the arithmetic encoder 40012) performs entropy coding on the quantized attribute values.

The point cloud encoder (for example, 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 encoder according to the embodiments scans the entire region from the voxel and repeats the merging process of merging the voxels into a larger block at each step until the root node is reached. The merging process according to the embodiments is performed only on the occupied nodes. The merging process is not performed on the empty node. The merging process is performed on an upper node immediately above the empty node.

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

$\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]$

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 (for example, encoding by the arithmetic encoder 400012). The weights are calculated as w_{l−1 x,y,z}=w_{l2 x,y,z}+w_l2x+1,y,z. The root node is created through the g_10,0,0 and g_l0,0,1 as follows.

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

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

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

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

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

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

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

The point cloud decoder according to the embodiments includes an arithmetic decoder (Arithmetic decode) 11000, an octree synthesizer (Synthesize octree) 11001, a surface approximation synthesizer (Synthesize surface approximation) 11002, and a geometry reconstructor (Reconstruct geometry) 11003, a coordinate inverse transformer (Inverse transform coordinates) 11004, an arithmetic decoder (Arithmetic decode) 11005, an inverse quantizer (Inverse quantize) 11006, a RAHT 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 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 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 encoder.

Although not shown in the figure, the elements of the point cloud 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 providing device, software, firmware, or a combination thereof. The one or more processors may perform at least one or more of the operations and/or functions of the elements of the point cloud decoder 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 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 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 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 acquirer 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 the 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 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 encoder (for example, 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. The 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. The 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 a combination 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 400012.

The transmission processor 12012 according to the embodiments may transmit each bitstream containing encoded geometry and/or encoded attributes and metadata information, or transmit one bitstream configured with the encoded geometry and/or the encoded attributes and the metadata information. When the encoded geometry and/or the encoded attributes and the metadata information according to the embodiments are configured into one bitstream, the bitstream may include one or more sub-bitstreams. The bitstream according to the embodiments may contain signaling information including a sequence parameter set (SPS) for signaling of a sequence level, a geometry parameter set (GPS) for signaling of geometry information coding, an attribute parameter set (APS) for signaling of attribute information coding, and a tile parameter set (TPS) for signaling of a tile level, and slice data. The slice data may include information about one or more slices. One slice according to embodiments may include one geometry bitstream Geom00 and one or more attribute bitstreams Attr00 and Attr10.

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

The TPS according to the embodiments may include information about each tile (for example, coordinate information and height/size information about a bounding box) for one or more tiles. The geometry bitstream may contain a header and a payload. The header of the geometry bitstream according to the embodiments may contain a parameter set identifier (geom_parameter_set_id), a tile identifier (geom_tile_id) and a slice identifier (geom_slice_id) included in the GPS, and information about the data contained in the payload. As described above, the metadata 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 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 decoder described with reference to FIGS. 1 to 11.

The reception device according to the embodiment includes 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 an inverse 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. The 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 13005 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 (for example, 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 transformer 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 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 illustrates an exemplary structure operable in connection with point cloud data transmission/reception methods/devices according to embodiments.

The structure of FIG. 14 represents a configuration in which at least one of a server 1460, a robot 1410, a self-driving vehicle 1420, an XR device 1430, a smartphone 1440, a home appliance 1450, and/or a head-mount display (HMD) 1470 is connected to the cloud network 1400. The robot 1410, the self-driving vehicle 1420, the XR device 1430, the smartphone 1440, or the home appliance 1450 is called a device. Further, the XR device 1430 may correspond to a point cloud data (PCC) device according to embodiments or may be operatively connected to the PCC device.

The cloud network 1400 may represent a network that constitutes part of the cloud computing infrastructure or is present in the cloud computing infrastructure. Here, the cloud network 1400 may be configured using a 3G network, 4G or Long Term Evolution (LTE) network, or a 5G network.

The server 1460 may be connected to at least one of the robot 1410, the self-driving vehicle 1420, the XR device 1430, the smartphone 1440, the home appliance 1450, and/or the HMD 1470 over the cloud network 1400 and may assist in at least a part of the processing of the connected devices 1410 to 1470.

The HMD 1470 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 1410 to 1450 to which the above-described technology is applied will be described. The devices 1410 to 1450 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 1430 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 1430 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 1430 may acquire information about the surrounding space or a real object, and render and output an XR object. For example, the XR/PCC device 1430 may match an XR object including auxiliary information about a recognized object with the recognized object and output the matched XR object.

The XR/PCC device 1430 may be implemented as a mobile phone 1440 by applying PCC technology.

The mobile phone 1440 may decode and display point cloud content based on the PCC technology.

The self-driving vehicle 1420 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 1420 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 1420 which is a target of control/interaction in the XR image may be distinguished from the XR device 1430 and may be operatively connected thereto.

The self-driving vehicle 1420 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 1420 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 1220 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 data (PCC) transmission/reception device according to the embodiments is connected to a vehicle for wired/wireless communication, the device may receive/process content data related to an AR/VR/PCC service, which may be provided together with the self-driving service, and transmit the same to the vehicle. In the case where the PCC transmission/reception device is mounted on a vehicle, the PCC transmission/reception device may receive/process content data related to the AR/VR/PCC service according to a user input signal input through a user interface device and provide the same to the user. The vehicle or the user interface device according to the embodiments may receive a user input signal. The user input signal according to the embodiments may include a signal indicating the self-driving service.

As described with reference to FIGS. 1 to 14, point cloud data is composed of a set of points, and each of the points may have geometry data (geometry information) and attribute data (attribute information). The geometry data is a three-dimensional position (e.g., coordinate values on x, y, and z axes) of each point. That is, the position of each point is represented by parameters of a coordinate system representing a three-dimensional space (e.g., parameters x, y, and z for three axes representing the space, such as the X-axis, Y-axis, and Z-axis). In addition, the attribute information may represent a color (RGB, YUV, etc.), reflectance, normal vectors transparency, and the like of a point. The attribute information may be expressed as a scalar or a vector.

According to embodiments, the point cloud data may be classified into category 1 of static point cloud data, category 2 of dynamic point cloud data, and category 3, which is acquired through dynamic movement, according to the type and acquisition method of the point cloud data. Category 1 is composed of a point cloud of a single frame with a high density of points for an object or space. The data of category 3 may be divided into frame-based data having multiple frames acquired through movement and fused data of a single frame obtained by matching a point cloud acquired through a LiDAR sensor and a color image acquired as a 2D image for a large space.

FIG. 15 is an exemplary block diagram illustrating a point cloud data transmission method according to embodiments. In the present embodiment, a projection process for point cloud data may be performed as a preprocessing for compressing attributes of the point cloud data. That is, geometry information related to input point cloud data is encoded. In the case of lossy coding, attribute information is matched with changed geometry information through geometry decoding and recoloring. Attribute encoding is then performed. Point cloud data projection may be performed by preprocessing when attribute encoding efficiency increases through changes in geometry information (when a certain pattern of data is acquired, such as, for example, LiDAR data).

This figure illustrates a point cloud data transmission method 15000 for a point cloud data transmission device (e.g., the point cloud data transmission device of FIG. 1, 2, 4, 11, or 12) according to embodiments. The point cloud data transmission device according to the embodiments may perform the same or similar operation as the encoding operation described with reference to FIGS. 1 to 14.

The point cloud data transmission method 15000 includes encoding geometry information related to point cloud data and encoding attribute information related to the point cloud data. The transmission method 15000 also includes transmitting a bitstream containing the point cloud data. The encoding of the attribute information may include determining whether to project the geometry information and projecting the geometry information (15001).

The point cloud data transmission device according to the embodiments may encode the point cloud data. The point cloud data transmission device according to the embodiments includes a geometry encoder configured to encode a geometry indicating a position of one or more points of the point cloud data and an attribute encoder configured to encode an attribute of the one or more points. The point cloud data transmission device may further include a projector configured to project positions of points for at least one of the geometry encoder and the attribute encoder. The geometry encoder according to the embodiments may perform the same or similar operation as the geometry encoding described with reference to FIGS. 1 to 14. The attribute encoder according to the embodiments may perform the same or similar operation as the attribute encoding operation described with reference to FIGS. 1 to 14. The projector may perform the projection operation in FIGS. 16 to 54, which will be described later.

The projection according to the embodiments may include converting coordinates indicating the positions of the points presented in the first coordinate system and presenting the converted coordinates in a second coordinate system, and projecting the positions of the points based on the converted coordinates indicating the positions of the points and presented in the second coordinate system. The projection 15001 of FIG. 15 may include converting the coordinates indicating the positions of the points presented in the first coordinate system and presenting the converted coordinates in the second coordinate system. The projection 15001 of FIG. 15 may include projecting the positions of the points based on the converted coordinates indicating the positions of the points and presented in the second coordinate system. The first coordinate system and the second coordinate system are the same as or similar to the first coordinate system and the second coordinate system in FIGS. 16 to 54, which will be described later. The first coordinate system according to embodiments may include a Cartesian coordinate system, and the second coordinate system may include a spherical coordinate system, a cylindrical coordinate system, or a fan-shaped coordinate system. The operation of projecting the positions of the points may be based on the coordinates indicating the positions of the points converted into the second coordinate system and a scale value. The scale value according to the embodiments is the same as or similar to the scale value for each axis described with reference to FIGS. 16 to 54.

The point cloud data transmission device according to the embodiments may transmit a bitstream containing the encoded point cloud data. The bitstream may contain signaling information about the projection operation.

FIG. 16 is an exemplary block diagram illustrating a point cloud data reception method 16000 according to embodiments. In the reception method 16000, geometry information is decoded and then attribute information is decoded. When the projection is performed on the transmitting side, the result of the attribute decoding corresponds to information matching the projected point cloud data. Accordingly, as a preprocessing operation in the attribute decoding, projection may be performed on the decoded geometry information, and the decoded attribute information may be matched with the projected geometry information. In the projection space, point cloud data in which geometry and attributes are matched may be converted into the original space positions through inverse projection.

This figure illustrates a method of receiving point cloud data by a point cloud data reception device (e.g., the point cloud data reception device of FIGS. 1, 2, 10, and 13) according to embodiments. The point cloud data reception device according to the embodiments may perform an operation the same as or similar to the decoding operation described with reference to FIGS. 1 to 35.

The point cloud data reception method 16000 includes receiving a bitstream containing point cloud data. The bitstream according to the embodiments may contain signaling information about inverse projection.

The point cloud data reception method 16000 according to embodiments includes decoding point cloud data. Specifically, the reception method 16000 includes decoding geometry information related to the point cloud data and decoding attribute information. The reception method 16000 may include an operation 16001 of projecting the geometry information and an operation 16002 of performing inverse projection in decoding and reconstructing the point cloud data. In this case, the projection 16001 and the inverse projection 16002 may be performed based on signaling information transmitted from the transmission device according to the embodiments.

From the perspective of the device corresponding to the above-described reception method 16000 and, the point cloud data reception device according to the embodiments may decode the point cloud data. The point cloud data reception device according to the embodiments may include a geometry decoder configured to decode a geometry indicating a position of one or more points of the point cloud data and an attribute decoder configured to decode an attribute of the one or more points. The point cloud data reception device according to the embodiments may further include an inverse projector configured to inversely project the positions of the points for at least one of the geometry decoder and the attribute decoder. The geometry decoder may perform an operation the same as or similar to the geometry decoding described with reference to FIGS. 1 to 54. The attribute decoder according to the embodiments may perform an operation the same as or similar to the attribute decoding described with reference to FIGS. 1 to 54. The inverse projector according to embodiments may perform an operation the same as or similar to the operation of the inverse projector described with reference to FIGS. 15 to 54.

The inverse projection according to the embodiments may include inversely projecting the positions of points based on coordinates indicating the positions of the points and converting the coordinates indicating the positions of the points presented in a second coordinate system into a first coordinate system. The coordinates indicating the positions of the inverse-projected points described above may be presented in the first coordinate system. The coordinate inverse projector 51130 of FIG. 51B may perform the inverse projection of the positions of the points based on the coordinates indicating the positions of the points. The inverse projector 51130 of FIG. 51B may perform convert the coordinates indicating the positions of the points presented in the second coordinate system into the first coordinate system. The first coordinate system and the second coordinate system according to the embodiments may be the same as or similar to the first coordinate system and the second coordinate system described with reference to FIGS. 15 to 54. The first coordinate system according to the embodiments may include a Cartesian coordinate system, and the second coordinate system may include a spherical coordinate system, a cylindrical coordinate system, or a fan-shaped coordinate system. The second coordinate system may include a fan-shaped spherical coordinate system and a fan-shaped cylindrical coordinate system. The inverse projection of the positions of the points according to the embodiments may be based on the coordinates indicating the positions of the points presented in the second coordinate system and a scale value.

FIG. 17 illustrates an example of coordinate conversion of point cloud data according to embodiments.

This figure illustrates an example of the projection 15001 in the transmission method 15000 of FIG. 15 by the point cloud data transmission device (e.g., the point cloud video encoder 10002 of FIG. 1) according to the embodiments.

The point cloud data transmission device (e.g., the point cloud video acquirer 10001 of FIG. 1) according to the embodiments may acquire point cloud video (e.g., the point cloud video of FIG. 1). The transmission device according to the embodiments may acquire a point cloud video using LiDAR. The transmission device according to the embodiments may acquire a point cloud video through an outward-facing technique (e.g., the outward-facing technique of FIG. 3) using LiDAR.

Point cloud data acquired through the LiDAR according to embodiments may be referred to as LiDAR data. The view 23001 on the left side of FIG. 23 exemplarily shows LiDAR data. The structure of the LiDAR according to the embodiments is schematically illustrated in the left view 18001 of FIG. 18 and FIGS. 21 and 22. The LiDAR according to the embodiments may acquire LiDAR data based on reflection of light emitted from one or more lasers arranged in a spinning LiDAR header. Accordingly, the LiDAR data may be configured in a cylindrical shape or a spherical shape. The distance between the light rays emitted by the one or more lasers increases as the distance from the LiDAR increases. Accordingly, the LiDAR data according to the embodiments may have a cylindrical shape in which points are sparsely distributed as the distance from the LiDAR header increases. When the encoding operation is performed based on the geometry of unevenly distributed points, coding efficiency may be degraded. For example, in the case where the encoding operation is performed based on the geometry of the unevenly distributed points, the transmission device may fail to search for points distributed at a close distance from a specific point.

Accordingly, the transmission device according to the embodiments may project may uniformly distribute the points of the point cloud data (e.g., LiDAR data) by projecting the points of the point cloud data (converting the positions thereof).

FIG. 17 illustrates converting coordinate by a transmission device (e.g., FIG. 49a) or a transmission method (FIG. 15) according to embodiments to perform a projection operation. In other words, FIG. 17 shows converting coordinates indicating positions of points presented in a first coordinate system (e.g., a Cartesian coordinate system 17001) into a second coordinate system (e.g., spherical coordinates 17003 or cylindrical coordinates 17002). The positions of the points of point cloud data (e.g., LiDAR data) acquired by the transmission device according to the embodiments may be represented by Cartesian coordinates. Accordingly, the positions of the points of the point cloud data acquired by the transmission device according to the embodiments may be expressed by parameters representing the Cartesian coordinate system (e.g., the coordinates on the x-axis, y-axis, and z-axis). The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into cylindrical coordinates or spherical coordinates.

The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into cylindrical coordinates. That is, the transmission device may convert the coordinates indicating the positions of the points represented as parameters representing the Cartesian coordinate system (e.g., the coordinates on the x-axis, y-axis, and z-axis) into parameters (e.g., r, θ, and z) representing cylindrical coordinates. For example, the operation of the transmission device converting the x-axis value, the y-axis value, and the z-axis value in the Cartesian coordinate system into values of r, θ, and z in the cylindrical coordinate system may be represented as follows.

$r=\sqrt{x^2+y^2};$$\theta ={\tan }^{-1}\left(\frac{y}{x}\right);$$z=z.$

The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into spherical coordinates. In other words, the transmission device according to the embodiments may convert the coordinates indicating the positions of the points represented by parameters representing the Cartesian coordinate system (e.g., the coordinates on the x-axis, y-axis, and z-axis) into parameters (e.g., τ, Φ and θ) representing the spherical coordinate system. For example, the operation of the transmission device converting the x-axis value, the y-axis value, and the z-axis value in the Cartesian coordinate system into values of ρ, Φ and θ in the spherical coordinate system may be represented as follows.

$\rho =\sqrt{x^2+y^2+z^2};$$\theta ={\tan }^{-1}\left(\frac{y}{x}\right);$$\Phi ={\cos }^{-1}\left(\frac{y}{\sqrt{x^2+y^2+z^2}}\right).$

The projection operation (15001 in FIG. 15) according to the embodiments may include converting coordinates and projecting positions of points based on the converted coordinates indicating the positions of the points. The projection operation according to the embodiments may be performed by the projector 49110 of the transmission device (FIG. 49A) according to the embodiments.

FIG. 18 shows an example of a fan-shaped coordinate system according to embodiments.

The fan-shaped coordinate system according to the embodiments may be an additional option for coordinate conversion in addition to the cylindrical coordinate system and the spherical coordinate system. The fan-shaped coordinate system is based on that lasers arranged vertically in the LiDAR rotate horizontally to acquire data. The lasers are vertically disposed at a certain angle on the LiDAR head and horizontally rotate around a vertical axis to acquire data. In order to widen the data acquisition range, the disposed lasers are often disposed to spread radially. The fan-shaped coordinate system adopted considering this arrangement is intended for conversion based on a point on the central axis of a cylinder or sphere. The shape of the fan-shaped coordinate system corresponds to a portion of the cylinder or sphere that overlaps a figure formed by horizontally rotating, around the vertical axis, a fan-shaped plane the cylindrical or spherical shape having the origin thereof on the vertical axis and arranged parallel to the vertical axis. The elevation of the fan-shaped cylindrical coordinate system and the fan-shaped spherical coordinate system may be limited to a predetermined range.

FIG. 19 illustrates an example of coordinate conversion of fan-shaped coordinates of point cloud data according to embodiments. FIG. 19 illustrates coordinate conversion performed by the transmission device (e.g., FIG. 49a) or the transmission method (FIG. 15) to perform the projection operation according to the embodiments. In other words, FIG. 19 illustrates an operation of converting coordinates indicating positions of points presented in a first coordinate system (e.g., Cartesian coordinate system 19001 into a second coordinate system (e.g., fan-shaped spherical coordinates 19003 or fan-shaped cylindrical coordinates 19002). Positions of points of point cloud data (e.g., LiDAR data) acquired by the transmission device according to embodiments may be presented as Cartesian coordinates. Accordingly, the positions of the points of the point cloud data acquired by the transmission device according to the embodiments may be represented by parameters representing the Cartesian coordinate system (e.g., coordinates on the x-axis, y-axis, and z-axis). The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into fan-shaped cylindrical coordinates or fan-shaped spherical coordinates.

The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into fan-shaped cylindrical coordinates. In other words, the transmission device according to the embodiments may convert the coordinates indicating the positions of the points represented by parameters representing the Cartesian coordinate system (e.g., the coordinates on the x-axis, y-axis, and z-axis) into parameters representing the fan-shaped cylindrical coordinates (e.g., r, θ, and Φ). For example, the operation of the transmission device converting the x-axis value, the y-axis value, and the z-axis value in the Cartesian coordinate system into values of r, 0, and z in the fan-shaped cylindrical coordinate system may be represented as follows.

$r=\sqrt{x^2+y^2};$$\theta ={\tan }^{-1}\left(\frac{y}{x}\right);$$\Phi ={\tan }^{-1}\left(\frac{z}{\sqrt{x^2+y^2}}\right).$

The transmission device according to the embodiments may convert the coordinates indicating the positions of the points presented in the Cartesian coordinate system into fan-shaped spherical coordinates. In other words, the transmission device according to the embodiments may convert the coordinates indicating the positions of the points represented by parameters representing the Cartesian coordinate system (e.g., the coordinates on the x-axis, y-axis, and z-axis) into parameters representing the fan-shaped spherical coordinates (e.g., ρ, θ, and Φ). For example, the operation of the transmission device converting the x-axis value, the y-axis value, and the z-axis value in the Cartesian coordinate system into values of ρ, θ, and Φ in the cylindrical coordinate system may be represented as follows.

$\rho =\sqrt{x^2+y^2+z^2};$$\theta ={\tan }^{-1}\left(\frac{y}{x}\right);$$\Phi ={\sin }^{-1}\left(\frac{z}{\sqrt{x^2+y^2+z^2}}\right).$

The projection operation (15001 in FIG. 15) according to the embodiments may include converting coordinates and projecting positions of points based on the converted coordinates indicating the positions of the points. The projection operation according to the embodiments may be performed by the projector 49110 of the transmission device (FIG. 49A) according to the embodiments.

The coordinate conversion may include selecting coordinates and applying the coordinate conversion. In the coordinate selection, coordinate conversion information is induced. The coordinate conversion information may include whether to perform coordinate conversion or coordinate information. The coordinate conversion information may be signaled per sequence, frame, tile, slice, or block. In addition, the coordinate conversion information may be derived based on coordinate conversion of a neighboring block, the size of the block, the number of points, the quantization value, the depth of block partition, the position of the unit, and the distance between the unit and the origin.

In the applying of the coordinate conversion, the coordinate conversion is performed based on the selected coordinate system. In the applying of the coordinate conversion, the coordinate conversion may be performed based on the coordinate conversion information. Alternatively, the coordinate conversion may not be performed based on the coordinate conversion information.

FIG. 20 illustrates an example of coordinate projection of point cloud data according to embodiments. This is an example of point cloud data projection by a point cloud data transmission device (e.g., the point cloud video encoder 10002 of FIG. 1) according to embodiments.

In order to compress point cloud data represented by converted coordinates, projection in a compressible form is required. FIG. 20 illustrates an operation of converting fan-shaped cylindrical or fan-shaped spherical coordinates into a cuboid space according to embodiments. Fan-shaped cylindrical or fan-shaped spherical coordinates (r, θ, Φ) or (ρ, θ, Φ) are converted into (X′, Y′, Z′). In the figure, (x_max, y_max, z_max) and (x_min, y_min, z_min) indicate the maximum and minimum values on each axis. (r, θ, Φ) or (ρ, θ, Φ) may correspond to each axis of X′, Y′, and Z′, or may be subjected to separate conversion. In the case of fan-shaped cylindrical coordinates, the range of Φ is limited, and compression efficiency may be increased by grouping the mapped values for the Z′ axis using tangent.

When the coordinates indicating the positions of the points presented in the Cartesian coordinate system in FIG. 20 are converted into fan-shaped cylindrical coordinates 20001 or fan-shaped spherical coordinates 20002, the transmission device (e.g., the projector 49110) according to the embodiments may project the points (i.e., convert the positions) based on the converted coordinates. For example, the transmission device may convert the positions of the points by matching the axes of the Cartesian coordinate system with r, θ, and Φ of the coordinates presented in the fan-shaped cylindrical coordinate system. The transmission device according to the embodiments may present coordinates indicating the positions of the projected points in a new coordinate system. For example, the transmission device may present the coordinates indicating the positions of the projected points as parameters representing a new Cartesian coordinate system (e.g., coordinates on each of the X′-axis, Y′-axis, and Z′-axis). The new Cartesian coordinate system according to the embodiments may be referred to as a third coordinate system. The new Cartesian coordinate system according to the embodiments may include origin (0, 0, 0), a pole (r_max (e.g., the maximum value of r), 3600 (e.g., a value corresponding to 2π [rad]), z_max (e.g., the maximum value of z]), X′-axis, Y′-axis, and Z′-axis. The X′, Y′, and Z′ axes of the new Cartesian coordinate system according to the embodiments may be orthogonal to each other at the origin (0, 0, 0). The operation of projecting points (converting positions) by the transmission device according to the embodiments is configured as follows.

1) Fan-Shaped Cylindrical Coordinate Projection 1

$f_x\left(r\right)=r=\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2},;$$f_y\left(\theta \right)=\theta ={\tan }^{-1}\left(\frac{y-y_c}{x-x_c}\right),$$f_z\left(\varphi \right)=\varphi ={\tan }^{-1}\left(\frac{z-z_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2}}\right).$

2) Fan-Shaped Cylindrical Coordinate Projection 2

It is a method that minimizes the calculation of the trigonometric function in consideration of the convenience of calculation.

$f_x\left(r\right)=r^2={\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2,$$f_y\left(\theta \right)={\cos }^2\frac{\theta }{2}=\frac{1+\cos \theta }{2}=\left[1+\frac{x-x_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2}}\right]/2=\frac{r+x-x_c}{2r},$$f_z\left(\varphi \right)=\tan \varphi =\frac{z-z_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2}}=\frac{z-z_c}{r}$

Projection may be performed for fan-shaped spherical coordinates in a similar manner.

1) Fan-Shaped Spherical Coordinate Projection 1

$f_x\left(\rho \right)=\rho =\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2+{\left(z-z_c\right)}^2},$$f_y\left(\theta \right)=\theta ={\tan }^{-1}\left(\frac{y-y_c}{x-x_c}\right),$$f_z\left(\varphi \right)=\varphi ={\sin }^{-1}\left(\frac{z-z_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2+{\left(z-z_c\right)}^2}}\right)$

2) Fan-Shaped Spherical Coordinate Projection 2

$f_x\left(\rho \right)={\rho }^2={\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2+{\left(z-z_c\right)}^2,$$f_y\left(\theta \right)={\cos }^2\frac{\theta }{2}=\frac{1+\cos \theta }{2}=\left[1+\frac{x-x_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2}}\right]/2=\frac{r+x-x_c}{2r},$$f_z\left(\varphi \right)=\sin \varphi =\frac{z-z_c}{\sqrt{{\left(x-x_c\right)}^2+{\left(y-y_c\right)}^2+{\left(z-z_c\right)}^2}}=\frac{z-z_c}{\rho }.$

In the equations given above, (x_c, y_c, z_c) may denote the center position in the coordinate system before conversion, and may mean the head position of the LiDAR (e.g., xyz coordinates in the world coordinate system).

Hereinafter, projection adjustment considering the laser position of the LiDAR will be described.

The projection adjustment considering the laser position may be performed by hardware including one or more processors or integrated circuits configured to communicate with the transmission device 10000 in FIG. 1, the transmission device in FIG. 4, the transmission device in FIG. 12, the XR device 1430 in FIG. 14, the transmission device 15000 in FIG. 15, the transmission device 49000 in FIG. 49, and/or one or more memories, software, firmware, or a combination thereof. Specifically, the operation may be performed by the projector 49110 of the transmission device 49000 according to the embodiments.

In addition, the projection adjustment considering the laser position may be performed by hardware including one or more processors or integrated circuits configured to communicate with the reception device 10004 in FIG. 1, the reception device in FIG. 11, the reception device in FIG. 13, the XR device 1430 in FIG. 14, the reception device 16000 in FIG. 16, the reception device 51000 in FIG. 51, and/or one or more memories, software, firmware, or a combination thereof. Specifically, the operation may be performed by the reprojector 51110 of the reception device 51000 according to the embodiments.

FIG. 21 schematically illustrates a LiDAR structure for acquiring point cloud data according to embodiments. In the structure of the LiDAR, a plurality of lasers is virtually arranged on the LiDAR head. In addition, as shown on the left side of FIG. 21, lasers may be disposed on the upper and lower portions of the LiDAR head, respectively, to acquire more point cloud data. In this case, a difference in position between the lasers may occur and may cause a decrease in accuracy of projection. Accordingly, a method of adjusting the projection in consideration of the positions of the lasers may be used.

FIG. 22 shows a head position of LiDAR will and a relative position of a laser according to embodiments. In FIG. 22, the position of the laser disposed in a vertical plane is spaced apart from the head position (x_c, y_c, z_c) by r_L in the horizontal direction and by z_L in the vertical direction. Assuming that the head position is (0, 0, 0), the laser may be positioned at (x_L, y_L, z_L) in the xyz coordinate system, and (x_L, y_L) may be obtained from the following equation having r_L.

x_L=r_L·cos θ. y_L=r_L·sin θ