JP7680398B2 - Point group decoding device, point group decoding method and program - Google Patents

Description

The present invention relates to a point cloud decoding device, a point cloud decoding method, and a program.

Non-Patent Document 1 discloses a technique for reconstructing a decoded point cloud using a method called Trisoup for one predetermined type of node size.

G-PCC Future Enhancement, ISO/IEC JTC1/SC29/WG11 N19328

However, the method in Non-Patent Document 1 has only one type of node size (fixed node size), and there is room for improvement in terms of coding efficiency.

The present invention has been made in consideration of the above-mentioned problems, and aims to provide a point cloud decoding device, a point cloud decoding method, and a program that can improve the encoding efficiency without compromising the subjective image quality of the decoded point cloud.

The first feature of the present invention is a point cloud decoding device comprising an approximate surface synthesis unit, which is configured to execute a vertex decoding process at each node size between the maximum and minimum based on the maximum node size and the minimum node size decoded from the control data, generate a vertex position at the minimum node size based on the decoded vertex if the node size is not the minimum node size, and set the vertex position of the edge to the vertex generated by the node of the largest node size among the adjacent different nodes if there are multiple vertices on the edge generated by nodes of different node sizes.

The second feature of the present invention is a point cloud decoding method comprising the steps of: executing a vertex decoding process at each node size between the maximum and minimum based on the maximum and minimum node sizes decoded from control data; generating a vertex position at the minimum node size based on the decoded vertex if the node size is not the minimum node size; and setting the vertex generated by the node with the largest node size among the adjacent different nodes as the vertex position of the side if there are multiple vertices on a side generated from nodes of different node sizes adjacent to the side.

The third feature of the present invention is a program for causing a computer to function as a point cloud decoding device, the point cloud decoding device including an approximate surface synthesis unit, which executes a vertex decoding process at each node size between the maximum and minimum based on the maximum node size and the minimum node size decoded from control data, generates a vertex position at the minimum node size based on the decoded vertex if the node size is not the minimum node size, and configures the vertex position of the edge to be the vertex generated by the node of the largest node size among the adjacent different nodes if there are multiple vertices on an edge that are generated from nodes of different node sizes.

The present invention provides a point cloud decoding device, a point cloud decoding method, and a program that can improve the encoding efficiency without compromising the subjective image quality of the decoded point cloud.

FIG. 1 is a diagram illustrating an example of the configuration of a point cloud processing system 10 according to an embodiment. FIG. 2 is a diagram illustrating an example of functional blocks of a point group decoding device 200 according to an embodiment. FIG. 3 is a diagram showing an example of the configuration of encoded data (bit stream) received by the geometric information decoding unit 2010 of the point cloud decoding device 200 according to an embodiment. FIG. 4 is a diagram showing an example of the syntax configuration of GPS2011. FIG. 5 is a diagram showing an example of the syntax configuration of GSH2012. FIG. 6 is a diagram showing an example of the syntax configuration of GSH2012. FIG. 7 is a diagram showing an example of the syntax configuration of GSH2012. FIG. 8 is a flowchart illustrating an example of a process in the tree synthesis unit 2020 of the point group decoding device 200 according to an embodiment. FIG. 9 is a flowchart showing an example of processing in the tree synthesis unit 2020 of the point group decoding device 200 according to an embodiment. FIG. 10 is a flowchart showing an example of processing by the approximate surface synthesis unit 2030 of the point cloud decoding device 200 according to an embodiment. FIG. 11A is a diagram for explaining an example of the process of step S1006 in FIG. FIG. 11B is a diagram for explaining an example of the process of step S1006 in FIG. FIG. 12A is a diagram for explaining an example of the process of step S1006 in FIG. FIG. 12B is a diagram for explaining an example of the process of step S1006 in FIG. FIG. 13 is a flowchart showing an example of the vertex decoding process used in step S1002 in FIG. FIG. 14 is a flowchart showing an example of the process of step S1304 in FIG. FIG. 15 is a diagram showing an example of functional blocks of the point group encoding device 100 according to this embodiment.

The following describes embodiments of the present invention with reference to the drawings. Note that the components in the following embodiments can be replaced with existing components as appropriate, and various variations, including combinations with other existing components, are possible. Therefore, the description of the following embodiments does not limit the content of the invention described in the claims.

First Embodiment
A point cloud processing system 10 according to a first embodiment of the present invention will be described below with reference to Figures 1 to 15. Figure 1 is a diagram showing a point cloud processing system 10 according to the present embodiment.

As shown in FIG. 1, the point cloud processing system 10 has a point cloud encoding device 100 and a point cloud decoding device 200.

The point cloud encoding device 100 is configured to generate encoded data (bit stream) by encoding an input point cloud signal. The point cloud decoding device 200 is configured to generate an output point cloud signal by decoding the bit stream.

The input point cloud signal and the output point cloud signal are composed of position information and attribute information of each point in the point cloud. The attribute information is, for example, color information and reflectance of each point.

Here, such a bit stream may be transmitted from the point cloud encoding device 100 to the point cloud decoding device 200 via a transmission path. Also, the bit stream may be stored in a storage medium and then provided from the point cloud encoding device 100 to the point cloud decoding device 200.

(Point Cloud Decoding Device 200)
Hereinafter, the point group decoding device 200 according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of functional blocks of the point group decoding device 200 according to this embodiment.

As shown in FIG. 2, the point cloud decoding device 200 has a geometric information decoding unit 2010, a tree synthesis unit 2020, an approximate surface synthesis unit 2030, a geometric information reconstruction unit 2040, an inverse coordinate transformation unit 2050, an attribute information decoding unit 2060, an inverse quantization unit 2070, a RAHT unit 2080, an LoD calculation unit 2090, an inverse lifting unit 2100, and an inverse color transformation unit 2110.

The geometric information decoding unit 2010 is configured to receive as input a bit stream related to geometric information (geometric information bit stream) from the bit streams output from the point cloud encoding device 100, and to decode the syntax.

The decoding process is, for example, a context-adaptive binary arithmetic decoding process. Here, for example, the syntax includes control data (flags and parameters) for controlling the decoding process of the position information.

The tree synthesis unit 2020 is configured to receive as input the control data decoded by the geometric information decoding unit 2010 and an occurrence code indicating at which node in the tree (described later) the point group exists, and generate tree information indicating in which area in the decoding target space the point exists.

The decoding process of the occasion code may be configured to be performed within the tree synthesis unit 2020.

This process divides the space to be decoded into rectangular parallelepipeds, refers to the occupancy code to determine whether a point exists in each rectangular parallelepiped, divides the rectangular parallelepiped in which the point exists into multiple rectangular parallelepipeds, and then refers to the occupancy code. This process is repeated recursively to generate tree information.

Here, when decoding such an occasion code, inter prediction, which will be described later, may be used.

In this embodiment, a method called "Octree" can be used, which recursively performs octree division on the above-mentioned rectangular parallelepiped, always treating it as a cube, and a method called "QtBt" can be used, which performs quadtree division and binary tree division in addition to octree division. Whether or not to use "QtBt" is transmitted as control data from the point cloud encoding device 100.

In this embodiment, the method of decoding geometric information by dividing the above-mentioned cube using "Octree" until it becomes 1x1x1 in size is specifically referred to as "Octree only."

In addition, even if "Octree" is used in combination with "QtBt", the method of decrypting geometric information by dividing the above-mentioned rectangular parallelepiped until it becomes 1x1x1 size using only "Octree" and "QtBt" in the same manner as above may also be called "Octree only".

Alternatively, when the control data specifies that predictive coding is to be used, the tree synthesis unit 2020 is configured to decode the coordinates of each point based on an arbitrary tree configuration determined by the point cloud encoding device 100.

The approximate surface synthesis unit 2030 is configured to generate approximate surface information using the tree information generated by the tree synthesis unit 2020, and to decode the point cloud based on the approximate surface information.

When decoding three-dimensional point cloud data of an object, for example, if the points are densely distributed on the object's surface, approximate surface information is used to represent the area in which the points exist by approximating the area using a small plane, rather than decoding each point individually.

Specifically, the approximate surface synthesis unit 2030 can generate approximate surface information and decode the point cloud using, for example, a method called "Trisoup." A specific processing example of "Trisoup" will be described later. Also, when decoding a sparse point cloud acquired by Lidar or the like, this processing can be omitted.

The geometric information reconstruction unit 2040 is configured to reconstruct the geometric information (position information in the coordinate system assumed by the decoding process) of each point of the point cloud data to be decoded based on the tree information generated by the tree synthesis unit 2020 and the approximate surface information generated by the approximate surface synthesis unit 2030.

The inverse coordinate transformation unit 2050 is configured to receive the geometric information reconstructed by the geometric information reconstruction unit 2040 as input, transform it from the coordinate system assumed by the decoding process to the coordinate system of the output point cloud signal, and output position information.

The attribute information decoding unit 2060 is configured to receive as input a bit stream relating to attribute information (attribute information bit stream) from the bit streams output from the point cloud encoding device 100, and to decode the syntax.

The decoding process is, for example, a context-adaptive binary arithmetic decoding process. Here, for example, the syntax includes control data (flags and parameters) for controlling the decoding process of the attribute information.

The attribute information decoding unit 2060 is also configured to decode the quantized residual information from the decoded syntax.

The inverse quantization unit 2070 is configured to perform an inverse quantization process based on the quantized residual information decoded by the attribute information decoding unit 2060 and the quantization parameter, which is one of the control data decoded by the attribute information decoding unit 2060, to generate inverse quantized residual information.

The dequantized residual information is output to either the RAHT unit 2080 or the LoD calculation unit 2090 depending on the characteristics of the point group to be decoded. The control data decoded by the attribute information decoding unit 2060 specifies which unit the information is output to.

The RAHT unit 2080 is configured to receive the inverse quantized residual information generated by the inverse quantization unit 2070 and the geometric information generated by the geometric information reconstruction unit 2040 as input, and to decode the attribute information of each point using a type of Haar transform (inverse Haar transform in the decoding process) called RAHT (Region Adaptive Hierarchical Transform). As a specific example of the RAHT process, the method described in Non-Patent Document 1 can be used.

The LoD calculation unit 2090 is configured to receive the geometric information generated by the geometric information reconstruction unit 2040 as input and generate the LoD (Level of Detail).

LoD is information for defining a reference relationship (a referencing point and a referenced point) to realize predictive coding, such as predicting attribute information of a certain point from attribute information of another point and encoding or decoding the prediction residual.

In other words, LoD is information that defines a hierarchical structure in which each point contained in the geometric information is classified into multiple levels, and the attributes of points belonging to lower levels are encoded or decoded using attribute information of points belonging to higher levels.

As a specific method for determining LoD, for example, the method described in the above-mentioned non-patent document 1 may be used.

The inverse lifting unit 2100 is configured to decode attribute information of each point based on the hierarchical structure defined by the LoD, using the LoD generated by the LoD calculation unit 2090 and the inverse quantized residual information generated by the inverse quantization unit 2070. As a specific example of the inverse lifting process, the method described in the above-mentioned non-patent document 1 can be used.

The inverse color conversion unit 2110 is configured to perform inverse color conversion processing on the attribute information output from the RAHT unit 2080 or the inverse lifting unit 2100 when the attribute information to be decoded is color information and color conversion has been performed on the point cloud encoding device 100 side. Whether or not such inverse color conversion processing is performed is determined by the control data decoded by the attribute information decoding unit 2060.

The point cloud decoding device 200 is configured to decode and output attribute information for each point in the point cloud through the above processing.

(Geometric information decoding unit 2010)
The control data decoded by the geometric information decoding unit 2010 will be described below with reference to FIGS.

Figure 3 shows an example of the structure of the encoded data (bit stream) received by the geometric information decoding unit 2010.

First, the bit stream may include a GPS2011. A GPS2011 is also called a geometry parameter set, and is a collection of control data related to decoding of geometric information. A specific example will be described later. Each GPS2011 includes at least GPS id information for identifying each GPS2011 when multiple GPS2011 exist.

Secondly, the bit stream may include GSH2012A/2012B. GSH2012A/2012B is also called a geometry slice header or geometry data unit header, and is a collection of control data corresponding to a slice, which will be described later. In the following description, the term "slice" will be used, but slice can also be read as data unit. Specific examples will be described later. GSH2012A/2012B includes at least GPS id information for specifying the GPS2011 corresponding to each GSH2012A/2012B.

Thirdly, the bit stream may include slice data 2013A/2013B following GSH 2012A/2012B. Slice data 2013A/2013B includes data that encodes geometric information. An example of slice data 2013A/2013B is an occasion code, which will be described later.

As described above, the bit stream is structured so that each slice data 2013A/2013B corresponds to one GSH 2012A/2012B and one GPS 2011.

As described above, the GPS ID information is used to specify which GPS 2011 to refer to in GSH 2012A/2012B, so a common GPS 2011 can be used for multiple slice data 2013A/2013B.

In other words, GPS2011 does not necessarily need to be transmitted for each slice. For example, as shown in FIG. 3, the bit stream can be configured so that GPS2011 is not encoded immediately before GSH2012B and slice data 2013B.

Note that the configuration in FIG. 3 is merely an example. As long as GSH 2012A/2012B and GPS 2011 correspond to each slice data 2013A/2013B, elements other than those described above may be added as components of the bit stream.

For example, as shown in FIG. 3, the bitstream may include a sequence parameter set (SPS) 2001. Similarly, when transmitted, the bitstream may be shaped into a configuration different from that shown in FIG. 3. Furthermore, the bitstream may be combined with a bitstream decoded by an attribute information decoding unit 2060 (described later) and transmitted as a single bitstream.

Figure 4 is an example of the syntax configuration of GPS2011.

Note that the syntax names explained below are merely examples. If the syntax functions explained below are similar, the syntax names may be different.

GPS2011 may include GPS ID information (gsps_geom_parameter_set_id) for identifying each GPS2011.

The Descriptor column in Figure 4 indicates how each syntax is coded. ue(v) indicates an unsigned zeroth-order exponential Golomb code, and u(1) indicates a 1-bit flag.

GPS2011 may include a flag (trisoup_enabled_flag) that controls whether or not Trisoup is used in the approximate surface synthesis unit 2030.

For example, it may be defined that Trisoup is not used when the value of trisoup_enabled_flag is "0", and that Trisoup is used when the value of trisoup_enabled_flag is "1".

The geometric information decoding unit 2020 may be configured to additionally decode the following syntax when Trisoup is used, i.e., when the value of trisoup_enabled_flag is "1".

Note that trisoup_enabled_flag may be included in SPS2001 instead of GPS2011.

GPS2011 may include a flag (trisoup_multilevel_enabled_flag, first flag) that controls whether or not trisoup is allowed at multiple levels.

For example, when the value of trisoup_multilevel_enabled_flag is "0", multi-level trisoup is not allowed, i.e., trisoup is performed at a single level, and when the value of trisoup_multilevel_enabled_flag is "1", multi-level trisoup is allowed.

If this syntax is not included in GPS2011, the value of this syntax may be considered to be the value when performing a trisoup at a single level, i.e., "0".

Note that trisoup_multilevel_enabled_flag may be defined to be included in SPS2001 instead of GPS2011. In this case, if trisoup_multilevel_enabled_flag is not included in SPS2001, the value of the syntax may be considered to be the value for performing trisoup at a single level, i.e., "0".

Figure 5 shows an example of the syntax configuration of GSH2012. As mentioned above, GSH is also called GDUH (Geometry Data Unit Header).

The geometric information decoding unit 2020 may be configured to additionally decode the following syntax when Trisoup at multiple levels is permitted, i.e., when the value of trisoup_multilevel_enabled_flag is "1".

GSH2012 may include a flag (adaptive_octree_enabled_flag) that enables a mode of adaptively selecting between Octree-only and Trisoup within a slice when Trisoup is allowed at multiple levels.

For example, it may be defined that when the value of the flag is "1", a mode that adaptively selects between Octree and Trisoup (hereinafter referred to as "adaptive Octree mode") is enabled, and when the value of the flag is "0", the adaptive Octree mode is disabled.

Also, if the GSH2012 does not include this flag, the value of the flag may be implicitly considered to be "0", i.e., adaptive Octree mode is disabled.

GSH2012 may include syntax (log2_trisoup_max_node_size_minus2) to specify the maximum Trisoup node size when multi-level Trisoup is allowed.

The syntax may be expressed as a value obtained by converting the actual maximum Trisoup node size to a logarithm with base 2. Furthermore, the syntax may be expressed as a value obtained by subtracting 2 from the logarithm with base 2, for the actual maximum Trisoup node size.

GSH2012 may include syntax (log2_trisoup_min_node_size_minus2) to specify a minimum Trisoup node size when multi-level Trisoup is allowed.

The syntax may be expressed as a value obtained by converting the actual minimum Trisoup node size to a logarithm with base 2. Furthermore, the syntax may be expressed as a value obtained by subtracting 2 from the logarithm obtained by converting the actual minimum Trisoup node size to 4 (=2^2).

The value of this syntax may also be constrained to be greater than or equal to 0 and less than or equal to log2_trisoup_max_node_size_minus2.

In this case, trisoup_depth may be defined as trisoup_depth = log2_trisoup_max_node_size_minus2 - log2_trisoup_min_node_size_minus2 + 1, as shown in Figure 5.

In addition, instead of directly decoding the minimum and maximum Trisoup node sizes, the Depth values corresponding to the maximum and minimum Trisoup node sizes in the Octree processing described below may be decoded.

For example, if the maximum Depth (the Depth at which all nodes are 1x1x1 in size) is 10, and you want the minimum Trisoup node size to be 4 (= ²² ) and the maximum Trisoup node size to be 16 (= ²⁴ ), you may decode the Depth value corresponding to the minimum Trisoup node size as 8 and the Depth value corresponding to the maximum Trisoup node size as 6.

The geometric information decoding unit 2020 may be configured to additionally decode the following syntax when multi-level Trisoup is not permitted, i.e., when the value of trisoup_multilevel_enabled_flag is "0".

GSH2012 may include syntax (log2_trisoup_node_size_minus2) to specify the Trisoup node size when multi-level Trisoup is not allowed and when Trisoup is used.

The syntax may be expressed as a value obtained by converting the actual Trisoup node size to a logarithm with base 2. Furthermore, the syntax may be expressed as a value obtained by subtracting 2 from the logarithm with base 2 for the actual Trisoup node size.

In this case, trisoup_depth may be defined as trisoup_depth = 1, as shown in Figure 5.

When using Trisoup, GSH2012 may include a syntax (trisoup_sampling_value_minus1) that controls the sampling interval of the decoding point. The specific definition of the syntax may be the same as that described in the above-mentioned non-patent document 1, for example.

When GSH2012 uses Trisoup and allows Trisoup at multiple levels, it may include a flag (unique_segments_exist_flag[i]) for each hierarchical level i (i=0,...,trisoup_depth-1) indicating whether a unique segment exists in the target hierarchical level.

For example, if the value of unique_segments_exist_flag[i] is "1", it means that at least one unique segment exists in hierarchical layer i. Also, if the value of unique_segments_exist_flag[i] is "0", it means that no unique segments exist in hierarchical layer i.

For each hierarchical layer i (i = 0, ..., trisoup_depth-1), if a unique segment exists in the target hierarchical layer, i.e., if the value of unique_segments_exist_flag[i] is "1", GSH2012 may additionally include syntax (num_unique_segments_bits_minus1[i]) indicating the number of bits of the syntax indicating the number of unique segments in the target hierarchical layer and syntax (num_unique_segments_minus1[i]) indicating the number of unique segments in the target hierarchical layer.

Here, for both num_unique_segments_bits_minus1[i] and num_unique_segments_minus1[i], the original value minus "1" may be encoded as the syntax value.

Figure 6 shows an example of the syntax configuration of GSH2012. In what follows, we will only explain the differences from Figure 5.

As shown in Fig. 6, when the adaptive Octree mode is enabled, that is, when the value of adaptive_octree_enabled_flag is "1", the decoding of the syntax (log2_trisoup_min_node_size_minus2) that specifies the minimum value of the Trisoup node size may be omitted. In this case, the minimum value of the Trisoup node size may be regarded as a predetermined value. For example, the predetermined value may be regarded as "0" (i.e., 2 ⁰ =1).

As described above, the geometric information decoding unit 2010 may be configured to decode a flag that controls whether or not only Octree and both Trisoup are available within the same slice or data unit, and if the value of the flag indicates "available," to implicitly consider the value of the minimum Trisoup node size to be 1x1x1.

By configuring as described above, it is possible to eliminate the transmission of unnecessary syntax and reduce the amount of coding.

Figure 7 shows an example of the syntax configuration of GSH2012. In what follows, we will only explain the differences from Figure 5.

7, for each of the syntax that specifies the maximum value of the Trisoup node size and the syntax that specifies the minimum value of the Trisoup node size, values may be transmitted after being converted to logarithms with the base 2. In this case, the minimum value of each syntax may be specified as "0" (i.e., 2 ⁰ =1).

As described above, the geometric information decoding unit 2010 may be configured to decode the minimum Trisoup node size, and the minimum possible value of the minimum Trisoup node size may be a value equivalent to 1 x 1 x 1.

By configuring in this way, processing can be standardized when only Trisoup is used.

(Tree synthesis unit 2020)
The processing of the tree merging unit 2020 will be described below with reference to Figures 8 and 9. Figure 6 is a flowchart showing an example of the processing in the tree merging unit 2020. Note that the following describes an example in which trees are merged using "Octree".

In step S801, the tree synthesis unit 2020 checks whether processing for all depths has been completed. Note that the depth number may be included as control data in the bit stream transmitted from the point cloud encoding device 100 to the point cloud decoding device 200.

The tree synthesis unit 2020 calculates the node size of the target depth. In the case of "Octree", the node size of the first depth may be defined as "2 to the power of the depth number". In other words, if the depth number is N, the node size of the first depth may be defined as 2 to the power of N.

In addition, the node size at the second and subsequent depths may be defined by decrementing the number N by one. That is, the node size at the second depth may be defined as "2 to the power of (N-1)", the node size at the third depth may be defined as "2 to the power of (N-2)", and so on.

Alternatively, because node sizes are always defined as powers of 2, you can simply think of the exponent part (N, N-1, N-2, etc.) as the node size. In the following explanation, node size refers to the exponent part of the length of one side of the node.

For simplicity's sake, we'll use the example below where the node shape is a cube, i.e., all edges of the node are the same length.

When using QtBt, that is, when the node shape is a rectangular prism and the length of each side of the node differs in each axial direction (x, y, z), the length of the shortest side of the three directions may be considered to be the node size. Similarly, the length of the longest side of the three directions may be considered to be the node size.

Here, when the flag (trisoup_enabled_flag) that controls whether or not to use Trisoup indicates that Trisoup is to be used, that is, when the value of trisoup_enabled_flag is "1", the tree synthesis unit 2020 may change the number of Depths to be processed based on the value of the syntax (log2_trisoup_min_node_size_minus2) that specifies the minimum value of the Trisoup node size or the syntax (log2_trisoup_node_size_minus2) that specifies the Trisoup node size. In such a case, for example, it may be defined as follows.

Processing Depth Number=Total Depth Number−(Minimum) Trisoup Node Size Here, the minimum Trisoup node size can be defined as, for example, (log2_trisoup_min_node_size_minus2+2). Similarly, the Trisoup node size can be defined as (log2_trisoup_node_size_minus2+2).

In this case, if processing for all processing depths has been completed, the tree synthesis unit 2020 proceeds to step S809; otherwise, the tree synthesis unit 2020 proceeds to step S802.

In other words, if (processing depth number - n) = 0, the tree synthesis unit 2020 proceeds to step S809, and if (processing depth number - n) > 0, the tree synthesis unit 2020 proceeds to step S802.

The tree synthesis unit 2020 may also determine that Trisoup is applied to all nodes having the node size (N-processing depth number) when proceeding to step S809.

Here, when Trisoup at multiple levels is permitted, i.e., when the value of trisoup_multilevel_enabled_flag is "1", and when adaptive Octree mode is enabled, i.e., when the value of adaptive_octree_enabled_flag is "1", step S801 may be configured as follows:

Specifically, if processing of all depths has been completed, the tree synthesis unit 2020 proceeds to step S809, and if processing of all depths has not been completed, the tree synthesis unit 2020 proceeds to step S802.

In other words, when the target depth is the nth depth, if (N-n)=0, the tree synthesis unit 2020 proceeds to step S809, and if (N-n)>0, the tree synthesis unit 2020 proceeds to step S802.

In step S802, the tree synthesis unit 2020 determines whether or not it is necessary to decode the Trisoup_applied_flag described below at the target depth.

For example, if "Trisoups at multiple levels are permitted (the value of trisoup_multilevel_enabled_flag is "1")" and "the node size (N-n) of the target depth is equal to or smaller than the maximum trisoup node size," the tree synthesis unit 2020 may determine that "decoding of trisoup_applied_flag is necessary."

For example, if "Trisoups at multiple levels are allowed (the value of trisoup_multilevel_enabled_flag is "1")," and "the node size (N-n) of the target depth is equal to or smaller than the maximum trisoup node size," and "the node size (N-n) of the target depth is equal to or larger than the minimum trisoup node size," the tree synthesis unit 2020 may determine that "decoding of trisoup_applied_flag is necessary."

In addition, if the above conditions are not met, the tree synthesis unit 2020 may determine that "decoding of Trisoup_applied_flag is not necessary."

Here, the maximum Trisoup node size can be defined, for example, as (log2_trisoup_max_node_size_minus3 + 2). Similarly, the minimum Trisoup node size can be defined, for example, as (log2_trisoup_min_node_size_minus2 + 2).

Once the above-mentioned determination is completed, the tree synthesis unit 2020 proceeds to step S803.

In step S803, the tree synthesis unit 2020 determines whether processing of all nodes included in the target Depth has been completed.

If it is determined that processing of all nodes at the target Depth has been completed, the tree synthesis unit 2020 proceeds to step S801 and processes the next Depth.

On the other hand, if processing of all nodes at the target Depth has not been completed, the tree synthesis unit 2020 proceeds to step S804.

In step S804, the tree synthesis unit 2020 checks whether or not it is necessary to decode the Trisoup_applied_flag determined in step S802.

If it is determined that decoding of Trisoup_applied_flag is necessary, the tree synthesis unit 2020 proceeds to step S805, and if it is determined that decoding of Trisoup_applied_flag is not necessary, the tree synthesis unit 2020 proceeds to step S808.

In step S805, the tree synthesis unit 2020 decodes Trisoup_applied_flag.

Trisoup_applied_flag is a 1-bit flag (second flag) that indicates whether Trisoup is applied to the target node. For example, it may be defined that Trisoup is applied to the target node when the value of this flag is "1", and that Trisoup is not applied to the target node when the value of this flag is "0".

After the tree synthesis unit 2020 decodes Trisoup_applied_flag, it proceeds to step S806.

In step S806, the tree synthesis unit 2020 checks the value of Trisoup_applied_flag decoded in step S805.

If Trisoup is to be applied to the target node, i.e., if the value of Trisoup_applied_flag is "1", the tree synthesis unit 2020 proceeds to step S807.

If Trisoup is not applied to the target node, i.e., if the value of Trisoup_applied_flag is "0", the tree synthesis unit 2020 proceeds to step S808.

In step S807, the tree synthesis unit 2020 stores the target node as a node to which Trisoup is applied, i.e., as a Trisoup node. No further node splitting by "Octree" is applied to this target node. The tree synthesis unit 2020 then proceeds to step S803 and moves on to processing the next node.

In step S808, the tree synthesis unit 2020 decodes information called the occupancy code.

In the case of "Octree", the occupancy code is information that indicates whether the point to be decoded is included in each of the child nodes when the target node is divided in half in each of the x, y, and z axis directions and divided into eight nodes (called child nodes).

For example, the occupancy code may assign one bit of information to each child node, and if the one bit of information is "1", it may be defined that the point to be decoded is included in the child node, and if the one bit of information is "0", it may be defined that the point to be decoded is not included in the child node.

When decoding such an occurrence code, the tree synthesis unit 2020 may estimate in advance the probability that the point to be decoded exists in each child node, and entropy decode the bits corresponding to each child node based on that probability.

Similarly, entropy coding may be performed in the point cloud coding device 100.

As described above, the point cloud decoding device 200 may be configured to use both Octree only and Trisoup within the same slice or data unit, and to decode the geometric information of points for each node using either the Octree only or Trisoup method.

By configuring as described above, it is possible to switch between using only Octree for areas with locally detailed geometric structures and Trisoup for areas that can be approximated by a plane, enabling efficient encoding.

As described above, the tree synthesis unit 2020 may be configured to perform Octree partitioning to recursively partition the space, and to decode the geometric information for all nodes that are smaller than a predetermined minimum Trisoup node size using only Octree.

As described above, when the adaptive Octree mode is enabled, the tree synthesis unit 2020 may be configured to decode, for each node of a predetermined minimum Trisoup node size, a flag (Trisoup_applied_flag) indicating whether or not Trisoup is applied to the node.

As described above, the geometric information decoding unit 2010 may be configured to decode a flag that controls whether or not both Octree and Trisoup are available within the same slice or data unit, and the tree synthesis unit 2020 may be configured to decode the geometric information using Octree only for all nodes that are smaller than a predetermined minimum Trisoup node size when the value of the flag indicates "available."

By configuring as described above, when the Trisoup node size is at its minimum, it is possible to use only Octree without making any changes from Non-Patent Document 1, which has the advantage of reducing the amount of changes to the processing of Trisoup itself and making design easier.

Figure 9 is a flowchart showing an example of processing in the tree synthesis unit 2020. The same reference numerals are used for parts that perform the same processing as in Figure 8, and the description will be omitted.

In step S901, if processing for all processing depths has been completed, the tree synthesis unit 2020 proceeds to step S809; otherwise, the tree synthesis unit 2020 proceeds to step S802.

Here, when Trisoup at multiple levels is permitted (the value of trisoup_multilevel_enabled_flag is "1"), the tree synthesis unit 2020 may change the number of Depths to be processed based on the value of the syntax (log2_trisoup_node_size shown in FIG. 7) that specifies the minimum value of the Trisoup node size. At this time, the tree synthesis unit 2020 may set the minimum value of the Trisoup node size to 0.

Alternatively, as shown in FIG. 6, the tree synthesis unit 2020 may implicitly set the minimum Trisoup node size to 0 when adaptive Octree mode is enabled. In such a case, it may be defined, for example, as follows:

Processing Depth Number=Total Depth Number−Minimum Trisoup Node Size In this case, if processing for all processing Depth numbers has been completed, the tree merging unit 2020 proceeds to step S809, and if not, the tree merging unit 2020 proceeds to step S902.

In other words, if (processing depth number - n) = 0, the tree synthesis unit 2020 proceeds to step S809, and if (processing depth number - n) > 0, the tree synthesis unit 2020 proceeds to step S902.

In step S902, the tree synthesis unit 2020 determines whether or not it is necessary to decode the Trisoup_applied_flag described below at the target depth.

For example, if "Trisoups at multiple levels are permitted (the value of trisoup_multilevel_enabled_flag is "1")" and "the node size (N-n) of the target depth is equal to or smaller than the maximum trisoup node size," the tree synthesis unit 2020 may determine that "decoding of trisoup_applied_flag is necessary."

Furthermore, if the node size (N-processing depth number) when proceeding to step S809 is 1 or more ( ^2.1 or more), the tree merging unit 2020 may determine that Trisoup is applied to all nodes having that node size.

Furthermore, when the node size (N-processing depth number) when proceeding to step S809 is 0 (2 ⁰ =1), i.e., in the case of a node of size 1×1×1, the tree synthesis unit 2020 may regard the 1×1×1 node itself as having been decoded, as in the case of performing only Octree, without performing Trisoup processing, in the approximate surface synthesis unit 2030 described later.

As described above, the tree synthesis unit 2020 may be configured to set a predetermined minimum Trisoup node size to 1, and determine that for nodes with a node size of 1x1x1, the geometric information is decoded using only Octree, and for nodes with a node size larger than 1x1x1, the geometric information is decoded using Trisoup.

The above configuration has the advantage that the processing is the same as when only Trisoup is used.

(Approximate surface synthesis unit 2030)
The approximate surface synthesis unit 2030 is configured to perform a decoding process for each node determined to be a Trisoup node by the tree synthesis unit 2020, as will be described with reference to Figs.

Below, an example of the processing performed by the approximate surface synthesis unit 2030 is explained using Figures 10 to 14.

Figure 10 is a flowchart showing an example of processing by the approximate surface synthesis unit 2030.

As shown in FIG. 10, in step S1001, the approximate surface synthesis unit 2030 determines whether processing has been completed for all trisoup_depths.

If processing has been completed for all trisoup_depths, proceed to step S1009 and end processing. If processing has not been completed for all trisoup_depths, proceed to step S1002.

In step S1002, the approximate surface synthesis unit 2030 obtains and integrates the vertex positions for each node.

First, the approximate surface synthesis unit 2030 decodes the vertex positions for each node in the Trisoup node size corresponding to the trisoup_depth (hereinafter, referred to as the Trisoup node size). The specific process is described in FIG. 13.

Secondly, if the Trisoup node size is smaller than the maximum Trisoup node size, the approximate surface synthesis unit 2030 performs integration processing with vertices generated at a Trisoup node size larger than the Trisoup node size in step S1008 described below.

Specifically, when both a vertex decoded using the method described in FIG. 13 (a vertex at the Trisoup node size) and a vertex generated using the method described in step S1008 (a vertex generated at a Trisoup node size larger than the Trisoup node size) exist on an edge of a node at the Trisoup node size, the approximate surface synthesis unit 2030 performs processing to retain only the vertex generated at the largest node size and delete the other vertices. In this way, it is possible to reduce the number of vertices per node to a maximum of one.

After the vertex positions have been obtained and integrated, the approximate surface synthesis unit 2030 proceeds to step S1003.

In step S1003, the approximate surface synthesis unit 2030 determines a projection surface for each node in the Trisoup node size.

For example, when there are three axes, x-axis, y-axis, and z-axis, a plane with one of the axes degenerated is called a projection plane.

In step S1003, the approximate surface synthesis unit 2030 determines which of the above-mentioned axes to degenerate, i.e., which of the x-y plane, x-z plane, and y-z plane to use as the projection plane. The specific method of determining the projection plane is omitted because a publicly known method can be applied.

After determining the projection surface, the approximate surface synthesis unit 2030 proceeds to step S1004.

In step S1004, the approximate surface synthesis unit 2030 sorts the vertices projected onto the projection surface, for example, in counterclockwise order, and assigns an index to the vertices according to that order. Note that if sorting is performed during the processing of step S1003, the processing of step S1004 can be omitted by saving the results of the sorting processing.

After assigning the index, the approximate surface synthesis unit 2030 proceeds to step S1005.

In step S1005, the approximate surface synthesis unit 2030 generates triangles based on the above-mentioned index and the number of vertices present in the target node.

For example, the approximate surface synthesis unit 2030 can generate triangles by creating a table that defines which index to generate triangles from for each number of vertices, and referencing the table. A specific example of the table can be the one described in the above-mentioned non-patent document 1.

Alternatively, the approximate surface synthesis unit 2030 may first generate a centroid by averaging the coordinates of each vertex, and then generate a triangle using the two adjacent points in the index order described above and the centroid.

After generating the triangles, the approximate surface synthesis unit 2030 proceeds to step S1006.

In step S1006, the approximate surface synthesis unit 2030 generates points based on the triangles generated in step S1005. A specific method for this can be, for example, the method described in the above-mentioned non-patent document 1.

After point generation has been completed for all nodes, the approximate surface synthesis unit 2030 proceeds to step S1007.

In step S1007, the approximate surface synthesis unit 2030 determines whether the Trisoup node size is equal to the minimum node size.

If the Trisoup node size is equal to the minimum node size, the approximate surface synthesis unit 2030 proceeds to step S1001 and performs the next trisoup_depth process; otherwise, it proceeds to step S1008.

In step S1008, the approximate surface synthesis unit 2030 generates vertices at the minimum Trisoup node size based on the point cloud at the Trisoup node size generated in step S1006 or the triangle generated in step S1005.

Figure 11 shows an example of generating vertices at the minimum Trisoup node size from the point cloud at the Trisoup node size generated in step S1006.

Figure 11A shows an example of the decoded point group generated in step S1006.

First, the approximate surface synthesis unit 2030 divides the node into parts with the minimum Trisoup node size, as shown by the dotted lines in Figure 11A.

FIG. 11 shows a case where the Trisoup node size is N (=2 ^N ) and the minimum Trisoup node size is N-1 (=2 ^(N-1) ).

Second, the approximate surface synthesis unit 2030 checks whether each point in the decoded point cloud exists on each edge (solid and dotted lines in FIG. 11A) when divided for each minimum Trisoup node size.

For example, if it is assumed that each edge exists at 2 ^(N-1) intervals from an arbitrary origin, the coordinate of the edge in the z-axis direction can be expressed as (A x 2 ^(N-1) , B x 2 ^(N-1) , z), where A and B are constants.

Therefore, for example, if there is a point in the decoded point group whose coordinates are x = A x 2 ^(N-1) and y = B x 2 ^(N-1) , then that point can be considered to be on an edge. The above process can be similarly applied to edges in the x-axis direction and edges in the y-axis direction.

The approximate surface synthesis unit 2030 performs this process for all edges when divided into the minimum Trisoup node size, and can extract points that exist on the edges from the decoded point cloud.

Note that the above process is just one example, and other processes can be used as long as they can extract points on edges at the minimum Trisoup node size.

For example, if each edge is considered to exist at intervals of 2 ^(N-1) from an arbitrary origin, the approximate surface synthesis unit 2030 can extract points that exist on an edge in the z-axis direction by dividing the x-coordinate and y-coordinate of each point in the decoded point group by 2 ^(N-1) , and extracting points that have zero remainders in both the x-coordinate and y-coordinate as points that exist on an edge.

Figure 11B shows an example of the extraction result of points on edges. As shown in Figure 11B, the approximate surface synthesis unit 2030 may extract only points on edges that exist on the surface of the Trisoup node size, and may exclude points on edges that exist inside the Trisoup node size from the extraction target.

The approximate surface synthesis unit 2030 stores the points on the edges extracted in the above manner as vertices in the minimum Trisoup node size.

As described above, the approximate surface synthesis unit 2030 may be configured to perform a vertex decoding process at each node size between the maximum and minimum based on the maximum node size and minimum node size decoded from the control data, generate a vertex position at the minimum node size based on the decoded vertex if the node size is not the minimum node size, and, if there are multiple vertices on a certain edge that are generated from nodes of different node sizes adjacent to the edge, set the vertex generated by the node with the largest node size among the adjacent nodes as the vertex position of the edge.

This configuration eliminates gaps in the mesh even at boundaries of different node sizes, which is expected to improve the subjective quality of the decoded point cloud.

As described above, when generating vertex positions for the minimum node size, the system may be configured to generate vertices only on the surface of the node.

As mentioned above, by limiting the vertex positions to be generated only to node surfaces that may be adjacent to different node sizes, it is possible to avoid generating unnecessary vertices and prevent an increase in the amount of processing.

As described above, when the node size is not the minimum node size, the approximate surface synthesis unit 2030 may be configured to set, among the points in the decoded point group of the target node, the points on the edges when the target node is divided by the minimum node size as the vertex positions at the minimum node size.

With this configuration, it is possible to prevent an increase in the amount of processing by using a decoded point group that has already been generated for another purpose.

The above describes an example of a method for generating vertices based on a decoded point group at the Trisoup node size, but the vertices may also be generated by an interpolation method from vertices at the Trisoup node size, as shown in Figure 12.

Specifically, for example, first, the approximate surface synthesis unit 2030 calculates the equation of a straight line connecting adjacent vertices based on the result of sorting the vertices in step S1003 or step S1004.

Similar to FIG. 11A, FIG. 12A shows an example of dividing the Trisoup node into the minimum Trisoup node size, and an example of vertices at that Trisoup node size.

Figure 12A shows an example of generating straight lines connecting the 0th and 1st vertices, the 1st and 2nd vertices, and the 2nd and 0th vertices in the sorted index.

The line l connecting two points a = (ax, ay, az) and b = (bx, by, bz) can be expressed as l = (1-t)a + tb, for example.

The points on the edge can be calculated by finding the intersections between this line and the lines of each edge at the minimum Trisoup node size.

Here, as in Figure 11B, if the calculation target is limited to edges that exist on the surface at the Trisoup node size, the calculation can be simplified as follows.

First, when finding a line connecting two vertices, if both vertices are not on the same surface of the Trisoup node, they can be excluded from the calculation.

In the example of Figure 12A, the 0th and 1st vertices are on the same surface. Similarly, the 2nd and 0th vertices are on the same surface. On the other hand, the 1st and 2nd vertices are not on the same surface, so the line connecting these two points can be excluded from the calculation.

When two vertices are limited to being on the same surface, the equation of the line connecting the two vertices can be expressed as the equation of a line on a two-dimensional plane.

For example, the 0th vertex and the 1st vertex have the same z coordinate, so the line connecting the two vertices can be expressed as a line on the x-y plane in the form y = ax + b (a and b are constants).

On this x-y plane, when divided into the minimum Trisoup node size, the edge can be regarded as a straight line of x = C x 2 ^(N-1) or y = D x 2 ^(N-1) (C and D are constants), so the intersection point can be calculated by substituting x = C x 2 ^(N-1) or y = D x 2 ^(N-1) into the equation for the straight line above.

The intersection calculated in the above manner and the vertex at the Trisoup node size are both considered to be the vertices after interpolation.

Figure 12B shows an example of a vertex generated using the method described above.

As described above, when the node size is not the minimum node size, the approximate surface synthesis unit 2030 may be configured to determine the intersection of a straight line connecting two of the node vertices with a side when the node is divided by the minimum node size as the vertex position at the minimum node size.

By configuring it in this way, the intersection point of the two lines can be mathematically calculated uniquely, making it possible to accurately calculate the vertex position.

After vertex generation is complete, proceed to step S1001.

Figure 13 is a flowchart showing an example of the vertex decoding process used in step S1002.

As shown in FIG. 13, in step S1301, the approximate surface synthesis unit 2030 determines whether processing has been completed for all trisoup_depths.

If processing has been completed for all trisoup_depths, proceed to step S1305 and end processing. If processing has not been completed for all trisoup_depths, proceed to step S1302.

In step S1302, the approximate surface synthesis unit 2030 checks the number of unique segments belonging to the target Trisoup hierarchy.

If the number of unique segments is "0", i.e., if the target Trisoup hierarchy does not contain any Trisoup nodes, the approximate surface synthesis unit 2030 proceeds to step S1301 and moves on to processing the next Trisoup hierarchy.

If the number of unique segments is greater than "0", the approximate surface synthesis unit 2030 proceeds to step S1303.

In step S1303, the approximate surface synthesis unit 2030 decodes whether each unique segment contains a vertex to be used in the Trisoup process.

The number of vertices that can exist for each unique segment may be limited to one. In this case, the number of unique segments in which a vertex exists can be interpreted as being equal to the number of vertices.

After the approximate surface synthesis unit 2030 decodes the presence or absence of vertices for all unique segments in the target Trisoup hierarchy, it proceeds to step S1304.

In step S1304, the approximate surface synthesis unit 2030 decodes position information indicating where on each unique segment the vertex is located for each unique segment in which it was determined in step S1303 that a vertex exists.

Figure 14 is a flowchart showing an example of the processing of step S1304.

As shown in FIG. 14, in step S1401, the approximate surface synthesis unit 2030 determines whether processing has been completed for all segments.

If processing has been completed for all segments, proceed to step S1406 and end processing. If processing has not been completed for all segments, proceed to step S1402.

In step S1402, the approximate surface synthesis unit 2030 acquires the direction of the segment. Specifically, the approximate surface synthesis unit 2030 acquires whether the segment is oriented in the x-axis, y-axis, or z-axis direction.

For example, such information is obtained by assigning indices to edges (= segments) belonging to the node to be decoded in a predetermined order and recording the direction of the segments. In this process, each segment is processed in index order, and the direction of the segment can be determined from the record.

As another method, the approximate surface synthesis unit 2030 can determine which axis direction a vertex lies along by storing information on the coordinates of the start point and the coordinates of the end point for each segment. After obtaining the direction of the segment in this manner, the process proceeds to step S1403.

In step S1403, the approximate surface synthesis unit 2030 obtains the size of the segment.

The size of the segment corresponds to the node size at the depth of the target Octree.

When using only Octree, the size will be the same in the x, y, and z directions. On the other hand, when using QtBt, the values may be different in the x, y, and z directions.

The approximate surface synthesis unit 2030 obtains the size of the segment based on the node sizes in the x-axis, y-axis, and z-axis directions at the depth and the direction of the segment obtained in step S1402.

As an alternative method, if the approximate surface synthesis unit 2030 holds information on the coordinates of the start point and the end point for each segment, it can obtain the size of the segment by subtracting the coordinate value of the start point from the coordinate value of the end point.

After obtaining the size of the segment, the approximate surface synthesis unit 2030 proceeds to step S1404.

In step S1404, the approximate surface synthesis unit 2030 sets the size of the segment acquired in step S1403 as the maximum possible value for the Position of the vertex to be decoded. Then, the process proceeds to step S1405.

In step S1405, the approximate surface synthesis unit 2030 sets the range of possible values for Position to 0 to "the maximum value set in S1404" - 1, and then decodes Position.

For example, when the maximum value is 2 ^L , the approximate surface synthesis unit 2030 can set the maximum value to 2 ^L−1 . At this time, for example, Position may be encoded with an L-bit equal-length code (the point cloud decoding device 200 may decode on the premise that it is encoded in this way).

Furthermore, the approximate surface synthesis unit 2030 may set the range of possible values of Position to 0 to 2 ^L−1 , and then perform entropy coding/decoding using Huffman coding, arithmetic coding, etc. After decoding Position, the approximate surface synthesis unit 2030 proceeds to step S1401.

As described above, when decoding the vertex positions of Trisoup, the approximate surface synthesis unit 2030 may be configured to set a range of possible values for the vertex positions for each segment according to the node shape, and to decode the vertex positions based on the range of possible values for the vertex positions.

The approximate surface synthesis unit 2030 may also be configured to set the range of possible values for the vertex positions based on the axis direction of each segment when generating segments from nodes and the node shape, as described above.

The approximate surface synthesis unit 2030 may also be configured to set the range of possible values for the vertex positions based on the start and end coordinates of each segment, as described above.

This configuration makes it possible to apply Trisoup even when the node size is a rectangular prism rather than a cube, which is expected to improve coding efficiency.

(Point cloud encoding device 100)
Hereinafter, the point cloud encoding device 100 according to this embodiment will be described with reference to Fig. 15. Fig. 15 is a diagram showing an example of functional blocks of the point cloud encoding device 100 according to this embodiment.

As shown in FIG. 15, the point cloud encoding device 100 has a coordinate transformation unit 1010, a geometric information quantization unit 1020, a tree analysis unit 1030, an approximate surface analysis unit 1040, a geometric information encoding unit 1050, a geometric information reconstruction unit 1060, a color conversion unit 1070, an attribute transfer unit 1080, a RAHT unit 1090, an LoD calculation unit 1100, a lifting unit 1110, an attribute information quantization unit 1120, and an attribute information encoding unit 1130.

The coordinate conversion unit 1010 is configured to perform a conversion process from the three-dimensional coordinate system of the input point cloud to any different coordinate system. The coordinate conversion may, for example, convert the x, y, and z coordinates of the input point cloud into any s, t, and u coordinates by rotating the input point cloud. In addition, as one variation of the conversion, the coordinate system of the input point cloud may be used as is.

The geometric information quantization unit 1020 is configured to quantize the position information of the input point group after coordinate transformation and remove points with overlapping coordinates. Note that when the quantization step size is 1, the position information of the input point group and the position information after quantization match. In other words, when the quantization step size is 1, it is equivalent to not performing quantization.

The tree analysis unit 1030 is configured to receive position information of the quantized point group as input, and generate an occurrence code that indicates at which node in the encoding target space the point exists, based on the tree structure described below.

In this process, the tree analysis unit 1030 is configured to generate a tree structure by recursively dividing the encoding target space into rectangular parallelepipeds.

If a point exists within a certain rectangular parallelepiped, a tree structure can be generated by recursively dividing the rectangular parallelepiped into multiple rectangular parallelepipeds until the rectangular parallelepiped reaches a specified size. Each such rectangular parallelepiped is called a node. Each rectangular parallelepiped generated by dividing a node is called a child node, and the occurrence code is expressed as 0 or 1 to indicate whether or not a point is contained within a child node.

As described above, the tree analysis unit 1030 is configured to generate an occupancy code while recursively splitting the node until it reaches a predetermined size.

In this embodiment, a method called "Octree" can be used, which recursively performs octree division on the above-mentioned rectangular parallelepiped, always treating it as a cube, and a method called "QtBt" can be used, which performs quadtree division and binary tree division in addition to octree division.

Here, whether or not to use "QtBt" is transmitted to the point cloud decoding device 200 as control data.

Alternatively, predictive coding using an arbitrary tree structure may be specified. In such a case, the tree analysis unit 1030 determines the tree structure, and the determined tree structure is transmitted to the point cloud decoding device 200 as control data.

For example, the tree-structured control data may be configured so that it can be decoded using the procedure described in FIG. 6.

The approximate surface analysis unit 1040 is configured to generate approximate surface information using the tree information generated by the tree analysis unit 1030.

When decoding three-dimensional point cloud data of an object, for example, if the points are densely distributed on the object's surface, approximate surface information is used to represent the area in which the points exist by approximating the area using a small plane, rather than decoding each point individually.

Specifically, the approximate surface analysis unit 1040 may be configured to generate approximate surface information using, for example, a method called "Trisoup." In addition, when decoding a sparse point cloud acquired by Lidar or the like, this process can be omitted.

The geometric information encoding unit 1050 is configured to generate a bit stream (geometric information bit stream) by encoding syntax such as the occupancy code generated by the tree analysis unit 1030 and the approximate surface information generated by the approximate surface analysis unit 1040. Here, the bit stream may include, for example, the syntax described in Figures 4 and 5.

The encoding process is, for example, a context-adaptive binary arithmetic encoding process. Here, for example, the syntax includes control data (flags and parameters) for controlling the decoding process of the position information.

The geometric information reconstruction unit 1060 is configured to reconstruct the geometric information of each point of the point cloud data to be encoded (the coordinate system assumed by the encoding process, i.e., the position information after the coordinate transformation in the coordinate transformation unit 1010) based on the tree information generated by the tree analysis unit 1030 and the approximate surface information generated by the approximate surface analysis unit 1040.

The color conversion unit 1070 is configured to perform color conversion when the input attribute information is color information. Color conversion does not necessarily have to be performed, and whether or not the color conversion process is performed is coded as part of the control data and transmitted to the point cloud decoding device 200.

The attribute transfer unit 1080 is configured to correct the attribute values based on the position information of the input point cloud, the position information of the point cloud after reconstruction in the geometric information reconstruction unit 1060, and the attribute information after color change in the color conversion unit 1070, so as to minimize distortion of the attribute information.

The RAHT unit 1090 is configured to receive the attribute information transferred by the attribute transfer unit 1080 and the geometric information generated by the geometric information reconstruction unit 1060 as input, and to generate residual information for each point using a type of Haar transform called RAHT (Region Adaptive Hierarchical Transform). As a specific example of the RAHT process, the method described in the above-mentioned literature 2 can be used.

The LoD calculation unit 1100 is configured to receive the geometric information generated by the geometric information reconstruction unit 1060 as input and generate the LoD (Level of Detail).

LoD is information for defining a reference relationship (a referencing point and a referenced point) to realize predictive coding, such as predicting attribute information of a certain point from attribute information of another point and encoding or decoding the prediction residual.

In other words, LoD is information that defines a hierarchical structure in which each point contained in the geometric information is classified into multiple levels, and the attributes of points belonging to lower levels are encoded or decoded using attribute information of points belonging to higher levels.

As a specific method for determining LoD, for example, the method described in the above-mentioned literature 2 may be used.

The lifting unit 1110 is configured to generate residual information by a lifting process using the LoD generated by the LoD calculation unit 1100 and the attribute information after attribute transfer in the attribute transfer unit 1080.

Specific lifting processing may be, for example, the method described in the literature (Text of ISO/IEC 23090-9 DIS Geometry-based PCC, ISO/IEC JTC1/SC29/WG11 N19088).

The attribute information quantization unit 1120 is configured to quantize the residual information output from the RAHT unit 1090 or the lifting unit 1110. Here, a quantization step size of 1 is equivalent to no quantization being performed.

The attribute information encoding unit 1130 is configured to perform encoding processing using the quantized residual information, etc. output from the attribute information quantization unit 1120 as syntax, and generate a bit stream related to the attribute information (attribute information bit stream).

The encoding process is, for example, a context-adaptive binary arithmetic encoding process. Here, for example, the syntax includes control data (flags and parameters) for controlling the decoding process of the attribute information.

The point cloud encoding device 100 is configured to perform encoding processing using the position information and attribute information of each point in the point cloud as input, and to output a geometric information bit stream and an attribute information bit stream through the above processing.

Furthermore, the above-mentioned point cloud encoding device 100 and point cloud decoding device 200 may be realized as a program that causes a computer to execute each function (each process).

In each of the above embodiments, the present invention has been described using the point cloud encoding device 100 and the point cloud decoding device 200 as examples, but the present invention is not limited to such examples and can be similarly applied to a point cloud encoding/decoding system having the functions of the point cloud encoding device 100 and the point cloud decoding device 200.

In addition, according to this embodiment, for example, it is possible to realize an improvement in the overall service quality in video communication, which makes it possible to contribute to Goal 9 of the Sustainable Development Goals (SDGs) led by the United Nations, which is to "build resilient infrastructure, promote sustainable industrialization and foster innovation."

10... Point cloud processing system 100... Point cloud encoding device 1010... Coordinate transformation unit 1020... Geometric information quantization unit 1030... Tree analysis unit 1040... Approximate surface analysis unit 1050... Geometric information encoding unit 1060... Geometric information reconstruction unit 1070... Color conversion unit 1080... Attribute transfer unit 1090... RAHT unit 1100... LoD calculation unit 1110... Lifting unit 1120... Attribute Information quantization unit 1130...attribute information encoding unit 200...point cloud decoding device 2010...geometric information decoding unit 2020...tree synthesis unit 2030...approximate surface synthesis unit 2040...geometric information reconstruction unit 2050...inverse coordinate transformation unit 2060...attribute information decoding unit 2070...inverse quantization unit 2080...RAHT unit 2090...LoD calculation unit 2100...inverse lifting unit 2110...inverse color transformation unit

Claims (6)

A point cloud decoding device, comprising:
An approximate surface synthesis unit is provided,
The approximate surface synthesis unit includes:
Based on the maximum node size and the minimum node size decoded from the control data, a decode process is performed for the vertices at each node size between the maximum and minimum;
if the node size is not the minimum node size, generating vertex positions at the minimum node size based on the decoded vertices;
A point cloud decoding device characterized in that, when there are multiple vertices on a certain edge generated from nodes of different node sizes adjacent to the certain edge, the vertex generated by the node with the largest node size among the different adjacent nodes is configured to be the vertex position of the certain edge. The point cloud decoding device according to claim 1, characterized in that the approximate surface synthesis unit is configured to generate vertices only on the surface of the node when generating the vertex positions for the minimum node size. The point cloud decoding device according to claim 1 or 2, characterized in that, when the node size is not the minimum node size, the approximate surface synthesis unit is configured to set, among the points in the decoded point cloud of the node to be decoded, the points on the edges when the node to be decoded is divided by the minimum node size, as the vertex positions at the minimum node size. The point cloud decoding device according to claim 1 or 2, characterized in that, when the node size is not the minimum node size, the approximate surface synthesis unit is configured to determine the intersection of a straight line connecting two of the vertices of the node to be decoded and an edge obtained by dividing the node to be decoded by the minimum node size as the vertex position at the minimum node size. 1. A point cloud decoding method, comprising:
performing a decoding process for vertices at each node size between the maximum and minimum based on the maximum and minimum node sizes decoded from the control data;
if the node size is not the minimum node size, generating a vertex position at the minimum node size based on the decoded vertex;
A point cloud decoding method characterized by comprising a step of, when there are multiple vertices on a certain edge generated from nodes of different node sizes adjacent to the certain edge, setting the vertex generated by the node with the largest node size among the different adjacent nodes as the vertex position of the certain edge. A program for causing a computer to function as a point group decoding device,
The point group decoding device comprises:
An approximate surface synthesis unit is provided,
The approximate surface synthesis unit includes:
Based on the maximum node size and the minimum node size decoded from the control data, a decode process is performed for the vertices at each node size between the maximum and minimum;
if the node size is not the minimum node size, generating vertex positions at the minimum node size based on the decoded vertices;
A program characterized by being configured such that, when there are multiple vertices on a certain edge generated from nodes of different node sizes adjacent to the certain edge, the vertex generated by the node with the largest node size among the different adjacent nodes is set as the vertex position of the certain edge.

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