CN112565795B - Point cloud geometric information encoding and decoding method - Google Patents
Point cloud geometric information encoding and decoding method Download PDFInfo
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Abstract
The invention provides a point cloud geometric information coding and decoding method, which comprises the steps of obtaining a current octree partitioning model of point cloud geometric information; aiming at a sub-node layer to be coded in a current octree partitioning model, determining nodes at the edges of two nodes which are away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node to be coded and the sub-node to be coded as target nodes, acquiring the occupation situation of the target nodes, and determining a context model of the occupation code of the sub-node to be coded according to the occupation situation of the target nodes; and entropy coding the placeholder codes of the sub-nodes to be coded after the context model is determined to obtain a binary code stream. Compared with the prior art, the point cloud geometric information coding method provided by the embodiment of the invention can improve the coding performance of the point cloud geometric information by utilizing the geometric correlation between point cloud points in adjacent spaces.
Description
Technical Field
The invention belongs to the technical field of coding, and particularly relates to a point cloud geometric information coding and decoding method.
Background
With the continuous development of point cloud technology, the compression and encoding of point cloud data becomes an important research problem. At present, the Standard working Group (AVS) of the domestic digital Audio and Video coding Standard (Standard) of China and the Moving Picture Experts Group (MPEG) in the International organization for standardization both make the Standard of point cloud coding. At present, as shown in fig. 1, the point cloud encoding framework of AVS performs coordinate transformation on geometric information to make all point clouds contained in a cube bounding box. And then, quantizing, wherein the step of quantizing mainly plays a role of scaling, as the geometric information of a part of points is the same due to quantization rounding, whether to remove the repeated points is determined according to parameters, and the process of quantizing and removing the repeated points is also called voxelization. The bounding box is called a root node, the bounding box is divided into eight equal parts by an octree division of the root node, namely the bounding box is divided into 8 subcubes, each subcube is called a child node of the root node, the eight child nodes are respectively represented by 1 bit whether the eight child nodes are occupied or not, namely whether points in the point cloud are contained or not, 0 is represented by 0, 1 is represented by child nodes which are occupied, the bit is called a occupied bit code, and the binary code stream is generated through entropy coding. And (3) continuing to carry out octree division on the non-empty (including the points in the point cloud) child nodes until the leaf nodes obtained by division are a unit cube of 1x1x1, wherein the whole process adopts a traversal sequence with a priority in breadth. And after the division is finished, coding the points in the unit cube, namely the leaf nodes to generate a binary code stream. And reconstructing the geometric information after the geometric coding is finished. The encoding of the attribute information is mainly performed for color information. First, color information is converted from an RGB color space to a YUV color space. The point cloud is then recolored with the reconstructed geometric information such that the unencoded attribute information corresponds to the reconstructed geometric information. In color information coding, after dot clouds are sequenced by Morton codes, the nearest neighbor of a point to be predicted is searched by using a geometric spatial relationship, interpolation prediction is carried out on the point to be predicted by using a reconstruction attribute value of the found neighbor to obtain a prediction attribute value, then difference is carried out on a real attribute value and the prediction attribute value to obtain a prediction residual error, and finally quantization and coding are carried out on the prediction residual error to generate a binary code stream. Decoding process as shown in fig. 2, the decoding process is reciprocal to the encoding process.
In the existing AVS point cloud coding framework, a context-based adaptive binary arithmetic coder is adopted for coding the space occupation code. The two sets of context models are respectively used for the sparse point cloud sequence and the dense point cloud sequence.
In the application of the method to a sparse point cloud sequence, the spatial positions of eight child nodes generated by the coordinate system and the octree division relative to the parent node, i.e. the current node, are shown in fig. 3 below. Under the breadth-first division mode, when the current node codes 8-bit space occupation code, the neighbor reference information of the same layer can be obtained and comprises occupation information of neighbor sub-nodes in the left direction, the front direction and the lower direction (the negative direction of each coordinate axis). As shown in fig. 4, for the child nodes at different positions in the current node, 3 co-planar, 3 collinear and 1 co-vertex neighbors of the same layer are found as the reference node. For the sub-node to be coded, the occupation situation of 7 reference neighbor nodes on the same layer is 2 7 =128 cases of (a) short-term,each of these 128 cases is assigned 1 context, and the same-layer reference neighbor nodes of the child nodes have 128 contexts in total. If none of the same-level reference neighbor nodes as the child node to be encoded are occupied, then the coplanar neighbors of the current node level are considered. As shown in FIG. 5, 3 coplanar neighbors in the right, upper and rear directions (positive directions of each coordinate axis) of the current node are taken as reference nodes, and 2 coplanar neighbors in the right, upper and rear directions (positive directions of each coordinate axis) of the current node are taken as reference nodes 3 If 1 context is allocated for each case, 8 contexts are shared by the reference neighbor nodes of the current node level for each child node. Since the position of each child node to be encoded in the current node is different, and the influence of the coplanar neighbors of the current node on each child node to be encoded is also different, the reference neighbor nodes of the current node layer have 8 × 8=64 contexts in total in consideration of the position of each child node in the current node. In the case of the integrated child node level and the current node level, the set of context models provides 128-1+64=191 contexts in total.
When applied to the dense point cloud sequence, the spatial positions of eight child nodes generated by the coordinate system and the octree partition with respect to the parent node, i.e., the current node, are shown in fig. 3 below. The set of contexts is configured using two layers of context references. The first layer is the occupation condition of the neighboring nodes which are coplanar and collinear with the current node in the current node layer; the second layer is the occupation condition of the neighbor nodes coplanar with the child nodes to be coded in the child node layer to be coded, and the process is as follows:
first, for each child node to be encoded, 6 current node layer neighbors coplanar and collinear with the parent node layer, that is, the current node layer, can be obtained as shown in fig. 6 below. Fig. 6 includes the current node, each child node to be encoded, and the coplanar and collinear neighbor nodes of the current node. For 3 coplanar neighbors, considering each distribution case, there are 2 3 =8 cases; for the remaining 3 collinear neighbors, only the number of occupied nodes in the three neighbors is calculated, and there are four cases of 0, 1, 2 and 3. The two cases are combined to have 4 × 8=32 cases, 1 context is configured for each case, and the current node layer provides 32 cases altogetherA context. Secondly, for each sub-node to be coded, 3 coplanar neighbor nodes on the same layer as the sub-node to be coded, namely, the left, the front and the lower (in the negative direction of each coordinate axis) nodes are searched as reference nodes, as shown in fig. 7, the total number of the 3 coplanar neighbor nodes on the same layer as the sub-node to be coded in fig. 7 is 2 3 If there are 8 cases, one context is allocated for each case, the current node provides a total of 8 contexts. The two layers of contexts do not interfere with each other, so the set of context models provides a total of 32 × 8=256 contexts for dense point cloud sequences.
In summary, in the prior art, no matter the point cloud sequence is dense or sparse, only the neighbor directly adjacent to the current sub-node to be encoded is considered by the reference node in the sub-node layer to be encoded in the context model, and the neighbor not directly adjacent to the current sub-node to be encoded is ignored. In reality, the bounding box may be a regular shape or an irregular shape, that is, the lengths of three sides of the bounding box are equal or different, and the length of the upper side of the Z dimension may be smaller than the lengths of the sides of the X dimension and the Y dimension in the octree division, so that the relevance between the neighbor node selected in the prior art and the current node may be low, and thus the performance of the scheme based on context model entropy coding in the prior art is low.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a point cloud geometric information encoding and decoding method. The technical problem to be solved by the invention is realized by the following technical scheme:
in a first aspect, the invention provides a method for encoding point cloud geometric information, including:
acquiring a current octree partitioning model of point cloud geometric information;
aiming at a sub-node layer to be coded in the current octree partitioning model, determining nodes at the positions of two node edges away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node to be coded and the sub-node to be coded as target nodes, and acquiring the occupation situation of the target nodes;
determining a context model of a bit occupying code of a child node to be coded according to the bit occupying situation of a target node;
and entropy coding the placeholder codes of the child nodes to be coded after the context model is determined to obtain a binary code stream.
Optionally, the step of determining the context model of the bit-occupying code of the child node to be encoded according to the bit-occupying condition of the target node includes:
judging whether the occupation situation of each target node is unoccupied or not;
when the occupation situation of the target nodes except the common vertex is occupied, determining a context model according to the occupation situation;
when there is an occupancy of each target node other than the common vertex as unoccupied, a context model is determined based on the occupancy of the first neighboring node of the common vertex.
Optionally, after the step of determining the context model based on the occupancy of the first neighboring node of the common vertex when the occupancy of each target node except the common vertex is not occupied, the point cloud geometric information encoding method further includes:
judging whether the occupation situation of each first neighbor node is occupied or not;
if the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situation of a second neighbor node which is coplanar after the upper right of the current node layer;
wherein, the left front-lower direction is the negative direction of each dimension coordinate axis, and the right upper-rear direction is the positive direction of each dimension coordinate axis; the current node is a father node of a child node to be coded;
judging whether the occupation situation of the second neighbor node is occupied or not;
when the occupation situation of a second neighbor node is occupied, acquiring the spatial position of the child node to be coded relative to the current node;
determining a context model based on the spatial position and the occupation condition of the second neighbor node;
and entropy coding the placeholders of the child nodes to be coded based on the context model.
Optionally, after the step of determining whether the occupancy status of the second neighboring node is occupied, the point cloud geometric information encoding method further includes:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a coplanar third neighbor node at the left front part and the lower part of the current node;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
if the occupation situation of the fourth neighbor node is occupied, determining a context model based on the occupation situation of the fourth neighbor node;
if the occupation situation of the fourth neighbor node is unoccupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is non-occupation, determining the context model.
Optionally, after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometric information encoding method further includes:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and a fourth neighbor node which is collinear at the upper right and the rear;
if the occupancy condition of the third neighbor node is occupied or the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the occupied neighbor node;
if the occupation situation of the third neighbor node is that the third neighbor node is not occupied and the occupation situation of the fourth neighbor node is not occupied, acquiring the occupation situation of a fifth neighbor node collinear at the left, front and lower parts of the current node;
if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is not occupied, determining the context model.
Optionally, after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometric information encoding method further includes:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and the occupation situation of a fourth neighbor node which is collinear at the upper right and the rear;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situations of other collinear neighbor nodes except the left front lower collinear neighbor node of the current node;
and if the occupancy of other collinear neighbor nodes exists, determining the context model based on the occupancy of the occupied neighbor nodes.
In a second aspect, the present invention provides a method for decoding geometric information of a point cloud, including:
receiving a binary code stream;
aiming at a code word to be decoded in a binary code stream, determining the code word as a placeholder of a child node to be decoded;
acquiring a current octree partitioning model of point cloud geometric information;
for the sub-node layer to be decoded in the current octree partitioning model, determining nodes at the positions of two node edges away from the sub-node to be decoded as target nodes in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node to be decoded and the sub-node to be decoded, and acquiring the occupation situation of the target nodes;
determining a context model of a bit occupying code of a child node to be decoded according to the bit occupying situation of the target node;
and entropy decoding the placeholders of the child nodes to be decoded after the context model is determined.
Optionally, the step of determining the context model of the child node to be decoded according to the occupancy of the target node includes:
judging whether the occupation situation of each target node is unoccupied;
if the occupation situation of the target nodes except the common vertex is occupied, determining a context model according to the occupation situation;
and if the occupation situation of each target node except the common vertex is not occupied, determining the context model based on the occupation situation of the first neighbor node of the common vertex.
Optionally, after the step of determining a context model based on the occupancy of the first neighboring node of the common vertex when the occupancy of each target node except the common vertex is not occupied, the point cloud geometric information decoding method further includes:
judging whether the occupation situation of each first neighbor node is occupied or not;
wherein, the left front-lower direction is the negative direction of each dimension coordinate axis, and the right upper-rear direction is the positive direction of each dimension coordinate axis;
when the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situations of 3 coplanar second neighbor nodes which are coplanar after the upper right of the current node layer;
the current node is a father node of a child node to be decoded;
judging whether the occupation situation of the second neighbor node is occupied or not;
if the occupation situation of the second neighbor node is occupied, acquiring the spatial position of the child node to be decoded relative to the current node;
determining a context model based on the spatial position and the occupation condition of the second neighbor node;
and entropy decoding the placeholders of the child nodes to be decoded based on the context model.
Optionally, after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometric information decoding method further includes:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a coplanar third neighbor node at the left front part and the lower part of the current node;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
if the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the fourth neighbor node;
if the occupation situation of the fourth neighbor node is unoccupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is not occupied, determining the context model.
The embodiment of the invention provides a point cloud geometric information coding and decoding method, which comprises the steps of obtaining a current octree partitioning model of point cloud geometric information; aiming at a sub-node layer to be coded in a current octree partitioning model, determining nodes at the edges of two nodes which are away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node to be coded and the sub-node to be coded as target nodes, acquiring the occupation situation of the target nodes, and determining a context model of the occupation code of the sub-node to be coded according to the occupation situation of the target nodes; and entropy coding the placeholder codes of the sub-nodes to be coded after the context model is determined to obtain a binary code stream. Compared with the prior art, the point cloud geometric information coding method provided by the embodiment of the invention can improve the coding performance of the point cloud geometric information by utilizing the geometric correlation between point cloud points in adjacent spaces.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Drawings
Figure 1 is a flow diagram of an AVS encoding process;
FIG. 2 is a schematic diagram of an AVS decoding process;
FIG. 3 is a schematic diagram of the spatial positions and coordinate systems of eight child nodes relative to a current node;
FIG. 4 is a schematic diagram of the spatial positions and coordinate systems of eight child nodes relative to a current node;
FIG. 5 is a schematic diagram of a current node level coplanar neighbor node;
FIG. 6 is a schematic diagram of a reference neighbor node of a parent node layer of each child node;
FIG. 7 is a schematic diagram of the same-layer co-planar neighbors of each child node;
fig. 8 is a flowchart of a point cloud geometric information encoding method according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of possible octree partitioning and non-coplanar, collinear, and common vertex reference nodes according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of four sets of neighbor nodes of a current node layer according to an embodiment of the present invention;
fig. 11 is a schematic diagram of a peer reference node of each child node according to an embodiment of the present invention;
fig. 12 is a flowchart of a point cloud geometric information decoding method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
As shown in fig. 8, a method for encoding point cloud geometric information according to an embodiment of the present invention includes:
s1a, acquiring a current octree partitioning model of point cloud geometric information;
it can be understood that, in the current octree partitioning model, each child node to be coded will be in the same coordinate system, and the above partitioning process is performed on the same coordinate system.
S2a, aiming at a sub-node layer to be coded in the current octree partitioning model, determining nodes at the edges of two nodes which are away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the adjacent nodes at the same layer as the sub-node to be coded and the sub-node to be coded as target nodes, and acquiring the occupation situation of the target nodes;
the number of nodes with the side length of two nodes away from a child node to be coded in the negative direction is 3, so that the target node is also 3, and a node with the shortest side length of the node and the negative bit position of the dimension and the length of the two node sides is selected from the 3 nodes to determine a context model; the neighbor node includes: the left front lower direction of the child node to be coded is one or a combination of 3 coplanar nodes, 3 collinear nodes and 1 common vertex node.
S3a, determining a context model of a bit occupying code of a child node to be coded according to the bit occupying condition of the target node;
and S4a, entropy coding is carried out on the placeholder codes of the sub-nodes to be coded after the context model is determined, and a binary code stream is obtained.
If the octree of the point cloud geometric information is divided in a breadth-first division mode, when the space occupying codes of all the sub nodes in the current node are coded, the 8-bit space occupying codes of the nodes on the same layer in the left, front and lower 3 directions (negative directions of all coordinate axes) of the current node can be obtained. In the sub-node layer to be coded, the nodes with strong correlation with the sub-nodes to be coded not only have 3 coplanar nodes, 3 collinear nodes and 1 common vertex neighbor nodes in the left, front and lower 3 directions, but also have sub-node layer nodes at the positions of two sub-node edges along the negative direction of an arbitrary axis. Especially, when the side lengths of the nodes are not equal, the correlation between the node in the dimension with the smallest side length and some neighboring nodes in the other dimensions is stronger, as shown in fig. 9 below.
The left graph of fig. 9 is an octree partition in theoretical analysis, and the side lengths in three dimensions of the nodes are considered to be equal, but when the side lengths of three sides of the bounding box are not equal, the octree partition may occur in the case of the graph in fig. 9, that is, the side length in the Z dimension is smaller than the side lengths in the X dimension and the Y dimension. At this time, the dotted frame node in the right diagram of fig. 9 is the current node, the innermost light gray node is a sub-node to be encoded, and the light gray nodes located on some surfaces and the sub-node to be encoded are neither coplanar nor collinear, and share a vertex, and are sub-node layer nodes starting from the sub-node to be encoded and offset along the negative direction of the Z axis by the edges of two sub-nodes. It can be seen from the figure that the distances between some light gray nodes on the surface and the child nodes to be coded are very close, and the correlation is still very strong. Considering this node together with the placeholders for 3 co-planar, 3 co-linear, 1 co-vertex neighbors of the sub-node layer, one context is provided for each placeholder.
The embodiment of the invention provides a point cloud geometric information coding method, which comprises the steps of obtaining a current octree partitioning model of point cloud geometric information; aiming at a sub-node layer to be coded in a current octree partitioning model, determining nodes at the edges of two nodes which are away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node to be coded and the sub-node to be coded as target nodes, acquiring the occupation situation of the target nodes, and determining a context model of the occupation code of the sub-node to be coded according to the occupation situation of the target nodes; and entropy coding the placeholder codes of the child nodes to be coded after the context model is determined to obtain a binary code stream. Compared with the prior art, the point cloud geometric information coding method provided by the embodiment of the invention can improve the coding performance of the point cloud geometric information by utilizing the geometric correlation between point cloud points in adjacent spaces.
Example two
As an optional embodiment of the present invention, the step of determining the context model of the bit-occupying code of the child node to be encoded according to the bit-occupying condition of the target node includes:
s41, judging whether the occupation situation of each target node is unoccupied;
s42, if the occupation situation of the target nodes except the common vertex is occupied, determining a context model according to the occupation situation;
the current node is a father node of a child node to be decoded;
s43, if the occupation situation of each target node except the common vertex is not occupied, determining a context model based on the occupation situation of the first neighbor node of the common vertex.
The first neighbor node is a neighbor node which shares a vertex with the child node to be coded.
Referring to fig. 11, the virtual frame node is a current node, the solid node is a sub-node to be encoded, and the solid frame node is a reference node on the same layer as the sub-node to be encoded. If the side lengths of the three dimensions of the node are the same, the node at the positions of the two node sides in the negative direction of the X dimension is selected by default. The 8 sub-node layers to be coded are adjacent, the common vertex neighbor is far away from the sub-node to be coded, and the correlation between the common vertex neighbor and the sub-node to be coded is not as good as that between the common vertex neighbor and the sub-node to be coded, so that special consideration is given to the common vertex neighbor and the sub-node to be coded. For the remaining 7 nodes, if not all are not occupied, there are 2^7-1=127 cases, allocate 1 context for each case; if all of the 7 nodes are unoccupied, the occupation situation of the common vertex neighbor node is considered. There are 2 possibilities for this co-vertex neighbor: occupied or unoccupied. And independently allocating 1 context for the occupied situation of the common vertex neighbor node, and if the common vertex neighbor node is not occupied, considering the occupied situation of the current node layer neighbor to be described next, namely 127+2-1=128 contexts corresponding to the child node layer neighbors to be coded.
EXAMPLE III
As an optional embodiment of the present invention, when the occupation situation of each target node except for the common vertex is not occupied, the context model is determined based on the occupation situation of the first neighboring node of the common vertex, and the method for encoding point cloud geometric information provided in the embodiment of the present invention further includes:
step a: judging whether the occupation situation of each first neighbor node is occupied or not;
step b: if the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situation of a second neighbor node which is coplanar after the upper right of the current node layer;
the current node is a father node of a child node to be coded;
step c: judging whether the occupation situation of the second neighbor node is occupied or not;
and the second neighbor node is 3 coplanar neighbor nodes which are coplanar after the upper right of the current node layer.
Step d: when the occupation situation of a second neighbor node is occupied, acquiring the spatial position of the child node to be coded relative to the current node;
step e: determining a context model based on the spatial position and the occupation condition of the second neighbor node;
step f: and entropy coding the placeholders of the child nodes to be coded based on the context model.
Example four
As an optional embodiment of the present invention, after the step of determining whether the occupancy status of the second neighboring node is occupied, the method for encoding geometric information of a point cloud provided in the embodiment of the present invention further includes:
step a: if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node coplanar at the left front lower part of the current node;
wherein, the left front-lower direction is the negative direction of each dimensional coordinate axis, and the right upper-rear direction is the positive direction of each dimensional coordinate axis; the third neighbor node is a coplanar neighbor node at the left, front and bottom of the current node layer.
Step b: if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
step c: if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
and the fourth neighbor node is a collinear neighbor node at the upper right of the current node layer.
Step d: if the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the fourth neighbor node;
step e: if the occupation situation of the fourth neighbor node is unoccupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
step f: if the occupation situation of the fifth neighbor node exists, determining a context model based on the occupation situation of the fifth neighbor node;
and the fifth neighbor node is a collinear neighbor node at the left, the front and the bottom of the current node layer.
Step g: and if the occupation condition of the fifth neighbor node is non-occupation, determining the context model.
For the current node layer, more neighbor node information can be acquired, and the distance information between the node which is closest to and occupied by the current node and the current node in the nodes is considered. In the neighbors of the current node layer, four groups of neighbor nodes, namely 3 coplanar nodes occupied in the upper right and rear direction (positive directions of all coordinate axes), 3 coplanar nodes occupied in the front left and lower direction (negative directions of all coordinate axes), 3 collinear nodes occupied in the upper right and rear direction (positive directions of all coordinate axes) and 3 collinear nodes occupied in the front left and lower direction (negative directions of all coordinate axes), are selected. As shown in fig. 10, the virtual border node is the current node, and the solid border node is 3 neighbor nodes in each group of neighbors. Among the four sets of neighbors, the nearest neighbors to the current node are two sets of co-planar neighbors followed by two sets of co-linear neighbors. However, considering that the neighbors found by the child node layer all come from the left-front-lower direction, there is a certain redundancy with the information provided by the left-front-lower direction neighbors of the current node layer, so the distances of the four groups of neighbors of the current node layer are ordered as:
right top rear direction 3 coplanar neighbors < left front lower direction 3 coplanar neighbors < right top rear direction 3 collinear neighbors < left front lower direction 3 collinear neighbors.
If none of the 8 peer reference nodes of the child node to be encoded is occupied, then consider the occupancy of four sets of neighbors of the current node layer as shown in FIG. 9. For the current node layer, the context is found according to the following steps:
first consider the top right 3 co-planar neighbors of the current node. The occupation situation of 3 co-planar neighbors behind the upper right of the current node is 2 3 =8 possibilities, each allocation for cases where not all are unoccupiedOne context, considering the position of the current node of the child node to be encoded, the set of neighbor nodes provides (8-1) × 7=56 contexts. If none of the top right 3 co-planar neighbors of the current point are occupied, then the remaining three groups of neighbors of the current node layer continue to be considered.
Secondly, considering the distance between the node occupied most recently and the current node, the correspondence between the distribution of specific neighbor nodes and the distance is shown in table 1. As can be seen from table 1, the distance has four values, 1 context is allocated to each of the four values, and then 4 × 8=32 contexts are total in consideration of the position of the current node of the child node to be encoded.
So far, the context model on the present sleeve is allocated 128+56+32=216 contexts in total.
TABLE 1 correspondence between current node level occupancy and distance
| Current node level occupancy | Distance between two adjacent plates |
| Left front lower coplanar neighbor occupancy | 1 |
| Left front lower coplanar neighbors do not occupy and right upper rear collinear neighbors occupy | 2 |
| The left front lower coplanar neighbor and the right upper rear collinear neighbor do not occupy and the left front lower collinear neighbor occupies | 3 |
| Four groups of neighbors of the current node layer do not occupy | 4 |
EXAMPLE five
As an optional embodiment of the present invention, after the step of determining whether the occupation situation of the 3 coplanar neighbor nodes that are coplanar after the upper right and the back of the current node layer are occupied, the point cloud geometric information encoding method provided in the embodiment of the present invention further includes:
step a: if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and a fourth neighbor node which is collinear at the upper right and the rear;
step b: if the occupancy condition of the third neighbor node is occupied or the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the occupied neighbor node;
step c: if the occupation situation of the third neighbor node is that the third neighbor node is not occupied and the occupation situation of the fourth neighbor node is not occupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
step d: when the occupation situation of the fifth neighbor node exists and is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
step e: when the occupancy of the fifth neighboring node is unoccupied, the context model is determined.
It can be understood that the corresponding relationship between the current node layer occupancy and the distance may be modified as shown in table 2 below:
TABLE 2 correspondence between current node level occupancy and distance
| Current node level occupancy | Distance between two adjacent devices |
| Left-front-lower coplanar neighbor occupancy or right-upper-rear collinear neighbor occupancy | 1 |
| The left front lower coplanar neighbor and the right upper rear collinear neighbor do not occupy and the left front lower collinear neighbor occupies | 2 |
| Four groups of neighbors of the current node layer do not occupy | 3 |
EXAMPLE six
As an optional embodiment of the present invention, after the step of determining whether the occupation situation of the 3 coplanar neighbor nodes that are coplanar after the upper right of the current node layer is occupied, the point cloud geometric information encoding method provided in the embodiment of the present invention further includes:
a, step a: if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and the occupation situation of a fourth neighbor node which is collinear at the upper right and the rear;
step b: if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
step c: if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situations of other collinear neighbor nodes except the left front lower collinear neighbor node of the current node;
step d: and if the occupancy of other collinear neighbor nodes exists, determining the context model based on the occupancy of the occupied neighbor nodes.
The top right co-linear neighbor among the four sets of neighbors of the current node layer shown in fig. 10 may be replaced with the remaining neighbors that are co-linear with the current node. There are a total of 12 neighbor nodes co-linear with the current node, where the nodes redundant to the level of the child node to be encoded are the 3 co-linear neighbors at the bottom left in FIG. 10. Removing these 3 neighbors, i.e., the remaining 9 neighbors, can optionally replace one or more of the top-right co-linear neighbors in fig. 10.
The effect of the point cloud geometric information encoding method provided by the invention is verified according to the experimental result.
The context model provided by the invention can better utilize the geometric correlation in the adjacent space, and further improve the geometric coding performance. Experimental results show that the coding performance can be improved by using the algorithm described by the technology, and as shown in the following table 3, the BD-GeomRate performance of the reconstructed point cloud becomes good under lossy conditions. ( PSNR is an objective standard for image evaluation, and the larger PSNR, the better the image quality. The BD-GeomRate is a parameter for measuring the performance of geometric information coding, and the BD-GeomRate shows that the performance is better when being negative, and on the basis, the absolute value of the BD-GeomRate is larger, the performance gain is larger. )
TABLE 3 comparison of the performance of the method with that of the existing AVS point cloud coding technique under lossy conditions
EXAMPLE seven
As shown in fig. 12, a method for decoding point cloud geometric information provided in the embodiment of the present invention includes:
s1b, receiving a binary code stream;
s2b, aiming at the code word to be decoded in the binary code stream, determining the code word as the placeholder of the child node to be decoded;
s3b, acquiring a current octree partitioning model of the point cloud geometric information;
s4b, aiming at a sub-node layer to be decoded in the current octree partitioning model, determining nodes at the edges of two nodes away from the sub-node to be decoded in the negative direction of each dimension of the same coordinate system as the adjacent nodes at the same layer as the sub-node to be decoded and the sub-node to be decoded as target nodes, and acquiring the occupation situation of the target nodes;
s5b, determining a context model of the bit occupying code of the child node to be decoded according to the bit occupying situation of the target node;
and S6b, entropy decoding the placeholder of the child node to be decoded after the context model is determined.
Example eight
As an optional embodiment of the present invention, the step of determining the context model of the child node to be decoded according to the occupancy of the target node includes:
judging whether the occupation situation of each target node is unoccupied or not;
when the occupation situation of the target nodes except the common vertex is occupied, determining a context model according to the occupation situation;
when the occupancy of each target node except the common vertex is not occupied, determining the context model based on the occupancy of the first neighbor node of the common vertex.
Example nine
As an optional embodiment of the present invention, after the step of determining the context model based on the occupancy of the first neighboring node that shares the vertex if the occupancy of each target node except the shared vertex is not occupied, the method for decoding point cloud geometric information provided in the embodiment of the present invention further includes:
step a: judging whether the occupation situation of each first neighbor node is occupied or not;
step b: when the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situations of 3 coplanar second neighbor nodes which are coplanar after the upper right of the current node layer;
the current node is a father node of a child node to be decoded;
step c: judging whether the occupation situation of the second neighbor node is occupied or not;
step d: when the occupation situation of the second neighbor exists, acquiring the spatial position of the child node to be decoded relative to the current node;
step e: determining a context model based on the spatial position and the occupation condition of the second neighbor node;
step f: and entropy decoding the placeholders of the child nodes to be decoded based on the context model.
Example ten
As an optional embodiment of the present invention, after the step of determining whether the occupancy status of the second neighboring node is occupied, the point cloud geometric information decoding method provided in the embodiment of the present invention further includes:
step a: if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node coplanar with the left front and the lower left of the current node;
wherein, the left front-lower direction is the negative direction of each dimension coordinate axis, and the right upper-rear direction is the positive direction of each dimension coordinate axis;
step b: if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
step c: if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
step d: if the occupation situation of the fourth neighbor node is occupied, determining a context model based on the occupation situation of the fourth neighbor node;
step e: if the occupation situation of the fourth neighbor node is unoccupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
step f: if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
step g: and if the occupation condition of the fifth neighbor node is non-occupation, determining the context model.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A point cloud geometric information encoding method is characterized by comprising the following steps:
acquiring a current octree partitioning model of point cloud geometric information;
aiming at a sub-node layer to be coded in the current octree partitioning model, nodes at the positions of the adjacent nodes at the same layer as the sub-node to be coded and the edges of two sub-nodes which are away from the sub-node to be coded in the negative direction of each dimension of the same coordinate system as the sub-node to be coded are determined as target nodes, and the occupation condition of the target nodes is obtained;
determining a context model of a bit occupying code of a child node to be coded according to the bit occupying situation of a target node;
and entropy coding the placeholder codes of the child nodes to be coded after the context model is determined to obtain a binary code stream.
2. The point cloud geometric information encoding method according to claim 1, wherein the step of determining the context model of the sub-node placeholder code to be encoded according to the placeholder of the target node comprises:
judging whether the occupation situation of each target node is unoccupied;
when the occupation situation of the target node except the first neighbor node is occupied, determining a context model according to the occupation situation;
when the occupation situation of each target node except the first neighbor node is not occupied, determining a context model based on the occupation situation of the first neighbor node sharing the vertex;
the first neighbor node is a neighbor node which shares a vertex with the child node to be coded.
3. The point cloud geometry information encoding method of claim 2, wherein after the step of determining a context model based on an occupancy of a first neighboring node of the common vertex when there is an occupancy of each target node other than the common vertex as unoccupied, the point cloud geometry information encoding method further comprises:
judging whether the occupation situation of each first neighbor node is occupied or not;
if the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situation of a second neighbor node which is coplanar after the upper right of the current node layer;
wherein, the left front-lower direction is the negative direction of each dimensional coordinate axis, and the right upper-rear direction is the positive direction of each dimensional coordinate axis; the current node is a father node of a child node to be coded;
judging whether the occupation situation of the second neighbor node is occupied or not;
when the occupation situation of the second neighbor node is occupied, acquiring the spatial position of the child node to be coded relative to the current node;
determining a context model based on the spatial position and the occupation condition of the second neighbor node;
and entropy coding the placeholders of the child nodes to be coded based on the context model.
4. The point cloud geometry information encoding method according to claim 3, wherein after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometry information encoding method further comprises:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node coplanar at the left front lower part of the current node;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
if the occupation situation of the fourth neighbor node is occupied, determining a context model based on the occupation situation of the fourth neighbor node;
if the occupation situation of the fourth neighbor node is not occupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the bottom of the current node;
if the occupation situation of the fifth neighbor node exists, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is non-occupation, determining the context model.
5. The point cloud geometry information encoding method according to claim 3, wherein after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometry information encoding method further comprises:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and a fourth neighbor node which is collinear at the upper right and the rear;
if the occupancy condition of the third neighbor node is occupied or the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the occupied neighbor node;
if the occupation situation of the third neighbor node is that the third neighbor node is not occupied and the occupation situation of the fourth neighbor node is not occupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is not occupied, determining the context model.
6. The point cloud geometry information encoding method according to claim 3, wherein after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometry information encoding method further comprises:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a third neighbor node which is coplanar at the left front and the lower part of the current node and the occupation situation of a fourth neighbor node which is collinear at the upper right and the rear;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situations of other collinear neighbor nodes except the left front lower collinear neighbor node of the current node;
and if the occupancy of other collinear neighbor nodes exists, determining the context model based on the occupancy of the occupied neighbor nodes.
7. A point cloud geometric information decoding method is characterized by comprising the following steps:
receiving a binary code stream;
aiming at a code word to be decoded in a binary code stream, determining the code word as a placeholder of a child node to be decoded;
acquiring a current octree partitioning model of point cloud geometric information;
aiming at a sub-node layer to be decoded in the current octree partitioning model, nodes at the edges of two sub-nodes which are away from the sub-node layer to be decoded in the negative direction of each dimension of the same coordinate system as the neighboring nodes at the same layer as the sub-node layer to be decoded and the sub-node layer to be decoded are determined as target nodes, and the occupation condition of the target nodes is obtained;
determining a context model of a subnode occupation code to be decoded according to the occupation situation of the target node;
and entropy decoding the placeholders of the child nodes to be decoded after the context model is determined.
8. The point cloud geometric information decoding method of claim 7, wherein the step of determining the context model of the child node to be decoded according to the occupancy of the target node comprises:
judging whether the occupation situation of each target node is unoccupied;
if the occupation situation of the target node except the first neighbor node is occupied, determining a context model according to the occupation situation;
if the occupation situation of each target node except the first neighbor node is not occupied, determining a context model based on the occupation situation of the first neighbor node sharing the vertex;
the first neighbor node is a neighbor node which shares a vertex with a child node to be coded.
9. The point cloud geometry information decoding method of claim 8, wherein after the step of determining a context model based on an occupancy of a first neighboring node that shares vertices if the occupancy of each target node other than the shared vertices is not occupied, the point cloud geometry information decoding method further comprises:
judging whether the occupation situation of each first neighbor node is occupied or not;
wherein, the left front-lower direction is the negative direction of each dimension coordinate axis, and the right upper-rear direction is the positive direction of each dimension coordinate axis;
when the occupation situation of each first neighbor node is unoccupied, acquiring the occupation situations of 3 coplanar second neighbor nodes which are coplanar after the upper right of the current node layer;
the current node is a father node of a child node to be decoded;
judging whether the occupation situation of the second neighbor node is occupied or not;
if the occupation situation of the second neighbor node is occupied, acquiring the spatial position of the child node to be decoded relative to the current node;
determining a context model based on the spatial position and the occupation condition of the second neighbor node;
and entropy decoding the placeholders of the child nodes to be decoded based on the context model.
10. The point cloud geometry information decoding method of claim 9, wherein after the step of determining whether the occupancy of the second neighboring node is occupied, the point cloud geometry information decoding method further comprises:
if the occupation situation of the second neighbor node is not occupied, acquiring the occupation situation of a coplanar third neighbor node at the left front part and the lower part of the current node;
if the occupation situation of the third neighbor node is occupied, determining a context model based on the occupation situation of the third neighbor node;
if the occupation situation of the third neighbor node is not occupied, acquiring the occupation situation of a collinear fourth neighbor node behind the upper right of the current node;
if the occupancy condition of the fourth neighbor node is occupied, determining a context model based on the occupancy condition of the fourth neighbor node;
if the occupation situation of the fourth neighbor node is unoccupied, acquiring the occupation situation of a fifth neighbor node which is collinear at the left, the front and the lower of the current node;
if the occupation situation of the fifth neighbor node is occupied, determining a context model based on the occupation situation of the fifth neighbor node;
and if the occupation condition of the fifth neighbor node is not occupied, determining the context model.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106846425A (en) * | 2017-01-11 | 2017-06-13 | 东南大学 | A kind of dispersion point cloud compression method based on Octree |
| WO2020189943A1 (en) * | 2019-03-15 | 2020-09-24 | 엘지전자 주식회사 | Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method |
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| US10805646B2 (en) * | 2018-06-22 | 2020-10-13 | Apple Inc. | Point cloud geometry compression using octrees and binary arithmetic encoding with adaptive look-up tables |
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|---|---|---|---|---|
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