CN113453009B - Point cloud space scalable coding geometric reconstruction method based on fitting plane geometric error minimum - Google Patents
Point cloud space scalable coding geometric reconstruction method based on fitting plane geometric error minimum Download PDFInfo
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Abstract
The invention relates to a point cloud space scalable coding geometric reconstruction method based on minimum fitting plane geometric error, belonging to the technical field of video coding and decoding. And fitting a local plane of the current node by using neighbor node information through a local space formed by each node and 26 neighbor nodes around, constructing a geometric error function according to a point-to-point geometric error measurement principle, and solving a corresponding coordinate value when the geometric error minimum value is solved to serve as a reconstructed geometric coordinate. The problem of the geometric error is great in the space scalable back geometry reconstruction process is solved.
Description
Technical Field
The invention relates to the technical field of video coding and decoding, in particular to a point cloud space scalable coding geometric reconstruction method based on minimum geometric error of a fitting plane.
Background
In a point cloud G-PCC encoder framework, the slice is independently encoded after the input point cloud is divided. In slice, the geometric information of the point cloud and the attribute information corresponding to the points in the point cloud are encoded separately. The G-PCC encoder first encodes the geometry information. The encoder performs coordinate conversion on the geometric information to enable the point clouds to be contained in a bounding box; and then, quantization is carried out, wherein the quantization step mainly plays a role of scaling, as the quantization rounding ensures that the geometric information of a part of points is the same, whether to remove the repeated points is determined according to the parameters, the process of not removing the repeated points is called geometric lossless compression, the process of removing the repeated points is called geometric lossy compression, and the process of quantizing and removing the repeated points is also called a voxelization process. Next, the bounding box is divided based on octree. The geometric lossless compression process and the geometric lossy compression process have geometric reconstruction processes when the octree division is completed.
In the point cloud G-PCC decoder framework, the geometric bit stream information of the point cloud and the attribute bit stream corresponding to the points in the point cloud are decoded separately. The G-PCC decoder first decodes the geometry bitstream. The decoder performs arithmetic decoding on the geometric bit stream to decode a bounding box of the point cloud and occupied bits (1 is non-empty and 0 is empty) based on octree; decoding of geometric information is divided into two frames based on octree and trisup (triangular patch set) according to the difference of the hierarchy depth of octree partitioning during encoding.
spatial scalability (spatial scalability) is an important function of G-PCC, generating point cloud thumbnails by decoding partial point cloud bitstream information, and currently only works in decoding frameworks based on octree geometry information. skip Layer is an octree level with less decoding at the decoding end. As shown in fig. 1, the octree geometry is encoded to the K-th layer, and when the spatial scalability process is not performed, the K-th layer is completely decoded; partial decoding is performed when the spatial scalability process is performed, and decoding is completed to the M Layer (M = K-skip Layer).
Its function is controlled by the parameter scalable _ shifting _ enabled _ flag. When scalable _ shifting _ enabled _ flag =0, the spatial scalability function is not performed; when scalable _ shifting _ enabled _ flag =1, a spatial scalability function is performed.
In an octree-based geometric information decoding frame, a maximum cube box of a current point cloud in a space is calculated according to a bounding box, then, non-empty sub cubes are continuously subjected to octal division according to occupied bits, the division is stopped when leaf nodes obtained through the division are unit cubes of 1 multiplied by 1, but if Spatial scalability is carried out in the decoding process, the leaf nodes are divided to a specified skip Layer, and 2 is generated skipLayer ×2 skipLayer ×2 skipLayer The cube of (1). Then, generating a geometric coordinate through the cube, wherein the geometric coordinate is the coordinate of the left front lower corner of the cube for a unit cube with a leaf node of 1 multiplied by 1, and the geometric coordinate is 2 for the leaf node skipLayer ×2 skipLayer ×2 skipLayer According to the different skip Layer, different reconstruction strategies are adopted for the geometrical coordinates of the unit cube.
The current scalable coded geometry reconstruction method in the standard is proposed by Hyejung Hur, sejin Oh of LG Electronics Inc in proposal m52315, 1 month 2020 and is received by the MPEG G-PCC standard (i.e., MPEG-I (ISO/IEC 23090) Part 9). The technical scheme is that different reconstruction strategies are adopted according to different levels of the skip Layer.
The specific implementation of the decoding end is described as follows:
when skip Layer =1, the geometric coordinate is the coordinate of the lower left front corner of the cube, as shown in the position of 3.Q point;
when skip Layer >1, the geometric coordinates are the coordinates of the center position of the cube, such as the point position of 3.P in fig. 5363;
the technique is currently in the standard appendix C.
C.3 decoded position shift procedure
When mingeotnesizelog 2 is greater than 1, the process operates as follows for each slice of the current point cloud image in the octree-based geometric information decoding framework:
MinGeomNodeSizeLog2 is the minimum node side length of the current octree, and the value is equal to skip Layer;
PointCount is the total number of nodes when the current slice is decoded to a MinGeomNodeSizeLog2 level;
PointPos [ i ] [0] is the x-axis coordinate of the current node reconstruction geometric point;
PointPos [ i ] [1] is the y-axis coordinate of the current node reconstruction geometric point;
PointPos [ i ] [2] is the z-axis coordinate of the current node reconstruction geometric point;
mask is the intermediate mask;
the initial value of PointPos [ i ] [0] PointPos [2], [ i ] [1] PointPos [2] is the coordinate of the left/front/lower corner of the cube with the current side length of MinGeomNodeSizeLog2 node, as shown in 3.Q, and through the shift operation of the above code, the value of PointPos [ i ] [0] PointPos [ i ] [1] PointPos [ i ] [2] is equal to the coordinate of the center position of the cube with the current node, as shown in 3.P.
Currently, G-PCC measures the process geometry error using the following two methods.
(1) Using the point-to-point distance representation, the point-to-point geometric error measure calculation process is shown in FIG. 5, which is a black dot (b) i ) The point generated after the point cloud expansion coding and decoding, red point (a) j ) The point in the original point cloud with the closest distance. Difference in coordinates between black dot and red dot (E (i, j) = b) i -a j ) Is a point-to-point error vector. The length of the error vector is a point-to-point geometric error, namely:
b is a sparse point cloud after expansion coding, A is an original point cloud, and the point-to-point distance of B belongs to all points i
With N B Is a pointThe point number in the cloud B defines the point-to-point error D1 of the whole point cloud as:
(2) Using the point-to-plane distance representation, error vector E (i, j) is oriented normal to N j Projecting to obtain a new error vector
Thus, the point-to-plane error is calculated as:
in the above technology, only part of the geometric bitstream information is decoded, and all points in the node range are represented by one geometric point in the node space with the side length of the skip Layer, so that the geometric reconstruction process after the spatial scalability is performed is a lossy process.
Disclosure of Invention
Technical problem to be solved
The method aims to solve the problems that in the existing geometric reconstruction process after space scalability, the difference of distribution conditions of internal points of different nodes is not considered, and the geometric error in the geometric reconstruction process after space scalability is large. The invention provides a point cloud space scalable coding geometric reconstruction method based on the minimum geometric error of a fitting plane.
Technical scheme
A point cloud space scalable coding geometric reconstruction method based on fitting plane geometric error minimum is characterized in that: and fitting a local plane of the current node by using neighbor node information through a local space formed by each node and 26 neighbor nodes around the node, constructing a geometric error function according to a point-to-point geometric error measurement principle, and solving a corresponding coordinate value when a geometric error minimum value is solved to serve as a reconstructed geometric coordinate.
The further technical scheme of the invention is as follows: the surrounding 26 neighbor nodes comprise 6 coplanar neighbor nodes, 12 common-edge neighbor nodes and 8 common-point neighbor nodes.
The further technical scheme of the invention is as follows: the fitting of the local plane of the current node by using the neighbor node information specifically comprises the following steps:
1) K neighbor node plane fitting judgment
Retrieving 26 neighbor situations of the current neighbor node, and setting the number of neighbors existing in the 26 neighbors as: neighNum;
when the neighbor num is larger than or equal to K, calculating K neighbor nodes closest to the node center position coordinate by using the neighbor num nodes existing in 26 neighbor nodes around the node, and calculating the neighbor node center position coordinate by using the P point coordinate, the octreeSize of the node side length of the octree and the position of the neighbor;
2) Least squares plane fitting
The final equation for plane fitting by using the least square method plane fitting principle and the central position coordinates of N1-Nk is as follows:
a 0 *x+a l *y+a 2 -z＝O
wherein x i 、y i 、z i And the coordinates of the center position of the neighbor node.
The further technical scheme of the invention is as follows: the method is characterized in that the K value is equal to the number of searching neighbors of the point cloud image plane normal vector, and 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15 and 16 can be taken.
The further technical scheme of the invention is as follows: when the minimum value of the geometric error is solved, the corresponding coordinate value is taken as the reconstructed geometric coordinate as follows:
advantageous effects
The point cloud space scalable coding geometric reconstruction method based on the minimum fitting plane geometric error provided by the invention makes full use of the spatial correlation of the point cloud, so that the error of geometric reconstruction after spatial scalability is reduced. The geometric information PSNR represents: compared with the prior art, under the condition of the same code rate, the geometric error brought by the method provided by the invention is reduced (PSNR is a positive value) or increased (PSNR is a negative value) than that brought by the prior art.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a schematic view of a spatial scalability;
FIG. 2G-PCC decoder framework diagram;
FIG. 3 a scalable post-coding geometric reconstruction method;
FIG. 4 is a schematic diagram of the location of the present invention in the frame of a point cloud G-PCC decoder;
FIG. 5 is a schematic of a point-to-point error;
FIG. 6 is a schematic diagram of the center positions of the nodes and the neighboring nodes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The terms and expressions referred to in the present invention are used for the following explanations:
1) Point Cloud Compression (PCC)
2) Geometry-based Point Cloud Compression (G-PCC)
3) Sheet (slice)
4) Surrounding box (bounding box)
5) OctreeImage (octree)
6) Intra-frame prediction (intra prediction)
7) Triangular patch set (triangle soup, trisup)
8) Context-based Adaptive Binary Arithmetic Coding (CABAC)
9) Block (block)
10 ) intersection point (vertex)
11 Level of Detail (LOD of Detail)
12 Region Adaptive Hierarchical Transform (RAHT)
13 Skip Layer (skip Layer)
14 Spatial Scalability (Spatial Scalability)
15 Moving Picture Experts Group (MPEG)
16 International Organization for Standardization (ISO)
17 International Electrotechnical Commission (IEC)
18 Log2 logarithm of the side length of the smallest geometrical Node (Minimum Geometry Node Size Log2, minGeomNodeSizeLog 2)
19 Direct Point Count (DirectPoint Count )
The invention provides a brand-new point cloud space scalable coding geometric reconstruction method based on the minimum geometric error of a fitting plane, which is characterized in that a local space is formed by each node and 26 surrounding neighbor nodes, the local plane of the current node is fitted by using neighbor node information, a geometric error function is constructed according to a point-to-point geometric error measurement principle, and a corresponding coordinate value when the minimum geometric error is solved is used as a reconstructed geometric coordinate. The specific process is as follows:
N j is a point a j The normal vector of (a) is a normal vector using a point a j And neighbor points (K points in total) of the local plane are fitted with the normal vector of the local plane, and after scalable is carried out, each specific position information of the point cloud cannot be obtained at a decoding end, and the accurate local plane and plane normal vector cannot be calculated, so that neighbor occupation information of the current node is adopted to carry out approximate fitting on the local plane, and the local plane equation is calculated.
1.K neighbor node plane fitting judgment
Retrieving 26 neighbor situations of the current neighbor node, and setting the number of neighbors existing in the 26 neighbors as: neighNum;
when neighNum is larger than or equal to K, K neighbor nodes closest to the node center position coordinate (P points in figure 6 are node center positions) are calculated by utilizing neighNum nodes existing in 26 neighbor nodes around the node, and the neighbor node center position coordinate (N1-Nk in figure 6 are neighbor node center position coordinates) is calculated by utilizing the P point coordinate, octreeSize node side length and neighbor positions.
2. Least square method plane fitting
And performing plane fitting by using the least square method plane fitting principle through the central position coordinates of N1-Nk. According to the least square method plane fitting principle and the general plane formula:
z＝a 0 *x+a 1 *y+a 2
the least square method shows that:
S＝minΣ[(a 0 *x i +a 1 *y i +a 2 )-2 i ] 2
for the above formula, respectively take a 0 ,a 1 ,a 2 Partial derivatives of (a):
and then, converting the formula into a matrix form after shifting:
then, for the above formula, a is obtained by the Kramer rule 0 ,a 1 ,a 2 A determinant expression of (a);
namely: the final equation for plane fitting by using the least square method plane fitting principle and the central position coordinates of N1-Nk is as follows:
a 0 *x+a l *y+a 2 -z＝O
3. calculation of geometrical reconstruction coordinates after plane fitting
According to the prior condition, the coordinate point after geometric reconstruction can not exceed the node space, namely, a point coordinate point P is selected 1 (x, y, z) satisfies the following constraints:
(Point P) 1 (x, y, z) are geometric coordinates satisfying the above conditions, and the point P 1 (x, y, z) is on the fitted plane. )
From the point-to-point error measure, point P 1 (x, y, z) in local space (e.g., the largest cubic space in FIG. 3), the point-to-point geometric error equation:
and because of the point P 1 (x, y, z) satisfies the fitted plane equation described above, i.e.:
a 0 *x+a l *y+a 2 ＝z
therefore, the above-mentioned error function is equivalent to:
partial derivatives of f (x, y) in the x direction:
when in use
When the temperature of the water is higher than the set temperature,
partial derivatives of f (x, y) in the y direction:
when in use
When the utility model is used, the water is discharged,
wherein,
the minimum value of the above error equation is:
and because
z＝a 0 *x+a 1 *y+a 2
Therefore, the final geometric coordinates of the selected points are as follows:
this value is the minimum solution of the above-mentioned geometric error equation.
The following compares the cases when skip Layer =3 different skips layers.
D1-PSNR is calculated as follows:
D2-PSNR is calculated as follows:
(where p is the peak constant value for each reference point cloud defined in the table, determined from the point cloud sequence, as shown in Table 2. Bold.)
TABLE 1 PSNR for geometric information
TABLE 2 constant value of point cloud sequence peak
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present disclosure.
Claims (1)
1. A point cloud space scalable coding geometric reconstruction method based on fitting plane geometric error minimum is characterized in that:
step 1: through a local space formed by each node and 26 surrounding neighbor nodes, the 26 surrounding neighbor nodes comprise 6 coplanar neighbor nodes, 12 common-edge neighbor nodes and 8 common-point neighbor nodes;
step 2: fitting the local plane of the current node by using the neighbor node information:
1) K neighbor node plane fitting judgment
Retrieving 26 neighbors of the current node, and setting the number of neighbors existing in the 26 neighbors as follows: neighNum;
when neighNum is larger than or equal to K, calculating K neighbor nodes closest to the coordinates of the center position of the node by utilizing neighNum nodes existing in 26 neighbor nodes around the node, wherein the K value is equal to the number of neighbor searching vectors of the point cloud image plane, and the K value is any one of 1,2,3,4,5,6,7,8,9, 10, 11, 12, 13, 14, 15 and 16; calculating the coordinates of the center position of the neighbor node by using the coordinates of the P point, the side length octreeSize of the node of the octree and the position of the neighbor; the coordinate of the point P is the coordinate of the center position of the current node;
2) Least square method plane fitting
The final equation for plane fitting by using the least square method plane fitting principle and the central position coordinates of N1-Nk is as follows:
a 0 *x+a 1 *y+a 2 -z＝0
wherein x i 、y i 、z i Coordinates of the center position of the neighbor node;
and step 3: and then according to a point-to-point geometric error measurement principle, constructing a geometric error function, and solving a coordinate value corresponding to the minimum value of the geometric error as a reconstructed geometric coordinate:
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