CN114998456B - Three-dimensional point cloud attribute compression method based on local similarity - Google Patents
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Abstract
A three-dimensional point cloud attribute compression method based on local similarity comprises the steps of inputting point cloud data, sorting octree, generating predicted points, determining normal vector included angles between planes where the points are located, determining similarity of adjacent points, determining attribute predicted values and determining attribute predicted residual errors. By extracting the local data characteristics of the point cloud, constructing a local similarity calculation method based on Euclidean distance and normal angle, and selecting an actual attribute prediction value according to the maximum similarity value, the attribute information loss in the attribute prediction process can be reduced, and the prediction precision is improved. Through a comparative simulation experiment, the invention effectively improves the color attribute distortion degree in the compression process, has the characteristics of high compression efficiency, high reconstruction quality, easy realization and the like, is favorable for the storage and transmission of point clouds, and can be used for compression coding of colored point clouds.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a compression method of color attributes of three-dimensional point clouds.
Background
Three-dimensional point cloud data is one of the most representative three-dimensional data because of the advantages of high efficiency, high precision, easy acquisition and the like, and has been widely applied to various fields such as automatic driving, medical care, military national defense, cultural heritage, topographic exploration, industrial part production, virtual reality and augmented reality content creation and communication scenes. People can perceive and capture 3D scenes through a multi-view camera, a depth sensor, a laser radar scanner and the like, however, the huge data volume leads to limited storage and transmission of point clouds, so that the point cloud compression coding technology becomes a research hot spot in the field of computer vision.
In order to solve the application bottleneck of point cloud storage and transmission, a dynamic image expert group (Moving Picture Expert Group, MPEG) establishes an open point cloud compression standard system and issues a Geometry-based compression (G-PCC) test model. The attribute compression method of the point cloud mainly comprises three types: a transform-based attribute compression method, a prediction-based attribute compression method, and a mapping-based attribute compression method. The prediction-based method is suitable for compression of dense and sparse point clouds, and redundancy of attribute information can be effectively reduced.
The distortion degree of the compressed point cloud attribute determines the visual perception effect of human eyes on the three-dimensional point cloud, and the lower the distortion degree is, the more vivid the three-dimensional effect is observed by the human eyes. In recent years, attribute compression based on prediction has achieved numerous research results, but the rate-distortion performance of compression has yet to be improved.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art, and provide a three-dimensional point cloud attribute compression method based on local similarity.
The technical scheme adopted for solving the technical problems is composed of the following steps:
(1) Inputting point cloud data
At point cloud P i ∈{P 1 ,P 2 ,...,P N In each point P i Each contains position information P i (x, y, z) and color attribute information P i (R, G, B), wherein N is a finite positive integer, x, y, z respectively represent three-dimensional coordinates, and R, G, B respectively represent red, green and blue.
(2) Octree ordering
Establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z And respectively representing the side lengths of bounding boxes, wherein in the dividing process, the subcubes containing the point cloud data are marked as 1, the subcubes without the point cloud data are marked as 0, and the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, and m is a preset value.
(3) Generating predicted points
Respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are taken as K adjacent points, and the K adjacent points are taken as candidate predicted points, wherein K is smaller than M and K, M is a finite positive integer.
(4) Determining the angle of normal vector between planes of points
Determining the point P according to (3) i Plane and point P j The normal vector included angle theta (P) i ,P j )：
Wherein V is i And V j Respectively are points P i Sum point P j Is ∈ {1,2,., K }, is modulo.
(5) Determining similarity of adjacent points
Determining the adjacent point P according to the formula (4) j Relative to point P i Similarity S of (2) j ：
Wherein, alpha represents an angle weight factor, beta represents a distance weight factor, alpha+beta is 1, and alpha > beta.
(6) Determining attribute predictors
Determining a similarity maximum value among K adjacent points according to a formula (5):
S max ＝max(S j ) (5)
where j e {1,2,., K }, determining point P according to (6) i Attribute predictors of (2)
Wherein,,
for point P j Is>
Is the actual attribute value of the adjacent point with the maximum similarity, and
t is a similarity threshold.
(7) Determining attribute prediction residuals
Determining an attribute prediction residual ρ according to (7) i ：
And encoding the prediction residual error to realize attribute compression of the point cloud.
The step of generating the predicted point in the invention (3) is as follows:
respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are taken as K adjacent points, and the K adjacent points are taken as candidate prediction points, wherein K is smaller than M, the K value is 3-5, and the M value is 20-30.
The step of determining the attribute predicted value in the invention (6) is as follows:
determining a similarity maximum value among K adjacent points according to a formula (5):
S max ＝max(S j ) (5)
where j e {1,2,., K }, determining point P according to (6) i Attribute predictors of (2)
Wherein,,
for point P j Is>
Is the actual attribute value of the adjacent point with the maximum similarity, and
t is the similarity threshold, and D is the average distance of the point cloud.
The step of determining the attribute prediction residual error in the invention (7) is as follows:
determining an attribute prediction residual according to (7)
The quantization parameters of the attribute residual comprise five quantization levels R1 to R5, and specific quantization values are 46, 40, 34, 28 and 22 respectively.
The octree ordering step (2) of the invention is:
establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z Representing the side lengths of bounding boxes, respectively, and in the partitioning process, subcubes containing point cloud data are marked as1, the subcubes without point cloud data are marked as 0, the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, m is a preset value, and the value of m is 1-100.
The beneficial effects of the invention are as follows:
the method is characterized in that the concave-convex change of the surface of the point cloud is fully utilized, the Euclidean distance and the normal angle between adjacent points are used as characteristic factors to construct a similarity model, two prediction modes are specified, and the final prediction mode is judged according to the similarity value. The simulation contrast experiment shows that the invention effectively improves the color attribute distortion degree in the compression process, has the characteristics of high compression efficiency, high reconstruction quality, easy realization and the like, is beneficial to the storage and transmission of point cloud, and can be used for compression coding of colored point cloud.
Drawings
Fig. 1 is a flow chart of the implementation of embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of an input point cloud fande_00009_vox12 according to embodiment 1 of the present invention.
Fig. 3 is a schematic diagram of the fa ade_00009_vox12 point cloud reconstructed at the quantization level R1 according to embodiment 1 of the present invention.
Fig. 4 is a schematic diagram of the fa ade_00009_vox12 point cloud reconstructed at the quantization level R2 according to embodiment 1 of the present invention.
Fig. 5 is a schematic diagram of the fa ade_00009_vox12 point cloud reconstructed at the quantization level R3 according to embodiment 1 of the present invention.
Fig. 6 is a schematic diagram of the fa ade_00009_vox12 point cloud reconstructed at the quantization level R4 according to embodiment 1 of the present invention.
Fig. 7 is a schematic diagram of the reconstructed facade_00009vox12 point cloud at quantization level R5 according to embodiment 1 of the present invention.
Fig. 8 is a luminance rate distortion contrast curve of the method of embodiment 1 of the present invention compressing the facade_00009_vox12 with the G-PCC attribute prediction compression method.
Detailed description of the preferred embodiments
The present invention will be described in further detail with reference to the drawings and examples, but the present invention is not limited to the following embodiments.
Example 1
The three-dimensional point cloud attribute compression method based on local similarity of the embodiment is composed of the following steps (see fig. 1):
(1) Inputting point cloud data
At point cloud P i ∈{P 1 ,P 2 ,...,P N In each point P i Each contains position information P i (x, y, z) and color attribute information P i (R, G, B), wherein N is a finite positive integer, x, y, z respectively represent three-dimensional coordinates, and R, G, B respectively represent red, green and blue.
(2) Octree ordering
Establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z The side lengths of bounding boxes are respectively represented, in the dividing process, the subcubes containing point cloud data are marked as 1, the subcubes without point cloud data are marked as 0, the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, m is a preset value, the value of m is 1-100, and the value of m in the embodiment is 50.
(3) Generating predicted points
Respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest KThe point is taken as a K adjacent point, K adjacent points are taken as candidate predicted points, wherein K is smaller than M, K, M is a limited positive integer, the K value is 3-5, the M value is 20-30, the K value in the embodiment is 4, and the M value is 25.
(4) Determining the angle of normal vector between planes of points
Determining the point P according to (3) i Plane and point P j The normal vector included angle theta (P) i ,P j )：
Wherein V is i And V j Respectively are points P i Sum point P j Is ∈ {1,2,., K }, is modulo.
(5) Determining similarity of adjacent points
Determining the adjacent point P according to the formula (4) j Relative to point P i Similarity S of (2) j ：
Wherein, alpha represents an angle weight factor, beta represents a distance weight factor, alpha+beta is 1, alpha is larger than beta, alpha in the embodiment takes on a value of 0.825, and beta takes on a value of 0.175.
(6) Determining attribute predictors
Determining a similarity maximum value among K adjacent points according to a formula (5):
S max ＝max(S j ) (5)
where j e {1,2,., K }, determining point P according to (6) i Attribute predictors of (2)
Wherein,,
for point P j Is>
Is the actual attribute value of the adjacent point with the maximum similarity, and
d is the average distance of the point cloud, T is the similarity threshold, and the alpha value and the beta value are the same as those in the step (5).
(7) Determining attribute prediction residuals
Determining an attribute prediction residual according to (7)
And encoding the prediction residual error to realize attribute compression of the point cloud. The quantization parameters of the attribute residual comprise five quantization levels R1 to R5, and specific quantization values are 46, 40, 34, 28 and 22 respectively.
And (3) completing the three-dimensional point cloud attribute compression method based on the local similarity.
Example 2
The three-dimensional point cloud attribute compression method based on local similarity in the embodiment comprises the following steps:
(1) Inputting point cloud data
This step is the same as in example 1.
(2) Octree ordering
Establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z And respectively representing the side lengths of bounding boxes, wherein in the dividing process, the subcubes containing the point cloud data are marked as 1, the subcubes without the point cloud data are marked as 0, and the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, wherein m is a preset value, the value of m is 1-100, and the value of m in the embodiment is 1.
(3) Generating predicted points
Respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are taken as K adjacent points, and the K adjacent points are taken as candidate prediction points, wherein K is smaller than M, K, M is a finite positive integer, the K value is 3-5, the M value is 20-30, the K value in the embodiment is 3, and the M value is 20.
(4) Determining the angle of normal vector between planes of points
This step is the same as in example 1.
(5) Determining similarity of adjacent points
Determining the adjacent point P according to the formula (4) j Relative to point P i Similarity S of (2) j ：
Wherein, alpha represents an angle weight factor, beta represents a distance weight factor, alpha+beta is 1, alpha is larger than beta, alpha in the embodiment takes on the value of 0.694, and beta takes on the value of 0.306.
The other steps were the same as in example 1. And (3) completing the three-dimensional point cloud attribute compression method based on the local similarity.
Example 3
The three-dimensional point cloud attribute compression method based on local similarity in the embodiment comprises the following steps:
(1) Inputting point cloud data
This step is the same as in example 1.
(2) Octree ordering
Establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z And respectively representing the side lengths of bounding boxes, wherein in the dividing process, the subcubes containing the point cloud data are marked as 1, the subcubes without the point cloud data are marked as 0, and the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, wherein m is a preset value, the value of m is 1-100, and the value of m in the embodiment is 100.
(3) Generating predicted points
Respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are taken as K adjacent points, and the K adjacent points are taken as candidatesWherein K is less than M, K, M is a finite positive integer, the K value is 3-5, the M value is 20-30, the K value in this embodiment is 5, and the M value is 30.
(4) Determining the angle of normal vector between planes of points
This step is the same as in example 1.
(5) Determining similarity of adjacent points
Determining the adjacent point P according to the formula (4) j Relative to point P i Similarity S of (2) j ：
Wherein, alpha represents an angle weight factor, beta represents a distance weight factor, alpha+beta is 1, alpha is larger than beta, alpha in the embodiment takes on the value of 0.547, and beta takes on the value of 0.453.
The other steps were the same as in example 1. And (3) completing the three-dimensional point cloud attribute compression method based on the local similarity.
In order to prove the beneficial effects of the invention, the inventor adopts the three-dimensional point cloud compression method based on local similarity and the G-PCC attribute compression method based on prediction (hereinafter referred to as G-PCC Pred) of the embodiment 1 of the invention to carry out a comparison experiment, and the experimental conditions are as follows:
fig. 2 is a standard point cloud face00009_vox12 provided by MPEG, which has rich texture variation, containing 1596085 points, each including position information (x, y, z) and color attribute information (R, G, B). The attribute compression test was performed as in example 1, with objective evaluation and subjective visual reconstruction. Y is the brightness index of the color, and the evaluation index comprises: the peak signal-to-noise ratio (hereinafter abbreviated as Y-PSNR), bit rate (hereinafter abbreviated as Bitrate), BD-PSNR of Y are shown in table 1 and fig. 8, and the subjective evaluation results are shown in fig. 3 to 7.
TABLE 1 Point cloud Facade_00009_vox12 compression results
Table 1 shows compression performance indexes of compressing the point cloud fande_00009_vox12 under different quantization parameters, and under different quantization parameters, the method of the present invention effectively improves the PSNR value of the reconstructed point cloud Y channel, and fig. 8 is a luminance attribute compression rate distortion graph of example 1, compared with the G-PCC Pred compression method, BD-PSNR is 0.55dB. The compression effect of five quantization levels of R1-R5 is shown in fig. 3-7, and as the quantization levels are raised, the reconstruction point cloud is closer to the original point cloud, and more color information is reserved. The method effectively reserves the color information of the point cloud, improves the compression quality and has better rate distortion performance.
In summary, experimental results show that compared with the G-PCC Pred compression method, the method provided by the invention effectively improves the color attribute distortion degree in the compression process, has the characteristics of high compression efficiency, high reconstruction quality, easiness in realization and the like, is beneficial to the storage and transmission of point clouds, and can be used for compression coding of colored point clouds.
Claims (5)
1. A three-dimensional point cloud attribute compression method based on local similarity is characterized by comprising the following steps:
(1) Inputting point cloud data
At point cloud P i ∈{P 1 ,P 2 ,...,P N In each point P i Each contains position information P i (x, y, z) and color attribute information P i (R, G, B), wherein N is a finite positive integer, x, y and z respectively represent three-dimensional coordinates, and R, G, B respectively represent red, green and blue;
(2) Octree ordering
Establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z Respectively representing the side length of the bounding box, wherein in the dividing process, the subcubes containing the point cloud data are marked as 1, the subcubes without the point cloud data are marked as 0, and the occupied nodes marked as 1 can be continuously divided downwards until the number of points in each subcubes is at most m, and m is a preset value;
(3) Generating predicted points
Respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are used as K adjacent points, and the K adjacent points are used as candidate prediction points, wherein K is smaller than M and K, M is a finite positive integer;
(4) Determining the angle of normal vector between planes of points
Determining the point P according to (3) i Plane and point P j The normal vector included angle theta (P) i ,P j )：
Wherein V is i And V j Respectively are points P i Sum point P j Is {1,2,., K }, is modulo;
(5) Determining similarity of adjacent points
Determining the adjacent point P according to the formula (4) j Relative to point P i Similarity S of (2) j ：
Wherein alpha represents an angle weight factor, beta represents a distance weight factor, alpha+beta is 1, and alpha > beta;
(6) Determining attribute predictors
Determining a similarity maximum value among K adjacent points according to a formula (5):
S max ＝max(S j ) (5)
where j e {1,2,., K }, determining point P according to (6) i Attribute predictors of (2)
Wherein,,
for point P j Is>
Is the actual attribute value of the adjacent point with the maximum similarity, and
t is a similarity threshold;
(7) Determining attribute prediction residuals
Determining an attribute prediction residual according to (7)
And encoding the prediction residual error to realize attribute compression of the point cloud.
2. The method for compressing three-dimensional point cloud attributes based on local similarity according to claim 1, wherein the step of (3) generating the predicted point is as follows:
respectively determining M adjacent points P according to the formula (2) j J e {1,2,., M } is equal to the current point P i Is a Euclidean distance d (P) i ,P j )：
Wherein, (x) i ,y i ,z i ) For point P i Three-dimensional coordinates of (x) j ,y j ,z j ) For point P j Taking the distance P i The nearest K points are taken as K adjacent points, and the K adjacent points are taken as candidate prediction points, wherein K is smaller than M, the K value is 3-5, and the M value is 20-30.
3. The method for compressing three-dimensional point cloud attributes based on local similarity according to claim 1, wherein the step of (6) determining the attribute predicted value is:
determining a similarity maximum value among K adjacent points according to a formula (5):
S max ＝max(S j ) (5)
where j e {1,2,., K }, determining point P according to (6) i Attribute predictors of (2)
Wherein,,
for point P j Is>
Is the actual attribute value of the adjacent point with the maximum similarity, and
t is the similarity threshold, and D is the average distance of the point cloud.
4. The method for compressing three-dimensional point cloud attributes based on local similarity according to claim 1, wherein the step of (7) determining the attribute prediction residual is:
determining an attribute prediction residual according to (7)
The quantization parameters of the attribute residual comprise five quantization levels R1 to R5, and specific quantization values are 46, 40, 34, 28 and 22 respectively.
5. The method for compressing three-dimensional point cloud attributes based on local similarity according to claim 1, wherein the step (2) of octree sequence is as follows:
establishing a point cloud bounding box, and dividing a point cloud space into 2 layers w ×2 w ×2 w Each subcube has a side length of 2 determined by w ：
2 w ＝max(B x ,B y ,B z ) (1)
Wherein w is the division number, and the size of w is determined by the side length of the bounding box, B x ,B y ,B z Respectively representing the side lengths of bounding boxes, wherein in the dividing process, the subcubes containing point cloud data are marked as 1, the subcubes without point cloud data are marked as 0, and the subcubes without point cloud data are markedThe occupied node with 1 can be continuously divided downwards until the number of points in each subcubes is at most m, m is a preset value, and the value of m is 1-100.
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