CN111754421A - Improved guide filtering three-dimensional scattered point cloud quick fairing method - Google Patents

Improved guide filtering three-dimensional scattered point cloud quick fairing method Download PDF

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CN111754421A
CN111754421A CN202010483771.6A CN202010483771A CN111754421A CN 111754421 A CN111754421 A CN 111754421A CN 202010483771 A CN202010483771 A CN 202010483771A CN 111754421 A CN111754421 A CN 111754421A
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王春阳
王珊
刘雪莲
肖博
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Xian Technological University
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Abstract

The invention discloses an improved guide filtering three-dimensional scattered point cloud rapid fairing method, which comprises the steps of arranging point cloud data to be processed in an ascending order according to an axis, solving the maximum point cloud number in quasi-block data according to the total point cloud number and the quasi-block number, calculating expansion processing points according to the maximum point cloud number, partitioning the point cloud data according to the maximum point cloud number and the expansion processing points in the quasi-block data, enabling the partitioned point clouds to generate an overlapping area, effectively solving the splitting phenomenon generated by the point cloud data, processing the partitioned point cloud data through a parallel three-dimensional guide filtering point cloud fairing algorithm to improve the operation rate, recovering and integrating the processed point cloud data, and outputting results. The method can effectively solve the problem of gradient inversion in the bilateral filtering fairing method and can effectively improve the operation speed of the algorithm.

Description

Improved guide filtering three-dimensional scattered point cloud quick fairing method
Technical Field
The invention relates to the field of reverse engineering of computer vision technology and three-dimensional reconstruction, and particularly provides an improved guide filtering rapid fairing method for three-dimensional scattered point clouds.
Background
Three-dimensional point cloud is widely concerned as data capable of expressing geometric information of object surfaces, and the acquisition of point cloud data is an important link of reverse engineering. However, due to the defects of the acquisition device, the interference of system noise, environmental factors and the like, noise generally exists in the acquired point cloud data, and the point cloud data containing the noise can have adverse effects on subsequent point cloud processing, so that the denoising of the three-dimensional point cloud data becomes one of the hot problems in the field of reverse engineering research.
The noise contained in the three-dimensional point cloud data can be divided into drift noise points and mixed noise points. The drift noise points are represented in the form of noise points which are obviously far away from the point cloud main body and float near the point cloud main body, and the mixed noise points are represented in the form of noise points mixed with the point cloud main body. The methods used by the scholars in the field to process the mixed noise points are mainly a bilateral filtering fairing method and a guided filtering fairing method. The bilateral filtering point cloud fairing method can effectively reduce image noise and maintain edge information to the maximum extent, but when a plurality of similar points are arranged around one point (usually on the edge), the average Gaussian weighting is unstable, and the gradient inversion problem possibly exists, so that the fairing effect is poor. The guiding filtering fairing method can effectively solve the problem of gradient inversion in the bilateral filtering fairing method, but the operation time of the algorithm is still not ideal. The prior two methods have the problem of slow running speed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an improved guide filtering three-dimensional scattered point cloud quick fairing method, thereby solving the problem of low operation speed in the prior art.
The invention provides an improved guide filtering three-dimensional scattered point cloud quick fairing method, which solves the technical scheme of the problem and comprises the following steps:
step 1: inputting three-dimensional scattered point cloud data P ═ P containing noisei},i＝1,2,...,n，piRepresenting each point in the point cloud P, wherein n is the total number of the point clouds;
step 2: partitioning the three-dimensional point cloud data P containing the noise obtained in the step 1 to form point cloud data Pj：
(2-1) arranging the point cloud data to be processed in ascending order according to the Z axis;
(2-2) calculating the maximum point cloud number M according to the formula (1), calculating the expanded processing point number M according to the formula (2),
Figure BDA0002518158100000011
Figure BDA0002518158100000012
wherein N represents the total number of point clouds P, N represents the number of quasi-blocks, and a represents the percentage of expansion processing point number M in the maximum point cloud number M
Figure BDA0002518158100000021
Represents rounding up;
(2-3) determining a partitioning range according to M and M, wherein the partitioning range of each processed point cloud is shown as a formula (3),
Figure BDA0002518158100000022
in the formula PjJ-th block, j ═ 1,2,. ·, N, representing the point cloud P;
and step 3: for the partitioned point cloud data P in the step 2jProcessing the parallel three-dimensional guiding filtering point cloud fairing algorithm to obtain fairing point cloud data Qj；
And 4, step 4: point cloud data Q processed in the step 3jRecovering and integrating to output result Q
According to the M in the step 3, the processed point cloud data QjIntegration into an output point cloud Q ═ Q j1,2, N, as in formula (9),
according to the parameter M in the step 2, the point cloud data Q processed in the step 3jIntegration into an output point cloud Q ═ QjJ is 1,2, N, as shown in formula (9),
Figure BDA0002518158100000023
and (4) recovering and integrating the partitioned point clouds according to a formula (9), and finally outputting a result graph processed by an algorithm.
The step 3 comprises the following steps:
(3-1) inputting three-dimensional point cloud data P to be processed after being partitionedjSetting the number K of search neighborhoods to 10 and the coefficient of unit matrix to 0.1, PjAre transmitted into different processing units to realize the parallel operation of the algorithm;
(3-2) searching for p by using K-D Tree algorithmiK neighborhood of points, wherein K is the number of neighborhood points and is less than the total number of each point cloud, and a neighborhood point set is set to be N (p) according to a formula (3)i)＝{pik∈pjIn which p isikIs piK neighborhood points, K1, 2,., K;
(3-3) from the neighborhood set, we can calculate a quantity
Figure BDA0002518158100000028
Referred to as point piThe centroid of the neighborhood, as shown in equation (4);
Figure BDA0002518158100000024
(3-4) calculating a of the point according to the formula (5) and the formula (6)iAnd bi，
Figure BDA0002518158100000025
Figure BDA0002518158100000026
Thereby obtaining the optimal solution of formula (7);
Figure BDA0002518158100000027
(3-5) equation (7) minimizes the reconstructed residual error of equation (8), i.e. the spatial position of the point is updated according to equation (8), qi＝aipi+bi(8)
Wherein p isiAnd q isiIs a point before and after the guided filtering, aiIs a scalar quantity, biIs a vector of 3 x 1;
(3-6) traversing and updating all data points in each block; judging whether the data point is the last data point, if so, finishing the filtering fairing algorithm, otherwise, returning to the step (3-2) and performing iteration;
(3-7) outputting three-dimensional point cloud data Q after fairingj。
Compared with the prior art, the method has the following beneficial effects:
compared with the traditional three-dimensional guide filtering point cloud fairing algorithm, the improved guide filtering three-dimensional scattered point cloud rapid fairing method provided by the invention improves fairing capacity and algorithm speed, blocks point cloud data to realize parallel processing, and greatly shortens the operation time of the algorithm.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of three-dimensional point cloud data of a rabbit model;
FIG. 3 is a schematic diagram of three-dimensional point cloud data containing noise;
FIG. 4 is a schematic block diagram of three-dimensional scattered point cloud data;
FIG. 5 is a flow chart of a three-dimensional point cloud guided filtering fairing algorithm;
FIG. 6 is a three-dimensional point cloud guide filtering fairing algorithm result diagram;
FIG. 7 is a diagram of the result of an improved guided filtering three-dimensional scattered point cloud fast fairing method.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
referring to fig. 1, the invention provides an improved method for guiding and filtering three-dimensional scattered point cloud rapid fairing, which comprises the following steps:
step 1: taking a rabbit point cloud model of the stanford university standard point cloud library as an example, referring to fig. 2, mixed noise is added thereto, which is denoted as P, and the number n of point clouds is 40256, referring to fig. 3.
Step 2: partitioning the three-dimensional point cloud data P containing the noise obtained in the step 1 into 7 blocks with N
The specific operation steps comprise:
(2-1) firstly, ordering the point cloud data to be processed from small to large according to the Z axis.
(2-2) then, according to the formula (1), calculating the maximum point cloud number M to be 5751, and according to the formula (2), calculating the expanded processing point number M to be 575, and taking a to be 0.1.
Figure BDA0002518158100000031
Figure BDA0002518158100000032
Wherein N represents the total number of point clouds P, N represents the number of quasi-blocks, and a represents the percentage of expansion processing point number M in the maximum point cloud number M
Figure BDA0002518158100000033
Represents rounding up;
(2-3) the partition range can then be determined from M and M, and the partition range is determined according to equation (3) as shown in Table 1.
Figure BDA0002518158100000041
TABLE 1 Point cloud data specific division Range
Block j Processing point cloud ranges Specific range
1 [1,1×M+m] [1,6326]
2 [1×M-m,2×M+m] [5176，12077]
3 [2×M-m,3×M+m] [10927,17828]
4 [3×M-m,4×M+m] [16678,23579]
5 [4×M-m,5×M+m] [22433，29360]
6 [5×M-m,6×M+m] [28180，35081]
7 [6×M-m,n] [33931，40256]
The point cloud is divided according to the method, and the block diagram is shown in fig. 4. The overlapping part exists between the blocks, so that the design has the advantage that the phenomenon of splitting cannot occur when different blocks are processed.
And step 3: for the partitioned point cloud data P in the step 2jThe parallel three-dimensional guiding filtering point cloud fairing algorithm processing is carried out, referring to fig. 5, and the specific steps comprise:
(3-1) inputting three-dimensional point cloud data P to be processed after being partitionedjSetting the number K of search neighborhoods to 10 and the coefficient of unit matrix to 0.1, PjAre transmitted into different processing units to realize the parallel operation of the algorithm.
(3-2) searching for p by using K-D Tree algorithmiK neighborhood of points, wherein K is the number of neighborhood points and is less than the total number of each point cloud, and a neighborhood point set is set to be N (p) according to a formula (3)i)＝{pik∈pjIn which p isikIs piK neighborhood points, K1, 2.
(3-3) from the neighborhood set, we can calculate a quantity
Figure BDA0002518158100000042
Referred to as point piThe centroid of the neighborhood is given by the formula (4).
Figure BDA0002518158100000043
(3-4) calculating a of the point according to the formula (5) and the formula (6)iAnd bi，
Figure BDA0002518158100000044
Figure BDA0002518158100000045
Thereby obtaining the optimal solution of equation (7).
Figure BDA0002518158100000046
(3-5) equation (7) minimizes the reconstructed residual error of equation (8), i.e. the spatial position of the point can be updated according to equation (8),
qi＝aipi+bi(8)
wherein p isiAnd q isiIs a point before and after the guided filtering, aiIs a scalar quantity, biIs a vector of 3 x 1.
(3-6) go through and update all data points in each block. And (4) judging whether the data point is the last data point, if so, finishing the filtering fairing algorithm, otherwise, returning to the step (3-2) and performing iteration.
(3-7) outputting three-dimensional point cloud data Q after fairingj。
And 4, step 4: point cloud data Q processed in the step 3jAnd recovering and integrating, and outputting a result Q.
Processing the point cloud data Q according to the result that M is 5751 in the step 3jIntegration into an output point cloud Q ═ Q j1,2, N, as in formula (9),
Figure BDA0002518158100000051
and restoring and integrating the partitioned point clouds according to the method, wherein the point taking range of the restored point clouds is shown in a table 2.
TABLE 2 Point cloud recovery partition Range
Block j Processing point cloud ranges Specific range
1 [1,1×M] [1,5751]
2 [1×M+1,2×M] [5752，11502]
3 [2×M+1,3×M] [11503,17253]
4 [3×M+1,4×M] [17254,23004]
5 [4×M+1,5×M] [23005，28755]
6 [5×M+1,6×M] [28756，34506]
7 [6×M+1,n] [34507，40256]
And finally, outputting a result graph processed by the algorithm, and referring to a graph 7 for an improved guide filtering three-dimensional scattered point cloud rapid fairing method experimental simulation result. Fig. 6 is a diagram of experimental simulation results of an unblocked three-dimensional guided filtering fairing method.
The comparison results of the three-dimensional guide filtering fairing method and the improved guide filtering three-dimensional scattered point cloud rapid fairing method under the condition of certain total number of point clouds and noise adding degree are shown in the table 3:
TABLE 3 comparison of results of different algorithms
Figure BDA0002518158100000052
As can be seen from Table 3, the improved method for guiding and filtering the three-dimensional scattered point cloud fast fairing is obviously improved in operation speed and is consistent with the three-dimensional guiding and filtering method in the aspect of filtering precision. Thus demonstrating the feasibility of the present invention.

Claims (2)

1. The improved guide filtering three-dimensional scattered point cloud fast fairing method is characterized by comprising the following steps: the method comprises the following steps:
step 1: inputting three-dimensional scattered point cloud data P ═ P containing noisei},i＝1,2,...,n，piRepresenting each point in the point cloud P, wherein n is the total number of the point clouds;
step 2: partitioning the three-dimensional point cloud data P containing the noise obtained in the step 1 to form point cloud data Pj：
(2-1) sequencing the point cloud data to be processed from small to large according to a Z axis;
(2-2) calculating the maximum point cloud number M according to the formula (1), calculating the expanded processing point number M according to the formula (2),
Figure FDA0002518158090000011
Figure FDA0002518158090000012
wherein N represents the total number of point clouds P, N represents the number of quasi-blocks, and a represents the percentage of expansion processing point number M in the maximum point cloud number M
Figure FDA0002518158090000013
Represents rounding up;
(2-3) determining the partitioning range according to M and M, wherein the partitioning range of each processed point cloud is shown as the formula (3),
Figure FDA0002518158090000014
in the formula PjJ-th block, j ═ 1,2,. ·, N, representing the point cloud P;
and step 3: for the partitioned point cloud data P in the step 2jProcessing the parallel three-dimensional guiding filtering point cloud fairing algorithm to obtain fairing point cloud data Qj；
And 4, step 4: point cloud data Q processed in the step 3jRecovering and integrating, and outputting a result Q;
according to the M in the step 3, the processed point cloud data QjIntegration into an output point cloud Q ═ Qj},j＝1,2,...,N；
According to the parameter M in the step 2, the point cloud data Q processed in the step 3jIntegration into an output point cloud Q ═ Qj},j＝1,2,...,N
Figure FDA0002518158090000015
And (4) recovering and integrating the partitioned point clouds according to a formula (9), and finally outputting a result graph processed by an algorithm.
2. The improved guide filtering three-dimensional scattered point cloud fast fairing method as claimed in claim 1, characterized in that: the step 3 comprises the following steps:
(3-1) inputting three-dimensional point cloud data P to be processed after being partitionedjSetting the number K of search neighborhoods to 10 and the coefficient of unit matrix to 0.1, PjAre transmitted into different processing units to realize the parallel operation of the algorithm;
(3-2) searching for p by using K-D Tree algorithmiK neighborhood of points, wherein K is the number of neighborhood points and is less than the total number of each point cloud, and a neighborhood point set is set to be N (p) according to a formula (3)i)＝{pik∈pjIn which p isikIs piK neighborhood points, K1, 2,., K;
(3-3) from the neighborhood set, we can calculate a quantity piReferred to as point piThe centroid of the neighborhood, as shown in equation (4);
Figure FDA0002518158090000021
(3-4) calculating a of the point according to the formula (5) and the formula (6)iAnd bi，
Figure FDA0002518158090000022
Figure FDA0002518158090000023
Thereby obtaining the optimal solution of formula (7);
Figure FDA0002518158090000024
(3-5) equation (7) minimizes the reconstructed residual error of equation (8), i.e. the spatial position of the point can be updated according to equation (8),
qi＝aipi+bi(8)
wherein p isiAnd q isiIs a point before and after the guided filtering, aiIs a scalar quantity, biIs a vector of 3 x 1;
(3-6) traversing and updating all data points in each block; judging whether the data point is the last data point, if so, finishing the filtering fairing algorithm, otherwise, returning to the step (3-2) and performing iteration;
(3-7) outputting three-dimensional point cloud data Q after fairingj。
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