CN104318622A - Triangular mesh modeling method of indoor scene inhomogeneous three dimension point cloud data - Google Patents
Triangular mesh modeling method of indoor scene inhomogeneous three dimension point cloud data Download PDFInfo
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Abstract
The invention relates to a triangular mesh modeling method of indoor scene inhomogeneous three dimension point cloud data. The triangular mesh modeling method of the indoor scene inhomogeneous three dimension point cloud data includes following steps: step 1, obtaining the indoor scene inhomogeneous three dimension point cloud data; step 2, performing neighborhood tangent plane projection; step 3, optimizing a projection neighborhood N'; step 4, obtaining a Delaunay adjacent side of a given point p; step 5, completing triangular mesh modeling: repeating the steps from the step 2 to the step 4, repeating the above algorithm on all points, and then completing the triangular mesh modeling of a whole three dimension point cloud. The triangular mesh modeling method of the indoor scene inhomogeneous three dimension point cloud data uses uniform partition of a sector area and all direction equilibrium selection, selects adjacent points of the given point in all directions well, evenly distributes the neighborhood in all the directions, and achieves precise modeling of the indoor scene inhomogeneous three dimension point cloud data. Simultaneously, selection of nearest neighbor points in all the directions further effectively streamlines the neighborhood of the given point, and therefore the triangular mesh modeling method of the indoor scene inhomogeneous three dimension point cloud data is short in running time and high in modeling efficiency.
Description
Technical field
The present invention relates to a kind of triangle gridding modeling method, more particularly, relate to a kind of triangle gridding modeling method of indoor scene non-homogeneous three-dimensional cloud data.
Background technology
Along with the development of 3-D scanning ranging technology, three dimensional point cloud is more and more extensive in the application in the fields such as reverse-engineering, industrial detection, independent navigation.Three dimensional point cloud treatment technology, as the basis realizing above-mentioned application, has played vital effect.In three dimensional point cloud treatment technology, the triangle gridding modeling of three dimensional point cloud is a very crucial technology.Because indoor environment is a kind of structuring scene, therefore triangle gridding modeling technique is particularly suitable for the three-dimensional modeling of indoor scene, and it not only can describe indoor scene in lifelike image ground, and lays a good foundation for the classification of indoor scene and target identification.The introducing of outstanding triangle gridding modeling method significantly can improve practical situations, improves application performance.When obtaining indoor scene three dimensional point cloud, the unexpected change of the operating characteristic that Laser Distance Measuring Equipment is lined by line scan and indoor environment structure, very easily cause the instability of scanning line spacing, thus make the distribution of three dimensional point cloud become extremely uneven, bring larger difficulty to the triangle gridding modeling of indoor scene.The triangle gridding modeling of three dimensional point cloud is the study hotspot of three dimensional point cloud process field always, and its modeling method is broadly divided into two large classes: the modeling method that modeling method and region based on Delaunay trigonometric ratio increase.Generally speaking, based on the modeling method of Delaunay trigonometric ratio, although there is good operation result, need a large amount of computings, so that the execution efficiency of its algorithm is lower, modeling speed is slower; The modeling method that region increases has good operational efficiency, and modeling speed is very fast, but its modeling effect is not satisfactory sometimes.Be directed to the triangle gridding modeling of non-homogeneous three-dimensional cloud data, current domestic and international rare report, existing triangle gridding modeling technique is no longer applicable.Such as, more famous rolling ball algorithm (Ball-Pivoting Algorithm) just needs repeatedly to run according to the ball of different size to process uneven three dimensional point cloud, and result is unsatisfactory sometimes; For another example, based on the curve reestablishing method of two-dimentional Delaunay trigonometric ratio, it lays particular emphasis on the structure of the sampled point Delaunay adjacent side in section, and just their backprojection in three dimensions, to form triangle grid model, the method travelling speed is very fast, but there is strict restriction to sampling condition and the distribution of some cloud, non-homogeneous three-dimensional cloud data cannot be processed, when each local triangle, be difficult to obtain Delaunay adjacent side accurately, Holistic modeling effect is poor.
Summary of the invention
In order to overcome the deficiencies in the prior art, the object of the invention is to provide a kind of triangle gridding modeling method of indoor scene non-homogeneous three-dimensional cloud data.The method is for an indoor scene, first laser scanning and ranging instrument is utilized to obtain the non-homogeneous three-dimensional cloud data of indoor scene, its essence is a non-homogeneous point set in three dimensions, then by certain surface modeling methods, this point set is configured to a triangular mesh topological structure, with the real indoor scene of accurate description.This method solve the modeling quality brought due to cloud data skewness lower, actual scene cannot be described, the problem such as to deviate from true topological structure, and there is modeling speed faster.
In order to realize foregoing invention object, solve problem existing in prior art, the technical scheme that the present invention takes is: a kind of triangle gridding modeling method of indoor scene non-homogeneous three-dimensional cloud data, comprises the following steps:
Step 1, acquisition indoor scene non-homogeneous three-dimensional cloud data: by laser sensor, obtain indoor scene information, as non-homogeneous three-dimensional cloud data;
Step 2, the projection of neighborhood section: choose set point p=(x, y, z), calculate the mean distance d of three-dimensional point cloud, setting 5d is the radius of neighbourhood, obtains this given neighborhood of a point N={p
i=(x
i, y
i, z
i) | 1≤i≤k}, wherein: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, by the neighborhood N around p point, calculates the normal vector n of this p; By this normal vector n, build the section T at p point place, and by adjoint point p
iproject on the T of section, the point set after note projection is projection neighborhood N ';
Step 3, projection neighborhood N ' to be optimized: evenly divided with each to equilibrium selection by sector region, the projection neighborhood N ' of further optimization set point p, make set point p all have nearest projection adjoint point in all directions, the point set after note optimization is for optimizing neighborhood N ";
The Delaunay adjacent side of step 4, acquisition set point p: utilize two-dimentional Delaunay method to set point p and optimize neighborhood N " carry out triangle gridding modeling; by the two-dimentional Delaunay triangulation network lattice back mapping of acquisition to three-dimensional neighborhood space, and the three-dimensional Delaunay adjacent side that pick up and store is connected with set point p;
Step 5, complete triangle gridding modeling: repeat step 2-4, above-mentioned algorithm is repeated to each point, then completes the triangle gridding modeling of whole three-dimensional point cloud.
The projection of described step 2 neighborhood section, specifically comprises following sub-step:
The mean distance d of step (a), calculating three-dimensional point cloud, setting 5d is the radius of neighbourhood, extracts the neighborhood N of set point p;
Step (b), pass through formula
Ask for the covariance matrix M of neighborhood N, in formula: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, and T is vector transpose symbol, and column vector transposition is row vector by it;
Step (c), ask for the eigenvalue λ of M
1, λ
2, λ
3(λ
1< λ
2< λ
3), and corresponding proper vector v
1, v
2, v
3;
Step (d), by minimal eigenvalue λ
1characteristic of correspondence vector v
1unitization, namely obtain the normal vector n of set point p;
Step (e), each point in neighborhood N is all projected on the section T corresponding to normal vector n, the point set after projection is designated as projection neighborhood N '.
Described step 3 is optimized projection neighborhood N ', specifically comprises following sub-step:
Step (a), in the T of section, with set point p for initial point, built the straight line of initial point p, and make it dextrorotation from horizontal level and circle, at interval of 22.5 degree, divide a sector region, finally can form 16 sector regions centered by initial point p;
Step (b), the projection neighborhood N ' being arranged in p point section T is inserted the ready-portioned sector region of step (a);
Step (c), extract the nearest point of each sector region middle distance initial point p, these points just form the point set after optimizing, and are designated as and optimize neighborhood N ".
Described step 4 obtains the Delaunay adjacent side of set point p point, specifically comprises following sub-step:
The method that step (a), employing are divided and ruled, " according to x coordinate, be divided into several zonules, the some number in each zonule is not more than 3, and for each zonule, requirement can ensure to meet Delaunay criterion will to optimize neighborhood N;
Step (b), territory, neighbor cell is integrated into a larger region meeting Delaunay criterion;
Step (c), repetition step (b), more each comparatively large regions is successively merged, merge as a whole until all compared with large regions, so far, the local Delaunay triangle division of single completes; According to the uniqueness criterion of Delaunay trigonometric ratio, unique Delaunay trigonometric ratio result can be obtained;
Step (d), from build two-dimentional Delaunay triangle division result extract the limit be connected with set point p, by its back mapping to three-dimensional neighborhood space, and store, the limit stored is the three-dimensional Delaunay limit of set point p point.
Beneficial effect of the present invention is: a kind of triangle gridding modeling method of indoor scene non-homogeneous three-dimensional cloud data, comprise the following steps: step 1, acquisition indoor scene non-homogeneous three-dimensional cloud data: pass through laser sensor, obtain indoor scene information, as non-homogeneous three-dimensional cloud data; Step 2, the projection of neighborhood section: choose set point p=(x, y, z), calculate the mean distance d of three-dimensional point cloud, setting 5d is the radius of neighbourhood, obtains this given neighborhood of a point N={p
i=(x
i, y
i, z
i) | 1≤i≤k}, wherein: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, by the neighborhood N around p point, calculates the normal vector n of this p; By this normal vector n, build the section T at p point place, and by adjoint point p
iproject on the T of section, the point set after note projection is projection neighborhood N '; Step 3, projection neighborhood N ' to be optimized: evenly divided with each to equilibrium selection by sector region, the projection neighborhood N ' of further optimization set point p, make set point p all have nearest projection adjoint point in all directions, the point set after note optimization is for optimizing neighborhood N "; The Delaunay adjacent side of step 4, acquisition set point p: utilize two-dimentional Delaunay method to set point p and optimize neighborhood N " carry out triangle gridding modeling; by the two-dimentional Delaunay triangulation network lattice back mapping of acquisition to three-dimensional neighborhood space, and the three-dimensional Delaunay adjacent side that pick up and store is connected with set point p; Step 5, complete triangle gridding modeling: repeat step 2-4, above-mentioned algorithm is repeated to each point, then completes the triangle gridding modeling of whole three-dimensional point cloud.Compared with the prior art, the present invention utilizes sector region evenly to divide with each to equilibrium selection, have selected the adjoint point of set point preferably in all directions, make it neighborhood distribution in all directions more balanced, achieve the Accurate Model of indoor scene non-homogeneous three-dimensional cloud data.Meanwhile, given neighborhood of a point has also been simplified in the selection respectively to nearest neighbor point effectively, makes this method have lower working time and the modeling efficiency of Geng Gao.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
Fig. 2 is the schematic diagram of step of the present invention.
In figure: (a) is neighborhood section projection figure, (b) is optimized figure to projection neighborhood N ', and (c) is the Delaunay adjacent side figure obtaining set point p, and (d) is the Delaunay modeling figure under section.
Fig. 3 does not adopt part plan triangle gridding modeling schematic diagram of the present invention.
Fig. 4 is the triangle gridding modeling result figure of indoor scene 1 non-homogeneous three-dimensional cloud data.
In figure: (a) is whole structure figure, (b), (c) are partial, detailed view respectively.
Fig. 5 is the triangle gridding modeling result figure of indoor scene 2 non-homogeneous three-dimensional cloud data;
In figure: (a) is whole structure figure, (b), (c) are partial, detailed view respectively.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
As shown in Figure 1, a kind of triangle gridding modeling method of indoor scene non-homogeneous three-dimensional cloud data, comprise the following steps: step 1, acquisition indoor scene non-homogeneous three-dimensional cloud data: by laser sensor, obtain indoor scene information, as non-homogeneous three-dimensional cloud data; Step 2, the projection of neighborhood section: choose set point p=(x, y, z), calculate the mean distance d of three-dimensional point cloud, setting 5d is the radius of neighbourhood, obtains this given neighborhood of a point N={p
i=(x
i, y
i, z
i) | 1≤i≤k}, wherein: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, by the neighborhood N around p point, calculates the normal vector n of this p; By this normal vector n, build the section T at p point place, and by adjoint point p
iproject on the T of section, the point set after note projection is projection neighborhood N '; Step 3, projection neighborhood N ' to be optimized: evenly divided with each to equilibrium selection by sector region, the projection neighborhood N ' of further optimization set point p, make set point p all have nearest projection adjoint point in all directions, the point set after note optimization is for optimizing neighborhood N "; The Delaunay adjacent side of step 4, acquisition set point p: utilize two-dimentional Delaunay method to set point p and optimize neighborhood N " carry out triangle gridding modeling; by the two-dimentional Delaunay triangulation network lattice back mapping of acquisition to three-dimensional neighborhood space, and the three-dimensional Delaunay adjacent side that pick up and store is connected with set point p; Step 5, complete triangle gridding modeling: repeat step 2-4, above-mentioned algorithm is repeated to each point, then completes the triangle gridding modeling of whole three-dimensional point cloud.
The projection of described step 2 neighborhood section, specifically comprises following sub-step:
The mean distance d of step (a), calculating three-dimensional point cloud, setting 5d is the radius of neighbourhood, extracts the neighborhood N of set point p;
Step (b), pass through formula
Ask for the covariance matrix M of neighborhood N, in formula: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, and T is vector transpose symbol, and column vector transposition is row vector by it;
Step (c), ask for the eigenvalue λ of M
1, λ
2, λ
3(λ
1< λ
2< λ
3), and corresponding proper vector v
1, v
2, v
3;
Step (d), by minimal eigenvalue λ
1characteristic of correspondence vector v
1unitization, namely obtain the normal vector n of set point p;
Step (e), each point in neighborhood N is all projected on the section T corresponding to normal vector n, the point set after projection is designated as projection neighborhood N '; As schemed shown in (a) in Fig. 2.
Described step 3 is optimized projection neighborhood N ', specifically comprises following sub-step:
Step (a), in the T of section, with set point p for initial point, built the straight line of initial point p, and make it dextrorotation from horizontal level and circle, at interval of 22.5 degree, divide a sector region, finally can form 16 sector regions centered by initial point p;
Step (b), the projection neighborhood N ' being arranged in p point section T is inserted the ready-portioned sector region of step (a);
Step (c), extract the nearest point of each sector region middle distance initial point p, these points just form the point set after optimizing, and are designated as and optimize neighborhood N "; As schemed shown in (b) in Fig. 2.
Described step 4 obtains the Delaunay adjacent side of set point p point, specifically comprises following sub-step:
The method that step (a), employing are divided and ruled, " according to x coordinate, be divided into several zonules, the some number in each zonule is not more than 3, and for each zonule, requirement can ensure to meet Delaunay criterion will to optimize neighborhood N;
Step (b), territory, neighbor cell is integrated into a larger region meeting Delaunay criterion;
Step (c), repetition step (b), more each comparatively large regions is successively merged, merge as a whole until all compared with large regions, so far, the local Delaunay triangle division of single completes; According to the uniqueness criterion of Delaunay trigonometric ratio, unique Delaunay trigonometric ratio result can be obtained;
Step (d), from build two-dimentional Delaunay triangle division result extract the limit be connected with set point p, by its back mapping to three-dimensional neighborhood space, and store, the limit stored is the three-dimensional Delaunay limit of set point p point; As schemed shown in (c) in Fig. 2.Reruning afterwards, to each repetition step 2-4 in original point cloud data, obtaining the modeling of final section as schemed shown in (d) in Fig. 2.By the limit of all storages according to the display of trigonometric ratio result, the triangle gridding modeling result shown in Fig. 4 and Fig. 5 can be obtained.
In sum, the present invention does not carry out direct triangle gridding modeling to the discrete point in three-dimensional point cloud, but first the projection of neighborhood section is carried out to it, recycling sector region divide and each to equilibrium selection, optimize the projection neighborhood of set point, the distribution made it in all directions is more balanced, and finally utilizes each optimization neighborhood to equilibrium to realize the foundation of local triangle grid model.This not only solves the accurate modeling problem of indoor scene non-homogeneous three-dimensional cloud data, avoid the generation in large area cavity in modeling process, as shown in Figure 3, describe the topological structure of indoor scene truly, and due to the existence of optimizing process, the quantity of discrete point during local neighborhood modeling is sharply declined, avoids the some modeling of local domain mass data and the huge operation expense brought, drastically increase the operational efficiency of modeling, decrease the working time needed for modeling.
Claims (4)
1. a triangle gridding modeling method for indoor scene non-homogeneous three-dimensional cloud data, is characterized in that comprising the following steps:
Step 1, acquisition indoor scene non-homogeneous three-dimensional cloud data: by laser sensor, obtain indoor scene information, as non-homogeneous three-dimensional cloud data;
Step 2, the projection of neighborhood section: choose set point p=(x, y, z), calculate the mean distance d of three-dimensional point cloud, setting 5d is the radius of neighbourhood, obtains this given neighborhood of a point N={p
i=(x
i, y
i, z
i) | 1≤i≤k}, wherein: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, by the neighborhood N around p point, calculates the normal vector n of this p; By this normal vector n, build the section T at p point place, and by adjoint point p
iproject on the T of section, the point set after note projection is projection neighborhood N ';
Step 3, projection neighborhood N ' to be optimized: evenly divided with each to equilibrium selection by sector region, the projection neighborhood N ' of further optimization set point p, make set point p all have nearest projection adjoint point in all directions, the point set after note optimization is for optimizing neighborhood N ";
The Delaunay adjacent side of step 4, acquisition set point p: utilize two-dimentional Delaunay method to set point p and optimize neighborhood N " carry out triangle gridding modeling; by the two-dimentional Delaunay triangulation network lattice back mapping of acquisition to three-dimensional neighborhood space, and the three-dimensional Delaunay adjacent side that pick up and store is connected with set point p;
Step 5, complete triangle gridding modeling: repeat step 2-4, above-mentioned algorithm is repeated to each point, then completes the triangle gridding modeling of whole three-dimensional point cloud.
2. the triangle gridding modeling method of a kind of indoor scene non-homogeneous three-dimensional cloud data according to claim 1, is characterized in that: the projection of described step 2 neighborhood section, specifically comprises following sub-step:
The mean distance d of step (a), calculating three-dimensional point cloud, setting 5d is the radius of neighbourhood, extracts the neighborhood N of set point p;
Step (b), pass through formula
Ask for the covariance matrix M of neighborhood N, in formula: p
ifor adjoint point, i is the sequence number of adjoint point, and k is the number of adjoint point, and T is vector transpose symbol, and column vector transposition is row vector by it;
Step (c), ask for the eigenvalue λ of M
1, λ
2, λ
3(λ
1< λ
2< λ
3), and corresponding proper vector v
1, v
2, v
3;
Step (d), by minimal eigenvalue λ
1characteristic of correspondence vector v
1unitization, namely obtain the normal vector n of set point p;
Step (e), each point in neighborhood N is all projected on the section T corresponding to normal vector n, the point set after projection is designated as projection neighborhood N '.
3. the triangle gridding modeling method of a kind of indoor scene non-homogeneous three-dimensional cloud data according to claim 1, is characterized in that: described step 3 is optimized projection neighborhood N ', specifically comprises following sub-step:
Step (a), in the T of section, with set point p for initial point, built the straight line of initial point p, and make it dextrorotation from horizontal level and circle, at interval of 22.5 degree, divide a sector region, finally can form 16 sector regions centered by initial point p;
Step (b), the projection neighborhood N ' being arranged in p point section T is inserted the ready-portioned sector region of step (a);
Step (c), extract the nearest point of each sector region middle distance initial point p, these points just form the point set after optimizing, and are designated as and optimize neighborhood N ".
4. the triangle gridding modeling method of a kind of indoor scene non-homogeneous three-dimensional cloud data according to claim 1, is characterized in that: described step 4 obtains the Delaunay adjacent side of set point p point, specifically comprises following sub-step:
The method that step (a), employing are divided and ruled, " according to x coordinate, be divided into several zonules, the some number in each zonule is not more than 3, and for each zonule, requirement can ensure to meet Delaunay criterion will to optimize neighborhood N;
Step (b), territory, neighbor cell is integrated into a larger region meeting Delaunay criterion;
Step (c), repetition step (b), more each comparatively large regions is successively merged, merge as a whole until all compared with large regions, so far, the local Delaunay triangle division of single completes; According to the uniqueness criterion of Delaunay trigonometric ratio, unique Delaunay trigonometric ratio result can be obtained;
Step (d), from build two-dimentional Delaunay triangle division result extract the limit be connected with set point p, by its back mapping to three-dimensional neighborhood space, and store, the limit stored is the three-dimensional Delaunay limit of set point p point.
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| CN109493380A (en) * | 2017-09-11 | 2019-03-19 | 重庆大学 | The calculation method of irregular shear surface area in a kind of Rock Joint Plane shear test |
| CN109493380B (en) * | 2017-09-11 | 2022-05-17 | 重庆大学 | Method for calculating area of irregular shear surface in rock joint surface shear test |
| CN108334080A (en) * | 2018-01-18 | 2018-07-27 | 大连理工大学 | A kind of virtual wall automatic generation method for robot navigation |
| CN108920574A (en) * | 2018-06-22 | 2018-11-30 | 黑龙江科技大学 | The efficient neighborhood search method of extensive three dimensional point cloud |
| CN108920574B (en) * | 2018-06-22 | 2022-09-30 | 黑龙江科技大学 | Efficient neighborhood searching method for large-scale three-dimensional point cloud data |
| CN109671152A (en) * | 2018-12-01 | 2019-04-23 | 大连理工大学 | A kind of line point cloud geometrical property evaluation method weighting discrete derivative based on Gauss |
| CN109671152B (en) * | 2018-12-01 | 2022-10-04 | 大连理工大学 | Line point cloud geometric characteristic estimation method based on Gaussian weighted discrete derivative |
| WO2020238111A1 (en) * | 2019-05-27 | 2020-12-03 | 浙江大学 | Half maximum probability density distribution-based three-dimensional reconstruction m cethod |
| US20200380738A1 (en) * | 2019-05-27 | 2020-12-03 | Zhejiang University | Three-Dimensional Reconstruction Method Based on Half-Peak Probability Density Distribution |
| US11816762B2 (en) * | 2019-05-27 | 2023-11-14 | Zhejiang University | Three-dimensional reconstruction method based on half-peak probability density distribution |
| US11699264B2 (en) | 2019-12-27 | 2023-07-11 | Industrial Technology Research Institute | Method, system and computing device for reconstructing three-dimensional planes |
| CN111815691A (en) * | 2020-09-10 | 2020-10-23 | 熵智科技(深圳)有限公司 | Neighborhood access method, device, equipment and medium for grid data structure |
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