CN107452065A - The border spot identification method of surface sampled data in kind - Google Patents
The border spot identification method of surface sampled data in kind Download PDFInfo
- Publication number
- CN107452065A CN107452065A CN201710540411.3A CN201710540411A CN107452065A CN 107452065 A CN107452065 A CN 107452065A CN 201710540411 A CN201710540411 A CN 201710540411A CN 107452065 A CN107452065 A CN 107452065A
- Authority
- CN
- China
- Prior art keywords
- point
- border
- sampling point
- sampled data
- point set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/30—Polynomial surface description
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- Algebra (AREA)
- Computer Graphics (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The present invention provides a kind of border spot identification method of surface sampled data in kind, belongs to the reverse-engineering field of product, it is characterised in that：Acquisition sampling cloud data M is scanned to surface in kind, KD tree index structures are built for sampled data M；Arbitrary target sampling point in M is chosen, based on neighbour's data of KD tree quick obtaining target sampling points, and the section at neighbour's point set construction target sampling point based on target sampling point, target sampling point and its neighbour's point set are projected on the section；The circumference of construction projection point set, if the subpoint of target sampling point is located on constructed circumference, target sampling point is border sampling point.
Description
Technical field
The present invention provides a kind of border spot identification method of surface sampled data in kind, belongs to the reverse-engineering neck of product
Domain.
Background technology
Curve reestablishing technology is the core technology in reverse-engineering, mainly reconstructs original using the sampling point set on surface in kind
Beginning curved surface, the boundary information of sampling point set have a major impact as the domain for solving curved surface to the quality of follow-up curve reestablishing.
In reverse-engineering field, sampling point set have three-dimensional spatial distribution it is uneven the characteristics of, the knowledge to border sampling point
Not, and it is not present strict mathematical expression model.Bai Zhongdong etc. is in academic journal《Machine science and technology》2011,20(4):
In the scientific paper " BORDER PROCESSING technical research in Complex Curving Surface in Reverse Engineering " that 481-483 is delivered, by different type curved surface
Sampling point set projects to specific parametric surface and forms mapping parameter domain, and the border sampling point of curved surface is extracted by partitioning parameters domain,
This method is only applicable to sampling point set as special applications scenes such as plane, cylinder and spheres, it is difficult to is generalized to more complicated
Situation.Ke Yinglin etc. is in academic journal《Mechanical engineering journal》2004,09:Paper " the side based on a cloud delivered on 116-120
The direct extractive technique of boundary's feature ", space lattice division is carried out to the bounding box of surface sampling data, established based on space lattice
Boundary Extraction model, when data volume is larger, computational efficiency is relatively low.Sun Dianzhu etc. is in academic journal《Agricultural mechanical journal》
2013,44 (12), the scientific paper " the dispersion point cloud Boundary characteristic extraction based on Density Estimator " delivered on 275-279+268
In, method based on Density Estimator calculates the mode point of target sampling point, and foundation is used as using the Euclidean distance of itself and target sampling point
Enter the judgement of row bound sampling point, this method is poor to the adaptability of the sampled data of non-uniform Distribution.
In summary, the border spot identification method of surface sampling point set in kind and imperfect at present, it is difficult to adapt to it is non-
The Boundary Recognition of the surface sampling point set in kind of even distribution, therefore it provides a kind of recognition capability and the stronger table in kind of adaptability
The border spot identification method of surface sample data has turned into those skilled in the art's technical problem urgently to be resolved hurrily.
The content of the invention
The problem to be solved in the present invention is：A kind of overcome the deficiencies in the prior art, there is provided side of surface sampled data in kind
Boundary's sampling point method for quickly identifying, identifies border sampling point exactly.
In order to solve the above technical problems, the technical solution adopted in the present invention is a kind of border of surface sampled data in kind
Spot identification method, it is characterised in that step is followed successively by：First, set sampled data point set in surface in kind as M and be M construct KD trees
Three dimensions index structure；2nd, using index structure, neighbour's point set of any sampling point x in M is inquired aboutλ(x) ；3rd, constructλ(x)
Approach plane P and willλ(x) project on P, note projection point set isλ'(x)；4th, it isλ'(x) projected outline L (x) is constructed；
5th, x subpoint x ' is judged whether on L (x), if x ' is located at L (x), point x is border sampling point.
To realize goal of the invention, the border spot identification method of the surface sampled data in kind, it is characterised in that：For
Sampling point set M in surface in kind, index structure is constructed for it first with KD trees, inquires about neighbour's point set of any sampling point x in Mλ(x)
, then utilize the method construct of least square fittingλ(x) approach plane p, using p as the section at x, and willλ(x) and
Point x is projected on plane P, must be projected point set and isλ'(x)。
To realize goal of the invention, the border spot identification method of the surface sampled data in kind, it is characterised in that：To throw
Shadow point setλ'(x) circumference is constructed, concretely comprises the following steps 1. calculatingλ'(x) convex closureL c(x), using convex closure side as original wheel
Profile；2. initializationi← 0, L (x) ← φ；3. line taking section, calculate liMidpoint O, using O as the center of circle, with li's
Length is that diameter is made to justify, and counts the number of the point in circlen；If 4. n=0, by liSet L (x) is stored in, goes to step 7；
If n>0, in circle O, inquire about distance liNearest pointa；5. connectionaPoint and liTwo end points, construct new contour line；6.
To the contour line repeat step 3-5 of neotectonics；7. i←i+1；8. repeat step 2-7, until having traveled throughL c(x)；Final institute structure
The closed polygon L (x) for the two dimensional surface made is asλ'(x) circumference.
To realize goal of the invention, the border spot identification method of the surface sampled data in kind, it is characterised in that：According to
The projection point set constructedλ'(x) circumference L (x), if target sampling point x subpoint is located at the L (x), x is exactly institute
The border sampling point for the surface sampling point set in kind to be identified, above-mentioned steps are performed to all sampling point sets, until identifying institute
Some border sampling point information.
The border spot identification method of present invention surface sampled data in kind has advantages below：
1st, by the structure of the border spot identification problem fractional sample two-dimensional projection point set circumference of surface sampled data in kind
Make, reduce the dimension of Boundary Recognition, effectively increase the border spot identification efficiency of surface sampling point set in kind；
2nd, in the construction of two-dimensional projection's point set circumference, using iterative strategy, solving result is accurate, can effectively improve
The border spot identification precision of surface sampled data in kind.
Brief description of the drawings
Fig. 1 is the flow chart of the border spot identification method of present invention surface sampled data in kind；
Fig. 2 is the circumference organigram for partial projection point set；
Fig. 3 is the uniform sampling data for jack models；
Fig. 4 is method (Shi Baoquan, Liang Jin, the Liu Qing. Adaptive using SHI
simplification of point cloud using k-means clustering[J]. Computer-Aided
Design, 2011, 43(8):910-922) to the recognition result schematic diagram of uniform sampling data boundary shown in Fig. 3；
Fig. 5 is method (dispersion point clouds of Sun Dianzhu, Liu Huadong, Shi Yang, the Li Yanrui based on Density Estimator using Sun Dianzhu
Boundary characteristic extraction [J] agricultural mechanical journals, 2013,12:275-279+268) to the side of uniform sampling data shown in Fig. 3
Boundary's recognition effect schematic diagram；
Fig. 6 is the Boundary Recognition effect diagram that this paper algorithms are directed to uniform sampling data shown in Fig. 3；
Fig. 7 is the non-uniform sampling data to wheel hub model；
Fig. 8 is method (Shi Baoquan, Liang Jin, the Liu Qing. Adaptive using SHI
simplification of point cloud using k-means clustering[J]. Computer-Aided
Design, 2011, 43(8):910-922) to the result schematic diagram of non-uniform sampling data Boundary Recognition shown in Fig. 7；
Fig. 9 is method (dispersion point clouds of Sun Dianzhu, Liu Huadong, Shi Yang, the Li Yanrui based on Density Estimator using Sun Dianzhu
Boundary characteristic extraction [J] agricultural mechanical journals, 2013,12:275-279+268) to non-uniform sampling data side shown in Fig. 7
Boundary's recognition effect schematic diagram；
Figure 10 is Boundary Recognition effect diagram of this paper algorithms to non-uniform sampling data shown in Fig. 7.
Embodiment
Below in conjunction with the accompanying drawings and example the invention will be further described.
Fig. 1 is the flow chart of the border spot identification method of present invention surface sampled data in kind, utilizes spatial digitizer
The sampled data M on mock-up surface is obtained, builds KD trees index structure for sampling point set M in order to inquire about data；
Any sampling point x chosen in M, its neighbour's sampling point set is obtained using KD trees, neighbour's point set based on x, is intended using least square
Section P at the method construct point x of conjunction, and point x and its neighbour's point set are projected on plane P；Using heuristic search
Constructing tactics project the circumference of point set, if x subpoint falls in the projected outline of its neighbour's point set, x is real
The border sampling point of thing surface sampled data.
For target sampling point and its projection point set of neighbour's point set, circumference is constructed for it, as shown in Fig. 2 wherein first
The solid line of tail connection is exactly the convex closure border for projecting point setL c(x), as original contour line to projection point set internal contraction,
The detailed process of contraction is as follows：(1) by convex closure borderL c(x) original contour line is used as, arbitrarily chooses a contour line (here
The line segment of selection is AB)；(2) line segment AB midpoint O is calculated, and using O as the center of circle, makees to justify using AB as diameter；(3) statistics falls
Points in circle O, if points are not 0, subpoint (Fig. 2 midpoint C) nearest apart from line segment AB in circle O is calculated, then point C is exactly
The new profile summit identified, CA, CB are connected respectively and constructs new contour line, then the contour line of neotectonics is performed again
Collapse step；If the points fallen in circle O are 0, search for next original contour line and perform contraction process；(4) to all
Original contour line performs above-mentioned steps, until contraction process terminates, the profile of the constructed two-dimensional projection's point set of note is L (x).
Whether inquiry x subpoint x ' falls on projected outline L (x), and concrete mode is：L (x) profile summit is counted,
If x ' belongs to L (x) profile vertex set, x is exactly the border sampling point of the surface sampled data in kind identified.
Fig. 3 is to be directed to the uniform sampling data that jack models are sampled to obtain, and Fig. 4 is the Algorithm for Solving using SHI
Boundary effect figure, Fig. 5 are to solve obtained boundary effect figure using Sun Dianzhu method, and Fig. 6 is obtained using this paper algorithms
Boundary Recognition effect；Observe Fig. 3-Fig. 6 and understand that, for the uniform sampling data of the model, the border that SHI methods identify has bright
The boundary effect that the Boundary Recognition method that aobvious noise, this paper Boundary Recognitions method and Sun Dianzhu are provided identifies is better than SHI side
Boundary's recognition methods.
Fig. 7 is to be directed to the sampled data heterogeneous that wheel hub model samples to obtain, and Fig. 8's is the recognition methods for utilizing SHI
Obtained boundary effect figure, Fig. 9 are the boundary effect figures obtained using Sun Dianzhu Boundary Recognition method, and Figure 10 is using herein
The boundary effect figure that algorithm obtains；Fig. 7-Figure 10 is observed to understand, for wheel hub non-uniform sampling data, SHI and Sun Dianzhu side
More noise is contained on the border that boundary's recognition methods is identified, SHI Boundary Recognition method is especially in point set inner void
A large amount of noises are contained at place, and before this paper recognition methods does not have obvious noise, Boundary Recognition effect to be better than on the border identified
Two methods.
It is described above, only it is the preferred embodiments of the present invention, not makees the limitation of other forms to the present invention, it is any ripe
Know the equivalent reality that professional and technical personnel is changed or be modified as possibly also with the technology contents of the disclosure above to change on an equal basis
Apply example.But it is every without departing from technical solution of the present invention content, what the technical spirit according to the present invention was done to above example appoints
What simple modification, equivalents and remodeling, still fall within the protection content of technical solution of the present invention.
Claims (2)
- A kind of 1. border spot identification method of surface sampled data in kind, it is characterised in that：First, surface sampled data in kind is set Point set is M and is M construction KD tree three dimensions index structures；2nd, using index structure, the Neighbor Points of any sampling point x in M are inquired about Collectionλ(x)；3rd, constructλ(x) section P and by point x andλ(x) project on P, note projection point set isλ'(x)；4th, it is projection Point setλ'(x) circumference L (x) is constructed；5th, x subpoint x ' is judged whether on L (x), if x ' is located at L (x), point x As border sampling point.
- 2. the border spot identification method of surface sampled data in kind as claimed in claim 1, it is characterised in that：In step 4 In, construct partial projection point setλ'(x) circumference, specially 1. calculateλ'(x) convex closureL c(x), using convex closure side as Original contour line；2. initializationi← 0, L (x)←φ；3. line taking section, calculate liMidpoint O, using O as the center of circle, With liLength make to justify for diameter, and count the number of point in circlen；If 4.n=0, by liSet L (x) is stored in, turns step Rapid 7；If n>0, in circle O, inquire about distance liNearest pointa；5. connectionaPoint and liTwo end points, construct new profile Line；6. the contour line repeat step 3-5 of pair neotectonics；7. i←i+1；8. repeat step 2-7, until having traveled throughL c(x)；Most Whole L (x) is asλ'(x) circumference.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710540411.3A CN107452065A (en) | 2017-07-05 | 2017-07-05 | The border spot identification method of surface sampled data in kind |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710540411.3A CN107452065A (en) | 2017-07-05 | 2017-07-05 | The border spot identification method of surface sampled data in kind |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107452065A true CN107452065A (en) | 2017-12-08 |
Family
ID=60488738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710540411.3A Pending CN107452065A (en) | 2017-07-05 | 2017-07-05 | The border spot identification method of surface sampled data in kind |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107452065A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241936A (en) * | 2018-09-26 | 2019-01-18 | 西安电子科技大学 | Calculation method based on space constraint and the semantic line segment of KD tree aggregation set |
| CN116542981A (en) * | 2023-07-06 | 2023-08-04 | 无锡晨智物联科技有限公司 | Quality assessment method for reference-point-free cloud |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104700458A (en) * | 2015-04-08 | 2015-06-10 | 山东理工大学 | Method for recognizing boundary sample point of sampling data on surface of material object |
| CN106127677A (en) * | 2016-06-22 | 2016-11-16 | 山东理工大学 | Surface in kind sampling point set boundary characteristic recognition methods based on fractional sample projected outline shape |
| CN106548484A (en) * | 2016-10-27 | 2017-03-29 | 济宁学院 | Product model dispersion point cloud Boundary characteristic extraction method based on two-dimentional convex closure |
| CN106778749A (en) * | 2017-01-11 | 2017-05-31 | 哈尔滨工业大学 | Based on the touring operating area boundary extraction method that concentration class and Delaunay triangles are reconstructed |
-
2017
- 2017-07-05 CN CN201710540411.3A patent/CN107452065A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104700458A (en) * | 2015-04-08 | 2015-06-10 | 山东理工大学 | Method for recognizing boundary sample point of sampling data on surface of material object |
| CN106127677A (en) * | 2016-06-22 | 2016-11-16 | 山东理工大学 | Surface in kind sampling point set boundary characteristic recognition methods based on fractional sample projected outline shape |
| CN106548484A (en) * | 2016-10-27 | 2017-03-29 | 济宁学院 | Product model dispersion point cloud Boundary characteristic extraction method based on two-dimentional convex closure |
| CN106778749A (en) * | 2017-01-11 | 2017-05-31 | 哈尔滨工业大学 | Based on the touring operating area boundary extraction method that concentration class and Delaunay triangles are reconstructed |
Non-Patent Citations (4)
| Title |
|---|
| 吴禄慎等: "一种基于散乱点云的边界提取算法", 《计算机应用与软件》 * |
| 孙殿柱等: "基于核密度估计的散乱点云边界特征提取", 《农业机械学报》 * |
| 李雯静等: "凸壳内缩法进行多密度离散点群边界检测", 《测绘科学》 * |
| 高尚兵等: "利用显著边界点的图像自动分割算法", 《电光与控制》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241936A (en) * | 2018-09-26 | 2019-01-18 | 西安电子科技大学 | Calculation method based on space constraint and the semantic line segment of KD tree aggregation set |
| CN109241936B (en) * | 2018-09-26 | 2022-10-11 | 西安电子科技大学 | Calculation method of semantic line segment aggregation set based on space constraint and KD tree |
| CN116542981A (en) * | 2023-07-06 | 2023-08-04 | 无锡晨智物联科技有限公司 | Quality assessment method for reference-point-free cloud |
| CN116542981B (en) * | 2023-07-06 | 2023-08-29 | 无锡晨智物联科技有限公司 | Quality assessment method for reference-point-free cloud |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109993748B (en) | Three-dimensional grid object segmentation method based on point cloud processing network | |
| CN109325993B (en) | Saliency feature enhanced sampling method based on class octree index | |
| CN106780458B (en) | Point cloud framework extraction method and device | |
| CN109166145B (en) | Fruit tree leaf growth parameter extraction method and system based on cluster segmentation | |
| CN104463164B (en) | It is a kind of based on umbrella frame method and crown height than trees canopy structure information extracting method | |
| CN107369161A (en) | A kind of workpiece point cloud segmentation method at random based on the European cluster of improvement | |
| CN101783016B (en) | Crown appearance extract method based on shape analysis | |
| CN103871100B (en) | Tree modelling method for reconstructing based on a cloud Yu data-driven | |
| CN107392875A (en) | A kind of cloud data denoising method based on the division of k neighbours domain | |
| CN101719140A (en) | Figure retrieving method | |
| CN107680168B (en) | Grid simplifying method based on plane fitting in three-dimensional reconstruction | |
| CN101477529A (en) | Three-dimensional object retrieval method and apparatus | |
| CN115661374B (en) | Rapid retrieval method based on space division and model voxelization | |
| CN104298990A (en) | Rapid graph matching and recognition method based on skeleton graphs | |
| CN102799763A (en) | Point cloud posture standardization-based method for extracting linear characteristic of point cloud | |
| CN106447777A (en) | Three-dimensional topological relation expressing and mapping achieved under support of Boolean operation | |
| CN107918953A (en) | The extracting method and device of laser scanning power line point cloud based on three dimensions | |
| CN106780748A (en) | A kind of quaternary tree index point cloud sort method based on grid association | |
| CN107452065A (en) | The border spot identification method of surface sampled data in kind | |
| CN102938165B (en) | Method for fairing product STL (Standard Template Library) model based on molded surface feature approximation | |
| CN113409332B (en) | Building plane segmentation method based on three-dimensional point cloud | |
| CN104821015B (en) | Surface sampling point α-shape curved surface topology rebuilding methods in kind | |
| CN107564024B (en) | SAR image aggregation region extraction method based on single-side aggregation line segment | |
| CN102679871B (en) | Rapid detection method of sub-pixel precision industrial object | |
| CN104700458B (en) | Surface sampled data in kind boundary spot identification method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20171208 |