CN106504332A - The curve reestablishing method and device of three-dimensional point cloud - Google Patents
The curve reestablishing method and device of three-dimensional point cloud Download PDFInfo
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Abstract
The embodiment of the invention discloses a kind of curve reestablishing method of three-dimensional point cloud and device.Methods described includes：Smooth resampling is carried out to cloud data；Space down-sampling is carried out to the cloud data；Curved surface trigonometric ratio is carried out to the cloud data.The curve reestablishing method and device of three-dimensional point cloud provided in an embodiment of the present invention improve the operational efficiency of curve reestablishing process.
Description
Technical field
The present embodiments relate to three-dimensional reconstruction field, more particularly to a kind of curve reestablishing method of three-dimensional point cloud and
Device.
Background technology
The three-dimensional reconstruction grid reconstruction technology that is otherwise known as in the field of business, or curve reestablishing technology.It is referred to given
One group of spatial data points X at random, and on the premise of these spatial data points known are located at unknown curved surface U, try to achieve one three
Angle grid surface S so that this triangle mesh curved surface S can preferably approach former curved surface U.Three-dimensional reconstruction reverse-engineering,
The fields such as medical scanning data three-dimensional imaging, interaction curved surface modeling, three-dimensional fax have a wide range of applications.
Trigonometric ratio is an important operation in three-dimensional reconstruction.Existing trigonometric ratio process mainly has：Delaunay
Triangulation Algorithm, greedy projection Triangulation Algorithm, and Octree algorithm.No matter which kind of algorithm is adopted, due to acquired original number
At random, uniform intensive point according to having, the presence redundancy that various algorithms can be different degrees of when carrying out three-dimensional reconstruction, amount of calculation compared with
Greatly, the not high disadvantage of operational efficiency.
Content of the invention
For above-mentioned technical problem, a kind of curve reestablishing method of three-dimensional point cloud and device is embodiments provided,
To improve the operational efficiency of curve reestablishing.
On the one hand, a kind of curve reestablishing method of three-dimensional point cloud is embodiments provided, and methods described includes：
Smooth resampling is carried out to cloud data；
Space down-sampling is carried out to smoothing the cloud data after resampling；
Curved surface trigonometric ratio is carried out to cloud data after down-sampling.
On the other hand, the embodiment of the present invention additionally provides a kind of curve reestablishing device of three-dimensional point cloud, and described device includes：
Smooth resampling module, for carrying out smooth resampling to cloud data；
Down sample module, for carrying out space down-sampling to the cloud data；
Trigonometric ratio module, for carrying out curved surface trigonometric ratio to the cloud data.
The curve reestablishing method and device of three-dimensional point cloud provided in an embodiment of the present invention, by smoothing to cloud data
Resampling, carries out space down-sampling to smoothing the cloud data after resampling, and to down-sampling after cloud data march
Face trigonometric ratio, improves the operational efficiency of curve reestablishing process.
Description of the drawings
By reading the detailed description made by non-limiting example that is made with reference to the following drawings, other of the invention
Feature, objects and advantages will become more apparent upon：
Fig. 1 is the flow chart of the curve reestablishing method of the three-dimensional point cloud that first embodiment of the invention is provided；
Fig. 2 is the flow chart of the curve reestablishing method of the three-dimensional point cloud that second embodiment of the invention is provided；
Fig. 3 be third embodiment of the invention provide three-dimensional point cloud curve reestablishing method in discrete point reject process stream
Cheng Tu；
Fig. 4 be fourth embodiment of the invention provide three-dimensional point cloud curve reestablishing method in smooth resampling process stream
Cheng Tu；
Fig. 5 be fifth embodiment of the invention provide three-dimensional point cloud curve reestablishing method in down-sampling process flow process
Figure；
Fig. 6 is the flow process that the curve reestablishing method intermediate camization of the three-dimensional point cloud that sixth embodiment of the invention is provided is processed
Figure；
Fig. 7 is the structure chart of the curve reestablishing device of the three-dimensional point cloud that seventh embodiment of the invention is provided.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment that states is used only for explaining the present invention, rather than limitation of the invention.It also should be noted that, in order to just
Part related to the present invention rather than entire infrastructure is illustrate only in description, accompanying drawing.
First embodiment
Present embodiments provide a kind of technical scheme of the curve reestablishing method of three-dimensional point cloud.In the technical scheme, should
The curve reestablishing method of three-dimensional point cloud includes：Smooth resampling is carried out to cloud data；Space down-sampling is carried out to cloud data；
Curved surface trigonometric ratio is carried out to cloud data.
Referring to Fig. 1, the curve reestablishing method of the three-dimensional point cloud includes：
S11, carries out smooth resampling to cloud data.
It is understood that the cloud data of acquired original does not have through any process.Directly using such original
Beginning data carry out surface model, and the surface of the curved surface that its result is generated occurs the so rough phenomenon of hole, projection unavoidably.
In the curve reestablishing method that the present embodiment is provided, smooth resampling is carried out first to original cloud data.Through
After crossing above-mentioned smooth resampling, by resampling after the curved surface that constitutes of cloud data there is more smooth surface, it is not necessary to
Individually the abnormal cases such as the hole on surface, projection are repaired again.
Preferably, during smooth resampling, using Moving Least Squares algorithm, original point cloud data is put down
Sliding resampling.
In addition, in the present embodiment, cloud data can be laser radar LiDAR point cloud data, or multi views
Three-dimensional (Multi-view Stereo, MVS) cloud data.
S12, carries out space down-sampling to the cloud data.
In general, the data volume of original cloud data is magnanimity.This point is embodied in first, original point cloud data
The number of the location point for including is magnanimity.And in actual curved surface determines, the presence of not all point is all very heavy
Will.Therefore, in order to reduce the amount of calculation of curve reestablishing, the operational efficiency of curve reestablishing is improved, cloud data is carried out under space
Sampling.
Preferably, when space down-sampling is carried out to cloud data, using previously given voxel grid to described cloud
Data carry out space down-sampling.
S13, carries out curved surface trigonometric ratio to the cloud data.
After space down-sampling is carried out to cloud data, the data volume of cloud data substantially reduces.Now, to a cloud
Data carry out curved surface trigonometric ratio, and the operational efficiency of curve reestablishing will be greatly improved.
Preferably, when curved surface trigonometric ratio being carried out to the cloud data, estimate indicator function and the extraction of model first
Contour surface, then curved surface trigonometric ratio is completed by solving poisson problem.
The present embodiment carries out space down-sampling by carrying out smooth resampling to cloud data to the cloud data, with
And curved surface trigonometric ratio is carried out to the cloud data, significantly improve the operational efficiency of curve reestablishing.
Second embodiment
The present embodiment further provides the curve reestablishing method of three-dimensional point cloud based on the above embodiment of the present invention
Another kind of technical scheme.In the technical scheme, the curve reestablishing method of the three-dimensional point cloud also includes：To described cloud
Before data carry out smooth resampling, the outlier in cloud data is rejected.
Referring to Fig. 2, the curve reestablishing method of the three-dimensional point cloud includes：
S21, rejects the outlier in cloud data.
Due to producing the cloud data collection of Density inhomogeneity during laser scanning, and the error in measuring can be produced
The sparse outlier of life.Outlier refers to some discrete points apart from each other with testee point cloud.The presence of outlier can
Can increase the amount of calculation of curve reestablishing process, in addition be also possible to the numerical value for also resulting in mistake, be likely to result in turn a little
Cloud procedure failure.Therefore, in the present embodiment, before the curve reestablishing process of normal operation, first in cloud data
Middle rejecting outlier.
Specifically, according to each spatial point in the cloud data with the mean space between its adjacent point of proximity away from
From, and gaussian distribution characteristic is judging in the cloud data which point is outlier.More specifically, point cloud number is calculated first
Average distance according between each adjacent point of point.Then by carrying out above-mentioned average distance with default distance threshold
Relatively, judge which discrete point is outlier.Finally the outlier that assert is rejected from the cloud data.
S22, carries out smooth resampling to cloud data.
S23, carries out space down-sampling to the cloud data.
S24, carries out curved surface trigonometric ratio to the cloud data.
The present embodiment is by, before smooth resampling is carried out to cloud data, rejecting peeling off in the cloud data
Point so that no longer include outlier in the sample cloud data adopted when rebuilding to curved surface, so that curve reestablishing
Amount of calculation less, operational efficiency is higher.
3rd embodiment
The present embodiment further provides the curve reestablishing method of three-dimensional point cloud based on the above embodiment of the present invention
A kind of technical scheme that middle discrete point is rejected.In the technical scheme, the outlier that rejects in the cloud data includes：Calculate
Average distance in the cloud data between each adjacent point of point；If the average distance is beyond default apart from threshold
Value, judges the point as outlier；The outlier is rejected from the cloud data.
Referring to Fig. 3, the outlier that rejects in the cloud data includes：
S31, calculates the average distance between each adjacent point of point in the cloud data.
Specifically, above-mentioned average distance is calculated according to equation below：
Wherein, K is point PiPoint of proximity number in three dimensions, diIt is discrete point PiWith its facing in three dimensions
Average distance between near point, PjIt is then above-mentioned point PiPoint of proximity in three dimensions.
S32, if the average distance judges the point as outlier beyond default distance threshold.
In general, in space, the average distance of each adjacent point of point assumes Gauss distribution.According to average distance
Above-mentioned statistical nature, calculates the average and variance of above-mentioned average distance first.Specifically, average distance is calculated according to equation below
Mean μ：
Wherein, diIt is spatial point PiAverage distance, N is the total number of described centrostigma.
In addition, calculating the standard deviation sigma of average distance according to equation below：
Wherein, diIt is spatial point PiAverage distance, the sum at N institute's pointed sets midpoint, μ is the average of the average distance.
Further, meet spatial point P such as lower inequalityiOutlier can be identified as：
|di-μ|>λσ
Wherein, diIt is spatial point PiAverage distance, μ is the average of the average distance, and σ is the standard deviation of average distance,
λ is threshold coefficient.And, the value of threshold coefficient is more than zero.In general, the value of λ is bigger, the outlier that identification is obtained
Quantity is fewer.
S33, the outlier is rejected from the cloud data.
Due to identifying outlier from cloud data, it is possible to from cloud data pick above-mentioned outlier
Remove.
The present embodiment passes through to calculate the average distance in the cloud data between each adjacent point of point, if described flat
Distance judges the discrete point as outlier beyond default distance threshold, and by the outlier from described cloud number
According to middle rejecting, it is achieved that the rejecting from cloud data to outlier.
Fourth embodiment
The present embodiment further provides the curve reestablishing method of three-dimensional point cloud based on the above embodiment of the present invention
A kind of technical scheme of middle resampling.In the technical scheme, carrying out smooth resampling to cloud data includes：Calculate point to converge
In each point normal vector；Using MLS, surface fitting is carried out to cloud data according to custom parameter.
Referring to Fig. 4, carrying out smooth resampling to cloud data includes：
S41, calculates the normal vector of each point during point converges.
Preferably, in being converged according to the point, the point of proximity of each point position each other, tries to achieve the normal vector of the point.
Specifically, the spatial point that a spatial point is adjacent is found out, covariance matrix is calculated according to this point and its neighbor point then, will
The normal vector that characteristic vector in covariance matrix corresponding to minimal eigenvalue is put as this.
S42, using MLS algorithms, carries out surface fitting according to custom parameter to cloud data.
In the present embodiment, surface fitting is carried out using MLS algorithms, obtain being fitted later curved surface.Intend in above-mentioned curved surface
During conjunction, need using some custom parameters.Exemplary, these custom parameters include：Fitting of a polynomial number of times,
Gauss weight coefficient etc..Concentrating in each plane local neighborhood defined in point, uniformly taken a little according to dot density, to take out
Point on fitting surface be projected as resampling after point.
The present embodiment passes through the normal vector for calculating each point during point converges, using MLS algorithms, according to custom parameter to point
Cloud data carry out fitting of a polynomial, achieve the resampling to cloud data by top sampling method.
5th embodiment
The present embodiment further provides the curve reestablishing method of three-dimensional point cloud based on the above embodiment of the present invention
A kind of technical scheme of middle down-sampling.In the technical scheme, carrying out space down-sampling to the cloud data includes：Set up by
The three-dimensional voxel grid of 3D bounding boxs composition, and by the institute in each bounding box a little by the point set in this bounding box
Center of gravity come approximate.
Referring to Fig. 5, carrying out space down-sampling to the cloud data includes：
S51, sets up the three-dimensional voxel grid being made up of 3D bounding boxs.
In the present embodiment, the space down-sampling of cloud data is carried out by a given three-dimensional voxel grid.Specifically
, initially set up a three-dimensional voxel grid.The three-dimensional voxel grid is made up of the 3D bounding boxs of one group of very little.Each 3D bag
Enclose box and there is certain volume in space, occupy certain space.And, during the foundation of three-dimensional voxel grid, 3D
The volume size of bounding box is contemplated that the spatial density of whole cloud data.
S52, by each bounding box a little come by the center of gravity of the point set being located in this bounding box approximate.
After completing the foundation of above-mentioned three-dimensional voxel grid, down-sampling is carried out to cloud data.Specifically, above-mentioned three
The spatial point of the cloud data in one 3D bounding box of dimension voxel grid, asks for their center of gravity, then using asking for obtaining
Center of gravity replace all spatial point in the bounding box.It should be noted that during center of gravity is asked for, in a bounding box
Spatial point there is identical status, can't be because position be different for the different weight of different spatial point applications.
The three-dimensional voxel grid that the present embodiment foundation is made up of 3D bounding boxs, and by the institute in each bounding box a little
Come by the center of gravity of the point set being located in this bounding box approximate, it is achieved that the space down-sampling to cloud data.
Sixth embodiment
The present embodiment further provides the curve reestablishing method of three-dimensional point cloud based on the above embodiment of the present invention
A kind of technical scheme of intermediate cam.In the technical scheme, carrying out Poisson curve reestablishing to the cloud data includes：Estimate
The indicator function of point cloud model and extraction contour surface；Solve poisson problem and complete curve reestablishing.
Referring to Fig. 6, carrying out curved surface trigonometric ratio to the cloud data includes：
S61, estimates the indicator function of point cloud model and extracts contour surface.
If point is located in model, indicator function is defined as 1, otherwise, is defined as 0.Isosurface extraction method is：First, select
Select an equivalence so that then the contour surface of extraction is carried by calculating indicator function approaching to the position of sample point
Take corresponding contour surface.
S62, solves poisson problem and completes curve reestablishing.
Poisson curve reestablishing is mainly included the following steps that：Define gradient fields, estimate vector field and solution poisson problem.Poisson is bent
The process that face rebuilds, actually makes the gradient fields of reconstruction curved surface approach the process of vector field.When gradient fields and original vector
When difference between is minimum, you can think the process of reconstruction for completing above-mentioned Poisson curved surface.
The present embodiment carries out curve reestablishing by Poisson curve reestablishing to the cloud data after down-sampling.The method is
A kind of triangle gridding algorithm for reconstructing based on implicit function, such method by cloud data is carried out optimized interpolation processing come
Obtain Proximal surface, it is achieved that the curve reestablishing to cloud data.
7th embodiment
Present embodiments provide a kind of technical scheme of the curve reestablishing device of three-dimensional point cloud.In the technical scheme, institute
The curve reestablishing device for stating three-dimensional point cloud includes：Resampling module 72, down sample module 73, and trigonometric ratio module 74.
The resampling module 72 is used for carrying out cloud data smooth resampling.
The down sample module 73 is used for carrying out space down-sampling to the cloud data.
The trigonometric ratio module 74 is used for carrying out curved surface trigonometric ratio to the cloud data.
Preferably, the curve reestablishing device of the three-dimensional point cloud also includes：Outlier rejects module 71.
The outlier rejects module 71 to be used for before smooth resampling is carried out to the cloud data, rejected the point
Outlier in cloud data.
Preferably, the resampling module 72 specifically for：Using Moving Least Squares algorithm, the cloud data is entered
The smooth resampling of row.
Preferably, the down sample module 73 specifically for：The cloud data is carried out using given voxel grid
Space down-sampling.
Preferably, the trigonometric ratio module 74 specifically for：Using Poisson curve reestablishing algorithm, the cloud data is entered
Row curved surface trigonometric ratio.
Preferably, the outlier is rejected module and is included：Average distance computing unit, outlier identifying unit, and pick
Remove unit.
The average distance computing unit is average between each adjacent point of point in the cloud data for calculating
Distance.
The outlier identifying unit be used for when the average distance beyond when default distance threshold, judge described from
Scatterplot is outlier.
The culling unit is used for rejecting the outlier from the cloud data.
Preferably, the cloud data includes：Laser radar LiDAR point cloud data, or multi views solid MVS point cloud numbers
According to.
The preferred embodiments of the present invention are the foregoing is only, the present invention is not limited to, for those skilled in the art
For, the present invention can have various changes and change.All any modifications that is made within spirit and principles of the present invention, equivalent
Replace, improve etc., should be included within the scope of the present invention.
Claims (10)
1. a kind of curve reestablishing method of three-dimensional point cloud, it is characterised in that include：
Smooth resampling is carried out to cloud data；
Space down-sampling is carried out to the cloud data；
Curved surface trigonometric ratio is carried out to the cloud data.
2. method according to claim 1, it is characterised in that also include：
Before smooth resampling is carried out to the cloud data, reject the outlier in the cloud data.
3. method according to claim 1 and 2, it is characterised in that carrying out smooth resampling to cloud data includes：
Using Moving Least Squares algorithm, smooth resampling is carried out to the cloud data.
4. method according to claim 1 and 2, it is characterised in that carrying out space down-sampling to the cloud data includes：
Space down-sampling is carried out to the cloud data using given voxel grid.
5. method according to claim 1 and 2, it is characterised in that carrying out curved surface trigonometric ratio to the cloud data includes：
Using Poisson curve reestablishing algorithm, curved surface trigonometric ratio is carried out to the cloud data.
6. method according to claim 2, it is characterised in that the outlier that rejects in the cloud data includes：
Calculate the average distance between each adjacent point set of point in the cloud data；
If the corresponding average distance of this point judges the point as outlier beyond default distance threshold；
The outlier is rejected from the cloud data.
7. method according to claim 1 and 2, it is characterised in that the cloud data includes：Laser radar LiDAR point
Cloud data, or multi views solid MVS cloud datas.
8. the curve reestablishing device of a kind of three dimensional point cloud, it is characterised in that include：
Smooth resampling module, for carrying out smooth resampling to cloud data；
Down sample module, for carrying out space down-sampling to the cloud data；
Trigonometric ratio module, for carrying out curved surface trigonometric ratio to the cloud data.
9. device according to claim 8, it is characterised in that also include：
Outlier rejects module, for, before smooth resampling is carried out to the cloud data, rejecting in the cloud data
Outlier.
10. device according to claim 8 or claim 9, it is characterised in that the cloud data includes：Laser radar LiDAR point
Cloud data, or multi views solid MVS cloud datas.
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| CN106960470A (en) * | 2017-04-05 | 2017-07-18 | 未来科技（襄阳）有限公司 | Three-dimensional point cloud curve reestablishing method and device |
| CN106960470B (en) * | 2017-04-05 | 2022-04-22 | 未来科技（襄阳）有限公司 | Three-dimensional point cloud curved surface reconstruction method and device |
| CN107747906A (en) * | 2017-11-09 | 2018-03-02 | 中国电建集团成都勘测设计研究院有限公司 | A kind of volume measuring method of testing pits for rolling earth-rock dam |
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| CN109389672A (en) * | 2018-09-29 | 2019-02-26 | 广东工业大学 | A kind of processing method of reflective workpiece point cloud |
| CN109389672B (en) * | 2018-09-29 | 2023-03-21 | 广东工业大学 | Processing method of point cloud of reflective workpiece |
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| CN111801707A (en) * | 2019-02-02 | 2020-10-20 | 深圳市大疆创新科技有限公司 | Method, device and system for point cloud resampling |
| WO2020187140A1 (en) * | 2019-03-15 | 2020-09-24 | Mediatek Inc. | Method and apparatus of patch segmentation for video-based point cloud coding |
| CN115564820A (en) * | 2022-11-10 | 2023-01-03 | 武汉工程大学 | Volume determination method, system, device and medium based on greedy projection triangularization |
| CN116993915A (en) * | 2023-08-08 | 2023-11-03 | 北京长木谷医疗科技股份有限公司 | Bone joint model reconstruction method, device and equipment based on greedy projection |
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