CN101127123A - Sign point hole filling method based on neural network in tri-D scanning point cloud - Google Patents
Sign point hole filling method based on neural network in tri-D scanning point cloud Download PDFInfo
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- CN101127123A CN101127123A CNA2007101321122A CN200710132112A CN101127123A CN 101127123 A CN101127123 A CN 101127123A CN A2007101321122 A CNA2007101321122 A CN A2007101321122A CN 200710132112 A CN200710132112 A CN 200710132112A CN 101127123 A CN101127123 A CN 101127123A
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- In the 3-D scanning point cloud based on the complementing method of the monumented point hole of neural network, it is characterized in that:The first step: the hole that forms for the monumented point of object exterior pasting, in 3-D scanning point cloud, when obtaining on every side sample point set of hole, according to the three-dimensional coordinate (x of monumented point b, y b, z b), will be that data point in the cube at center is as the sample point P of perforations adding with the monumented point s, s=0 wherein, 1 ..., t, the three-dimensional coordinate on cubical 8 summits is respectively (x b-r, y b-r, z b-r), (x b-r, y b-r, z b+ r), (x b-r, y b+ r, z b-r), (x b-r, y b+ r, z b+ r), (x b+ r, y b-r, z b-r), (x b+ r, y b-r, z b+ r), (x b+ r, y b+ r, z b-r), (x b+ r, y b+ r, z b+ r), wherein r is the thresholding of being got, and gets 1.2~1.5 times of radius of monumented point:Second the step: according to sample points according to P sThe neural network of training perforations adding, the neural network that is used to fill up hole is three layers a reverse transmittance nerve network, its input layer is 2 neurons, the x coordinate figure and the y coordinate figure of the corresponding sample data of difference, hidden layer is a m neuron, and output layer is the z coordinate figure of 1 neuron and corresponding sample data, and the excitation function of hidden layer and output layer is respectively the tanh sigmoid functionThe 3rd step: after obtaining neural network net, choose resample points,, calculate all sample point P for density and its ambient data that incomplete zone is heavily adopted is consistent along step-lengths such as x and y directions s, s=0 wherein, 1 ..., t, as the step-length L that resamples, the scope of resampling is interval (x at the mean distance of the point of xoy plane projection and on every side minor increment Min, x Max), (y Min, y Max), x wherein Min, x MaxBe minimum value and the maximal value of the coordinate x of sample point, y Min, y MaxBe minimum value and the maximal value of the coordinate y of sample point, heavily adopt ading up to a little Wherein " Expression rounds downwards, and the x coordinate figure of resample points is from x MinBeginning with step-length L equidistant increase until The y coordinate figure of resample points is from y MinBeginning with step-length L equidistant increase until With the x coordinate figure and the y of the point that resamples, coordinate figure just can obtain resample points z coordinate figure as the input of neural network, and then obtains all resample points;The 4th step: the curvature according to resample points is added sampled point, when asking for discrete space point curvature, all resample points are projected to the xoy plane, by the 3rd step as can be known, all subpoints, equidistantly distribute with spacing L along x and y direction, get ask point in 5 * 5 neighborhoods on the xoy plane a little as the neighborhood point, remove totally 24 point itself,, obtain the curvature ρ of all resample points according to existing method as a little k neighborhood of ask s, s=0 wherein, 1 ..., n-1 is according to given curvature thresholding ρ τ, ρ τGet 0.1, get curvature greater than ρ τResample points, obtain the deep camber point, around deep camber point, increase sampled point then, the strategy of taking is, all resample points are projected on the xoy plane, supposes that it is (x that the coordinate of deep camber point subpoint on the xoy plane is arranged ρ, y ρ), check that it is along x four the subpoint (xs adjacent with the y direction ρ-L, y ρ), (x ρ+ L, y ρ), (x ρ, y ρ-L), (x ρ, y ρ+ L), if the deep camber point is wherein also arranged, then two resample points of equidistant increase between deep camber point are without loss of generality, and suppose to project to (x ρ+ L, y ρ) point of coordinate is the deep camber point, then increase 2 points, all the other are not on the direction of deep camber point, increase a bit, (the x that projects to as described above ρ-L, y ρ), (x ρ, y ρ-L), (x ρ, y ρ+ L) 3 are not the deep camber points, then increase 3 points, adopt above strategy to mend point at all deep camber points, at last among the neural network net that the x of the point of all benefits and second step of y coordinate figure input are obtained, the z coordinate figure of asking, all resample points have so just been obtained according to the curvature increase, resample points together with obtaining in the 3rd step obtains final resample points;The 5th step: with in the final resample points with 3-D scanning point cloud in the distance of closest approach less than 0.5d 3DResample points give up, remaining resample points is added in the 3-D scanning point cloud the filling up of complement mark point hole, wherein d 3DBe the density of 3-D scanning point cloud, d 3DBe set to the mean value of the each point of a cloud and minor increment on every side.
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CNB2007101321122A CN100561521C (en) | 2007-09-11 | 2007-09-11 | In the 3-D scanning point cloud based on the complementing method of the monumented point hole of neural network |
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CNB2007101321122A CN100561521C (en) | 2007-09-11 | 2007-09-11 | In the 3-D scanning point cloud based on the complementing method of the monumented point hole of neural network |
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Cited By (11)
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CN104063898A (en) * | 2014-06-30 | 2014-09-24 | 厦门大学 | Three-dimensional point cloud auto-completion method |
CN106033620A (en) * | 2015-03-13 | 2016-10-19 | 腾讯科技(深圳)有限公司 | Point clouds model restoration method, point clouds model restoration device and calculation device |
CN107464223A (en) * | 2017-07-19 | 2017-12-12 | 西安理工大学 | A kind of dot cloud hole method for repairing and mending based on section |
CN107507127A (en) * | 2017-08-04 | 2017-12-22 | 深圳市易尚展示股份有限公司 | The global registration method and system of multiple views three-dimensional point cloud |
CN107610061A (en) * | 2017-08-30 | 2018-01-19 | 西安理工大学 | A kind of guarantor's edge point cloud hole repair method based on two-dimensional projection |
CN108399609A (en) * | 2018-03-06 | 2018-08-14 | 北京因时机器人科技有限公司 | A kind of method for repairing and mending of three dimensional point cloud, device and robot |
CN109670558A (en) * | 2017-10-16 | 2019-04-23 | 奥多比公司 | It is completed using the digital picture of deep learning |
CN109961517A (en) * | 2019-03-01 | 2019-07-02 | 浙江大学 | A kind of triangle gridding weight parametric method for parametric surface fitting |
CN110091505A (en) * | 2019-04-26 | 2019-08-06 | 宁波心思为三维科技有限公司 | A kind of intelligence control system based on 3D printer |
CN112991522A (en) * | 2021-03-30 | 2021-06-18 | 华南理工大学 | Personalized automatic modeling method, system and equipment for mitral valve |
CN113593011A (en) * | 2021-07-12 | 2021-11-02 | 杭州思锐迪科技有限公司 | Hole repairing method, electronic device and storage medium |
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2007
- 2007-09-11 CN CNB2007101321122A patent/CN100561521C/en not_active Expired - Fee Related
Cited By (19)
Publication number | Priority date | Publication date | Assignee | Title |
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CN104063898B (en) * | 2014-06-30 | 2017-05-03 | 厦门大学 | Three-dimensional point cloud auto-completion method |
CN104063898A (en) * | 2014-06-30 | 2014-09-24 | 厦门大学 | Three-dimensional point cloud auto-completion method |
CN106033620A (en) * | 2015-03-13 | 2016-10-19 | 腾讯科技(深圳)有限公司 | Point clouds model restoration method, point clouds model restoration device and calculation device |
CN106033620B (en) * | 2015-03-13 | 2018-10-19 | 腾讯科技(深圳)有限公司 | A kind of point cloud model restorative procedure, device and computing device |
CN107464223B (en) * | 2017-07-19 | 2020-01-14 | 西安理工大学 | Point cloud hole repairing method based on slices |
CN107464223A (en) * | 2017-07-19 | 2017-12-12 | 西安理工大学 | A kind of dot cloud hole method for repairing and mending based on section |
CN107507127A (en) * | 2017-08-04 | 2017-12-22 | 深圳市易尚展示股份有限公司 | The global registration method and system of multiple views three-dimensional point cloud |
CN107507127B (en) * | 2017-08-04 | 2021-01-22 | 深圳市易尚展示股份有限公司 | Global matching method and system for multi-viewpoint three-dimensional point cloud |
CN107610061B (en) * | 2017-08-30 | 2020-02-14 | 西安理工大学 | Edge-preserving point cloud hole repairing method based on two-dimensional projection |
CN107610061A (en) * | 2017-08-30 | 2018-01-19 | 西安理工大学 | A kind of guarantor's edge point cloud hole repair method based on two-dimensional projection |
CN109670558A (en) * | 2017-10-16 | 2019-04-23 | 奥多比公司 | It is completed using the digital picture of deep learning |
CN109670558B (en) * | 2017-10-16 | 2024-01-12 | 奥多比公司 | Digital image completion using deep learning |
CN108399609A (en) * | 2018-03-06 | 2018-08-14 | 北京因时机器人科技有限公司 | A kind of method for repairing and mending of three dimensional point cloud, device and robot |
CN108399609B (en) * | 2018-03-06 | 2020-07-31 | 北京因时机器人科技有限公司 | Three-dimensional point cloud data repairing method and device and robot |
CN109961517A (en) * | 2019-03-01 | 2019-07-02 | 浙江大学 | A kind of triangle gridding weight parametric method for parametric surface fitting |
CN110091505A (en) * | 2019-04-26 | 2019-08-06 | 宁波心思为三维科技有限公司 | A kind of intelligence control system based on 3D printer |
CN112991522A (en) * | 2021-03-30 | 2021-06-18 | 华南理工大学 | Personalized automatic modeling method, system and equipment for mitral valve |
CN112991522B (en) * | 2021-03-30 | 2023-03-24 | 华南理工大学 | Personalized automatic modeling method, system and equipment for mitral valve |
CN113593011A (en) * | 2021-07-12 | 2021-11-02 | 杭州思锐迪科技有限公司 | Hole repairing method, electronic device and storage medium |
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