Surface reconstruction from cloud points based on Support Vector Machine

Surface reconstruction based on Support Vector Machine (SVM) is a hot topic in the field of 3D surface construction. SVM based method for surface reconstruction can reduce the noise in the sampled data as well as repair the holes. However, the regress speed of SVM is too slow to reconstruct surface quickly from cloud points data set which has a lot of points. In this paper, a feature-preserved nonuniform simplification method for cloud points is presented, using which to simplifying points set. This method simplifies the data set to remove the redundancy while keeping down the features of the model. Then the surface is reconstructed from the simplified data using SVM. Both theoretical analysis and experimental results show that after the simplification, the performance of method for surface reconstruction based on SVM is improved greatly as well as the details of the surface are preserved well.

[1]  Jörg Peters,et al.  Local smooth surface interpolation: a classification , 1990, Comput. Aided Geom. Des..

[2]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[3]  Cai Yong.  Surface Reconstruction from Points Based on Support Vector Machine , 2007 .

[4]  Robert M. O'Bara,et al.  Geometrically deformed models: a method for extracting closed geometric models form volume data , 1991, SIGGRAPH.

[5]  Colin Bradley,et al.  Reverse Engineering Physical Models Employing Wrap-Around B-Spline Surfaces and Quadrics , 1996 .

[6]  Jörg Stiller.  Point-normal interpolation schemes reproducing spheres, cylinders and cones , 2007, Comput. Aided Geom. Des..

[7]  Zhou Ru-rong.  Simplification and Error Analysis Based on Implicit Surface for Measuring Point-sets , 2007 .

[8]  Mark Meyer,et al.  Implicit fairing of irregular meshes using diffusion and curvature flow , 1999, SIGGRAPH.

[9]  Geng Dong-fang.  Nonuniform simplification of cloud data based on octree , 2007 .

[10]  Bernhard Schölkopf,et al.  Kernel Methods for Implicit Surface Modeling , 2004, NIPS.

[11]  Markus Gross,et al.  Multiresolution modeling of point-sampled geometry , 2002 .

[12]  Zhang Kai.  A Delaunay-based Surface Reconstruction Algorithm for Unorganized Sampling Points , 2007 .

[13]  Wu Jian-jie.  Review of Surface Reconstruction Methods in Reverse Engineering , 2004 .

[14]  Xue Yan,et al.  Neural network approach to the reconstruction of freeform surfaces for reverse engineering , 1995, Comput. Aided Des..

[15]  Jon Louis Bentley,et al.  Multidimensional divide-and-conquer , 1980, CACM.

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  Zhou Ru-rong.  B-spline Surface Reconstruction from Scattered Data Points Based on SOM Neural Network , 2007 .