A Method for Surface Reconstruction from Cloud Points Based on Segmented Support Vector Machine

Surface reconstruction based on Support Vector Machine (SVM) is a hot topic in the field of 3-dimension surface construction. But it is difficult to apply this method to cloud points. A reconstruction method based on segmented data is proposed to accelerate SVM regression process from cloud data. First, by partitioning the original sampling data set, several training data subsets and testing data subsets are generated. Some segmentation technique is adopted to keep the continuity on the borders. Then regression calculation is executed on every training subset to generate a SVM model, from which a segmented mesh is obtained according to the testing data subset. Finally, all the mesh surfaces are stitched into one whole surface. Theoretical analysis and experimental result show that the segmentation technique presented in this paper is efficient to improve the performance of the SVM regression, as well as keeps the continuity of the subset borders.