JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 4 Issue : 9 Date : September 2009
A Method for Surface Reconstruction Based on Support Vector Machine
Lianwei Zhang, Wei Wang, Yan Li, Xiaolin Liu, Meiping Shi, and Hangen He
Full Text: PDF (872 KB)
Surface reconstruction is one of the main parts of reverse engineering and environment modeling.
In this paper a method for reconstruct surface based on Support Vector Machine (SVM) is proposed.
In order to overcome the inefficiency of SVM, a feature-preserved nonuniform simplification method
is employed to simplify cloud points set. The points set is reduced while the feature is preserved
after simplification. Then a reconstruction method based on segmented data is proposed to
accelerate SVM regression process for cloud data. Firstly, the original sampling data set is
partitioned to generate several training data subsets and testing data subsets. A segmentation
technique is adopted to keep the continuity on the borders. Secondly 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. Both theoretical analysis and experimental result show that the segmentation
technique presented in this paper is efficient to improve the performance of the SVM regression,
while keeping the continuity of the subset borders.
Surface reconstruction, surface variance, support vector machine, segmentation