Paper Info
Title
A Method for Surface Reconstruction from Cloud Points Based on Segmented Support Vector Machine
Abstract
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.
Year
DOI
Venue
2008
10.1109/ICYCS.2008.518
ICYCS
Keywords
Field
DocType
svm regression process,segmented data,surface reconstruction,svm regression,original sampling data,training data subsets,svm model,testing data subset,vector machine,segmentation technique,segmented support,cloud data,testing data subsets,support vector machines,regression analysis,testing,computer graphics,image reconstruction,support vector machine,training data,cloud point
Iterative reconstruction,Data mining,Surface reconstruction,Pattern recognition,Computer science,Segmentation,Support vector machine,Test data,Artificial intelligence,Sampling (statistics),Computer graphics,Cloud computing
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
6
Name
Order
Citations
PageRank
Lian-Wei Zhang141.16
Yan Li271.97
Jinze Song3395.26
Meiping Shi451.22
Xiaolin Liu5183.34
Hangen He630723.86