Feature sensitive re-sampling of point set surfaces with Gaussian spheres  

作　　者：MIAO YongWei BOSCH Jonas PAJAROLA Renato GOPI M. FENG JieQing 

机构地区：[1]College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China [2]Department of Informatics,University of Ziirich,Ziirich CH-8050,Switzerland [3]Department of Computer Science,University of California,Irvine CA 92697-3425,USA [4]State Key Laboratory of CAD&CG,Zhejiang University,Hangzhou 310027,China

出　　处：《Science China(Information Sciences)》2012年第9期2075-2089,共15页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61170138,60933007);Science and Technology Planning Project of Zhejiang Province (Grant No. 2009C34005);supported by Swiss National Science Foundation (Grant No. 200021-111746/1)

摘　　要：Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for maay computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification. Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner. The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme. A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme. Moreover, a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for maay computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling step by index propagation and a novel cluster optimization step by normalized rectification. Our proposed re-sampling scheme can generate non-uniformly distributed discrete sample points for the underlying point sets in a feature sensitive manner. The intrinsic geometric features of the underlying point set surfaces can be preserved efficiently due to our adaptive re-sampling scheme. A novel splat rendering technique is adopted to illustrate the efficiency of our re-sampling scheme. Moreover, a numerical error statistics and surface reconstruction for simplified models are also given to demonstrate the effectiveness of our algorithm in term of the simplified quality of the point set surfaces.

关 键 词：point set surfaces feature sensitive re-sampling Gaussian sphere SIMPLIFICATION surface recon- struction 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP391.41[自动化与计算机技术—计算机科学与技术]