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

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-s...

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Vydané v:	Science China. Information sciences Ročník 55; číslo 9; s. 2075 - 2089
Hlavní autori:	Miao, YongWei, Bösch, Jonas, Pajarola, Renato, Gopi, M., Feng, JieQing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Heidelberg SP Science China Press 01.09.2012 Springer Nature B.V
Predmet:	Adaptive sampling Algorithms Computer graphics Computer Science Gaussian Information Systems and Communication Service Mathematical models Reconstruction Rendering Research Paper Samples Sampling Statistical methods 几何特征敏感点集表面法线计算机图形学采样方案重采样高斯球 surface reconstruction point set surfaces feature sensitive re-sampling simplification Gaussian sphere
ISSN:	1674-733X, 1869-1919
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Abstract	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.
AbstractList	Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many 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.
Author	MIAO YongWei BOSCH Jonas PAJAROLA Renato GOPI M. FENG JieQing
AuthorAffiliation	College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China Department of Informatics, University of Ziirich, Ziirich CH-8050, Switzerland Department of Computer Science, University of California, Irvine CA 92697-3425, USA State Key Laboratory of CAD ＆ CG, Zhejiang University, Hangzhou 310027, China
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Cites_doi	10.1016/j.cag.2004.08.014 10.1016/j.cag.2006.11.012 10.1109/PROC.1984.13073 10.1109/TVCG.2007.19 10.1016/j.cad.2009.01.006 10.1007/s00371-009-0351-3 10.1109/TVCG.2003.1175093 10.1111/j.1467-8659.2008.01163.x 10.1109/TVCG.2004.19 10.1145/1015706.1015713 10.1111/1467-8659.00236 10.1016/j.cad.2006.04.007 10.1145/1015706.1015768 10.1016/j.cag.2004.08.009 10.1007/s11432-010-0068-y 10.1111/1467-8659.00532 10.1145/882262.882293 10.1145/566654.566584 10.1111/j.1467-8659.2004.00796.x 10.1145/1015706.1015817 10.1007/978-1-4757-1904-8 10.1007/978-3-540-24673-2_45 10.1145/383259.383266 10.1109/SMI.2009.5170160 10.1109/PBG.2005.194059 10.1145/882262.882296 10.1109/PBG.2005.194060 10.1109/SMI.2003.1199611 10.1145/258734.258849 10.1109/VISUAL.1999.809869
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Notes	11-5847/TP 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. point set surfaces, feature sensitive re-sampling, Gaussian sphere, simplification, surface recon- struction ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
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Snippet	Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for maay computer graphics and geometric modeling... Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling...
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SubjectTerms	Adaptive sampling Algorithms Computer graphics Computer Science Gaussian Information Systems and Communication Service Mathematical models Reconstruction Rendering Research Paper Samples Sampling Statistical methods 几何特征敏感点集表面法线计算机图形学采样方案重采样高斯球
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Title	Feature sensitive re-sampling of point set surfaces with Gaussian spheres
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