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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Bibliographic Details
Published in:Science China. Information sciences Vol. 55; no. 9; pp. 2075 - 2089
Main Authors: Miao, YongWei, Bösch, Jonas, Pajarola, Renato, Gopi, M., Feng, JieQing
Format: Journal Article
Language:English
Published: Heidelberg SP Science China Press 01.09.2012
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
Bibliography: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
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ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-012-4637-0