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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (3): 184-194    DOI: 10.1631/jzus.C1000068
    
Curvature-aware simplification for point-sampled geometry
Zhi-xun Su, Zhi-yang Li*, Yuan-di Zhao, Jun-jie Cao
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
Curvature-aware simplification for point-sampled geometry
Zhi-xun Su, Zhi-yang Li*, Yuan-di Zhao, Jun-jie Cao
School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
 全文: PDF(792 KB)  
摘要： We propose a novel curvature-aware simplification technique for point-sampled geometry based on the locally optimal projection (LOP) operator. Our algorithm includes two new developments. First, a weight term related to surface variation at each point is introduced to the classic LOP operator. It produces output points with a spatially adaptive distribution. Second, for speeding up the convergence of our method, an initialization process is proposed based on geometry-aware stochastic sampling. Owing to the initialization, the relaxation process achieves a faster convergence rate than those initialized by uniform sampling. Our simplification method possesses a number of distinguishing features. In particular, it provides resilience to noise and outliers, and an intuitively controllable distribution of simplification. Finally, we show the results of our approach with publicly available point cloud data, and compare the results with those obtained using previous methods. Our method outperforms these methods on raw scanned data.
关键词： Point-sampled geometry;  Particle simulation;  Locally optimal projection;  Simplification    
Abstract: We propose a novel curvature-aware simplification technique for point-sampled geometry based on the locally optimal projection (LOP) operator. Our algorithm includes two new developments. First, a weight term related to surface variation at each point is introduced to the classic LOP operator. It produces output points with a spatially adaptive distribution. Second, for speeding up the convergence of our method, an initialization process is proposed based on geometry-aware stochastic sampling. Owing to the initialization, the relaxation process achieves a faster convergence rate than those initialized by uniform sampling. Our simplification method possesses a number of distinguishing features. In particular, it provides resilience to noise and outliers, and an intuitively controllable distribution of simplification. Finally, we show the results of our approach with publicly available point cloud data, and compare the results with those obtained using previous methods. Our method outperforms these methods on raw scanned data.
Key words: Point-sampled geometry    Particle simulation    Locally optimal projection    Simplification
收稿日期: 2010-03-19 出版日期: 2011-03-09
CLC:  TP391.4  
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Zhi-xun Su
Zhi-yang Li
Yuan-di Zhao
Jun-jie Cao

引用本文:

Zhi-xun Su, Zhi-yang Li, Yuan-di Zhao, Jun-jie Cao. Curvature-aware simplification for point-sampled geometry. Front. Inform. Technol. Electron. Eng., 2011, 12(3): 184-194.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000068    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I3/184

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