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CLC number: TP391.4

On-line Access: 2011-03-09

Received: 2010-03-19

Revision Accepted: 2010-07-16

Crosschecked: 2011-01-31

Cited: 1

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.3 P.184-194


Curvature-aware simplification for point-sampled geometry

Author(s):  Zhi-xun Su, Zhi-yang Li, Yuan-di Zhao, Jun-jie Cao

Affiliation(s):  School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China

Corresponding email(s):   zxsu@hotmail.com, lizy0205@gmail.com

Key Words:  Point-sampled geometry, Particle simulation, Locally optimal projection, Simplification

Zhi-xun Su, Zhi-yang Li, Yuan-di Zhao, Jun-jie Cao. Curvature-aware simplification for point-sampled geometry[J]. Journal of Zhejiang University Science C, 2011, 12(3): 184-194.

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%A Zhi-xun Su
%A Zhi-yang Li
%A Yuan-di Zhao
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T1 - Curvature-aware simplification for point-sampled geometry
A1 - Zhi-xun Su
A1 - Zhi-yang Li
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A1 - Jun-jie Cao
J0 - Journal of Zhejiang University Science C
VL - 12
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EP - 194
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Y1 - 2011
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000068

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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