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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-29722

Pajarola, R; Miao, Y; Feng, J (2009). Curvature-aware adaptive re-sampling for point-sampled geometry. Computer-Aided Design, 41(6):395-403.

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Abstract

With the emergence of large-scale point-sampled geometry acquired by high-resolution 3D scanning devices, it has become increasingly important to develop efficient algorithms for processing such models which have abundant geometric details and complex topology in general. As a preprocessing step, surface simplification is important and necessary for the subsequent operations and geometric processing. Owing to adaptive mean-shift clustering scheme, a curvature-aware adaptive re-sampling method is proposed for point-sampled geometry simplification. The generated sampling points are non-uniformly distributed and can account for the local geometric feature in a curvature aware manner, i.e. in the simplified model the sampling points are dense in the high curvature regions, and sparse in the low curvature regions. The proposed method has been implemented and demonstrated by several examples.

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
DDC:000 Computer science, knowledge & systems
Uncontrolled Keywords:Point-sampled geometry; Adaptive re-sampling; Simplification; Curvature-aware; Mean-shift clustering
Language:English
Date:June 2009
Deposited On:08 Feb 2010 17:05
Last Modified:02 Dec 2013 19:06
Publisher:Elsevier
ISSN:0010-4485
Publisher DOI:10.1016/j.cad.2009.01.006
Citations:Web of Science®. Times Cited: 6
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