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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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.
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|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||03 Faculty of Economics > Department of Informatics|
|Dewey Decimal Classification:||000 Computer science, knowledge & systems|
|Uncontrolled Keywords:||Point-sampled geometry; Adaptive re-sampling; Simplification; Curvature-aware; Mean-shift clustering|
|Deposited On:||08 Feb 2010 16:05|
|Last Modified:||05 Apr 2016 13:52|
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