Curvature-aware adaptive re-sampling for point-sampled geometry

  • Authors:
  • Yongwei Miao;Renato Pajarola;Jieqing Feng

  • Affiliations:
  • State Key Laboratory of CAD&CG, Zhejiang University, China and College of Science, Zhejiang University of Technology, China and Department of Informatics, University of Zurich, Switzerland;Department of Informatics, University of Zurich, Switzerland;State Key Laboratory of CAD&CG, Zhejiang University, China

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2009

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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.