Robust mesh reconstruction from unoriented noisy points

Hoi Sheung, Charlie C.L. Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

We present a robust method to generate mesh surfaces from unoriented noisy points in this paper. The whole procedure consists of three steps. Firstly, the normal vectors at points are evaluated by a highly robust estimator which can fit surface corresponding to less than half of the data points and fit data with multi-structures. This benefits us with the ability to well reconstruct the normal vectors around sharp edges and corners. Meanwhile, clean point cloud equipped with piecewise normal is obtained by projecting points according to the robust fitting. Secondly, an error-minimized subsampling is applied to generate a well-sampled point cloud. Thirdly, a combinatorial approach is employed to reconstruct a triangular mesh connecting the down-sampled points, and a polygonal mesh which preserves sharp features is constructed by the dual-graph of triangular mesh. Parallelization method of the algorithm on a consumer PC using the architecture of GPU is also given.

Original languageEnglish
Title of host publicationProceedings - SPM 2009
Subtitle of host publicationSIAM/ACM Joint Conference on Geometric and Physical Modeling
Pages13-24
Number of pages12
DOIs
Publication statusPublished - 9 Nov 2009
EventSIAM/ACM Joint Conference on Geometric and Physical Modeling - San Francisco, United States
Duration: 5 Oct 20098 Oct 2009

Publication series

NameProceedings - SPM 2009: SIAM/ACM Joint Conference on Geometric and Physical Modeling

Conference

ConferenceSIAM/ACM Joint Conference on Geometric and Physical Modeling
Abbreviated titleSPM 2009
Country/TerritoryUnited States
CitySan Francisco
Period5/10/098/10/09

Keywords

  • GPU
  • Noisy points
  • Parallel computing
  • Robust approach
  • Surface reconstruction

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