Feature-preserving anisotropic smoothing for meshes with large-scale noise

Masatake Higashi, Tetsuo Oya, Tetsuro Sugiura, Masakazu Kobayashi

Research output: Contribution to journalArticlepeer-review


This paper presents a smoothing method which preserves features for a triangular mesh even when large-scale noise are included because of measurement errors. First, scale-dependent discrete Laplacian is introduced along with boundary Laplacian to deal with an open mesh. Then, a method for feature detection which uses the values by these Laplacians is constructed. Furthermore, anisotropic diffusion is proposed which determines suitable parameters from the values for preserving features. Finally a method is presented which discriminates features from large-scale noise by generating graph of feature lines. Effectiveness of the methods is shown by the experiment results of well-smoothed meshes with their features preserved.

Original languageEnglish
Pages (from-to)365-374
Number of pages10
JournalComputer-Aided Design and Applications
Issue number3
Publication statusPublished - 2009
Externally publishedYes


  • Anisotropic smoothing
  • Diffusion flow
  • Feature-preserving
  • Laplacian

ASJC Scopus subject areas

  • Computational Mechanics
  • Computer Graphics and Computer-Aided Design
  • Computational Mathematics


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