Diffusion-geometric maximally stable component detection in deformable shapes

Roee Litman*, Alexander M. Bronstein, Michael M. Bronstein

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Maximally stable component detection is a very popular method for feature analysis in images, mainly due to its low computation cost and high repeatability. With the recent advance of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world. In this paper, we formulate a diffusion-geometric framework for stable component detection in non-rigid 3D shapes, which can be used for geometric feature detection and description. A quantitative evaluation of our method on the SHREC'10 feature detection benchmark shows its potential as a source of high-quality features.

Original languageEnglish
Pages (from-to)549-560
Number of pages12
JournalComputers and Graphics
Issue number3
StatePublished - Jun 2011
Externally publishedYes


  • Component tree
  • Deformable shapes
  • Diffusion geometry
  • Feature detection
  • Level sets
  • MSER


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