Diffusion-geometric maximally stable component detection in deformable shapes

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.

Graphical AbstractFigure optionsDownload high-quality image (57 K)Download as PowerPoint slideHighlights
► Feature detector for deformable shapes is presented, which detects maximally stable regions.
► A generic framework for stable component detection is introduced, which unites vertex- and edge-weighted graph representations (as opposed to vertex-weighting used in images).
► Region detection is done using diffusion geometry, which makes the process isometry invariant.
► The method was tested qualitatively on the SHREC10 data-set and showed high repeatability rates.