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.
@article{arxiv.1012.3951,
title = {Diffusion-geometric maximally stable component detection in deformable shapes},
author = {Roee Litman and Alex M. Bronstein and Michael M. Bronstein},
journal= {arXiv preprint arXiv:1012.3951},
year = {2014}
}