  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.
