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

Diffusion-geometric maximally stable component detection in deformable shapes