Diffusion-geometric maximally stable component detection in deformable shapes

TL;DR

This paper introduces a diffusion-geometric framework for detecting stable components in deformable 3D shapes, aiming to improve feature detection and description in non-rigid shape analysis.

Contribution

It presents a novel diffusion-geometric approach for stable component detection in non-rigid 3D shapes, extending 2D image methods to 3D shape analysis.

Findings

Demonstrates high repeatability on SHREC'10 benchmark

Provides high-quality features for geometric analysis

Shows potential for non-rigid shape feature detection

Abstract

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.