Electrical Engineering-Systems Dept.

סמינר

Roee Litman,

(M.Sc. student under the supervision of Dr. Alexander Bronstein)

on the subject:

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 advent of feature-based methods in geometric shape analysis, there is significant interest in finding analogous approaches in the 3D world. In this study, 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.

The vast majority of studies of deformable 3D shapes model them as the two-dimensional boundary of the volume of the shape. Recent works have shown that a volumetric shape model is advantageous in numerous ways as it better captures the natural behavior of non-rigid deformations. We show that our framework easily adapts to this volumetric approach, and even demonstrates superior performance.

A quantitative evaluation of our methods on the SHREC'10 and SHREC'11 feature detection benchmarks as well as qualitative tests on the SCAPE dataset show its potential as a source of high-quality features. Examples demonstrating the drawbacks of surface stable components and the advantage of their volumetric counterparts are also presented.