Topologically-Robust 3D Shape Matching Based on Diffusion Geometry and Seed Growing

Avinash Sharma (1) , Radu Horaud (1) , Jan Cech (2) , Edmond Boyer (1, 3)

1 PERCEPTION - Interpretation and Modelling of Images and Videos
2 Department of Cybernetics [Prague]
3 MORPHEO - Capture and Analysis of Shapes in Motion

3D Shape matching is an important problem in computer vision. One of the major difficulties in finding dense correspondences between 3D shapes is related to the topological discrepancies that often arise due to complex kinematic motions. In this paper we propose a shape matching method that is robust to such changes in topology. The algorithm starts from a sparse set of seed matches and outputs dense matching. We propose to use a shape descriptor based on properties of the heat-kernel and which provides an intrinsic scale-space representation. This descriptor incorporates (i) heat-flow from already matched points and (ii) self diffusion. At small scales the descriptor behaves locally and hence it is robust to global changes in topology. Therefore, it can be used to build a vertex-to-vertex matching score conditioned by an initial correspondence set. This score is then used to iteratively add new correspondences based on a novel seed-growing method that iteratively propagates the seed correspondences to nearby vertices. The matching is farther densified via an EM-like method that explores the congruency between the two shape embeddings. Our method is compared with two recently proposed algorithms and we show that we can deal with substantial topological differences between the two shapes.

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Avinash Sharma, Radu Horaud, Jan Cech, Edmond Boyer. Topologically-Robust 3D Shape Matching Based on Diffusion Geometry and Seed Growing. CVPR '11 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2011, Colorado Springs, United States. pp.2481-2488, ⟨10.1109/CVPR.2011.5995455⟩. ⟨inria-00590280⟩