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

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 se...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Avinash Sharma, Radu Horaud, Jan Cech, Edmond Boyer
• Keywords: Complex Kinematic Motions, Computer Vision, CVPR 2011, Intrinsic Scale-space Representation, Vertex-to-vertex Matching Score