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

7 years 10 months ago
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...
Avinash Sharma, Radu Horaud, Jan Cech, Edmond Boye
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where CVPR
Authors Avinash Sharma, Radu Horaud, Jan Cech, Edmond Boyer
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