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Shape Matching Based on Diffusion Embedding and on Mutual Isometric Consistency

Avinash Sharma 1 Radu Horaud 1 
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We address the problem of matching two 3D shapes by representing them using the eigenvalues and eigenvectors of the discrete diffusion operator. This provides a representation framework useful for both scale-space shape descriptors and shape comparisons. We formally introduce a canonical diffusion embedding based on the combinatorial Laplacian; we reveal some interesting properties and we propose a unit hypersphere normalization of this embedding. We also propose a practical algorithm that seeks the largest set of mutually consistent point-to-point matches between two shapes based on isometric consistency between the two embeddings. We illustrate our method with several examples of matching shapes at various scales.
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Submitted on : Tuesday, December 21, 2010 - 9:35:41 PM
Last modification on : Thursday, January 20, 2022 - 5:28:07 PM
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Avinash Sharma, Radu Horaud. Shape Matching Based on Diffusion Embedding and on Mutual Isometric Consistency. NORDIA 2010 - Workshop on Nonrigid Shape Analysis and Deformable Image Alignment, Jun 2010, San Francisco, United States. pp.29-36, ⟨10.1109/CVPRW.2010.5543278⟩. ⟨inria-00549406⟩



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