Efficient Deformable Shape Correspondence via Kernel Matching

07/25/2017 ∙ by Zorah Lähner, et al. ∙ 0

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming. Surprisingly, in spite of the highly non-convex nature of the resulting quadratic assignment problem, our method converges to a semantically meaningful and continuous mapping in most of our experiments, and scales well. We provide preliminary theoretical analysis and several interpretations of the method.

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KernelMatching

Implementation of Efficient Deformable Shape Correspondence via Kernel Matching (https://arxiv.org/abs/1707.08991).


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