Efficient Deformable Shape Correspondence via Kernel Matching
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by
Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron
Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein,
Ron Kimmel, Daniel Cremers
2017
Abstract
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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