Efficient deformable shape correspondence via kernel matching
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.
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.
Efficient deformable shape correspondence via kernel matching
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 …
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 …
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