Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Canonical Embedding for Non-rigid Shape Matching   This paper provides a novel framework that learns canonical embeddings for
non-rigid shape matching. In contrast to prior work in this direction, our
framework is trained end-to-end and thus avoids instabilities and constraints
associated with the commonly-used Laplace-Beltrami basis or sequential
optimization schemes. On multiple datasets, we demonstrate that learning self
symmetry maps with a deep functional map projects 3D shapes into a low
dimensional canonical embedding that facilitates non-rigid shape correspondence
via a simple nearest neighbor search. Our framework outperforms multiple recent
learning based methods on FAUST and SHREC benchmarks while being
computationally cheaper, data-efficient, and robust.

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The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
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The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom