Learning Canonical Embedding for Non-rigid Shape MatchingDownload PDF

Sep 29, 2021 (edited Oct 06, 2021)ICLR 2022 Conference Withdrawn SubmissionReaders: Everyone
  • Keywords: Deep Functional Maps, Symmetry group, Point Clouds, Linear Transformation, Canonical 3D shape Embedding
  • Abstract: 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.
  • One-sentence Summary: learn an embedding of each shape that would make the given self-symmetry map linear in some higher-dimensional space.
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