Reduced Representation of Deformation Fields for Effective Non-rigid Shape MatchingDownload PDF

Published: 31 Oct 2022, 18:00, Last Modified: 12 Oct 2022, 14:13NeurIPS 2022 AcceptReaders: Everyone
Keywords: non-rigid 3D shape correspondence, 3D shape deformation, 3D Vision, Computer Graphics
TL;DR: Learning shape deformation through mesh-free approximations
Abstract: In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from existing works that represent deformation fields by training a general-purpose neural network, we advocate for an approximation based on mesh-free methods. By letting the network learn deformation parameters at a sparse set of positions in space (nodes), we reconstruct the continuous deformation field in a closed-form with guaranteed smoothness. With this reduction in degrees of freedom, we show significant improvement in terms of data-efficiency thus enabling limited supervision. Furthermore, our approximation provides direct access to first-order derivatives of deformation fields, which facilitates enforcing desirable regularization effectively. Our resulting model has high expressive power and is able to capture complex deformations. We illustrate its effectiveness through state-of-the-art results across multiple deformable shape matching benchmarks. Our code and data are publicly available at: https://github.com/Sentient07/DeformationBasis.
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