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Cyclic Functional Mapping: Self-supervised Correspondence Between Non-isometric Deformable Shapes

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

We present the first spatial-spectral joint consistency network for self-supervised dense correspondence mapping between non-isometric shapes. The task of alignment in non-Euclidean domains is one of the most fundamental and crucial problems in computer vision. As 3D scanners can generate highly complex and dense models, the mission of finding dense mappings between those models is vital. The novelty of our solution is based on a cyclic mapping between metric spaces, where the distance between a pair of points should remain invariant after the full cycle. As the same learnable rules that generate the point-wise descriptors apply in both directions, the network learns invariant structures without any labels while coping with non-isometric deformations. We show here state-of-the-art-results by a large margin for a variety of tasks compared to known self-supervised and supervised methods .

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Notes

  1. 1.

    An illustration of the distortion process is shown in Fig. 2.

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Acknowledgment

D.R. is partially funded by the Zimin Institute for Engineering Solutions Advancing BetterLives, the Israeli consortiums for soft robotics and autonomous driving, and the Shlomo Shmeltzer Institute for Smart Transportation.

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Authors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Dvir Ginzburg & Dan Raviv

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  1. Dvir Ginzburg
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  2. Dan Raviv
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Correspondence to Dvir Ginzburg .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Ginzburg, D., Raviv, D. (2020). Cyclic Functional Mapping: Self-supervised Correspondence Between Non-isometric Deformable Shapes. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12350. Springer, Cham. https://doi.org/10.1007/978-3-030-58558-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-58558-7_3

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