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    Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence

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    Type
    Conference Paper
    Authors
    Donati, Nicolas
    Sharma, Abhishek
    Ovsjanikov, Maks
    KAUST Grant Number
    CRG-2017-3426
    Date
    2020-08-05
    Preprint Posting Date
    2020-03-31
    Online Publication Date
    2020-08-05
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/662461
    
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    Abstract
    We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate on handcrafted input descriptors and thus generalize poorly across diverse datasets, our approach is both accurate and robust to changes in shape structure. Key to our method is a feature-extraction network that learns directly from raw shape geometry, combined with a novel regularized map extraction layer and loss, based on the functional map representation. We demonstrate through extensive experiments in challenging shape matching scenarios that our method can learn from less training data than existing supervised approaches and generalizes significantly better than current descriptor-based learning methods. Our source code is available at: https://github.com/LIX-shape-analysis/GeomFmaps.
    Citation
    Donati, N., Sharma, A., & Ovsjanikov, M. (2020). Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.00862
    Sponsors
    This work was supported by KAUST OSR Award No. CRG-2017-3426, a gift from Nvidia and the ERC Starting Grant No. 758800 (EXPROTEA).
    Publisher
    IEEE
    ISBN
    9781728171685
    DOI
    10.1109/cvpr42600.2020.00862
    arXiv
    2003.14286
    Additional Links
    https://ieeexplore.ieee.org/document/9156832/
    ae974a485f413a2113503eed53cd6c53
    10.1109/cvpr42600.2020.00862
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