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    Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps

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    Name:
    CVPR21_Fast_Sinkhorn_filters.pdf
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    9.126Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Pai, Gautam
    Ren, Jing cc
    Melzi, Simone
    Wonka, Peter cc
    Ovsjanikov, Maks
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2021-11-02
    Online Publication Date
    2021-11-02
    Print Publication Date
    2021-06
    Permanent link to this record
    http://hdl.handle.net/10754/673099
    
    Metadata
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    Abstract
    In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for the recovery of accurate and bijective pointwise correspondences with a superior time and memory complexity in comparison to existing approaches. Our method combines the simple and concise representation of correspondence using functional maps with the matrix scaling schemes from computational optimal transport. By exploiting the sparse structure of the kernel matrices involved in the transport map computation, we provide an efficient trade-off between acceptable accuracy and complexity for the problem of dense shape correspondence, while promoting bijectivity.
    Citation
    Pai, G., Ren, J., Melzi, S., Wonka, P., & Ovsjanikov, M. (2021). Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence with Functional Maps. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr46437.2021.00045
    Publisher
    IEEE
    Conference/Event name
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    ISBN
    978-1-6654-4510-8
    DOI
    10.1109/CVPR46437.2021.00045
    Additional Links
    https://ieeexplore.ieee.org/document/9577605/
    https://ieeexplore.ieee.org/document/9577605/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9577605
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR46437.2021.00045
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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