Structure from Recurrent Motion: From Rigidity to Recurrency

Xiu Li, Hongdong Li, Hanbyul Joo, Yebin Liu, Yaser Sheikh

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    20 Citations (Scopus)

    Abstract

    This paper proposes a new method for Non-Rigid Structure-from-Motion (NRSfM) from a long monocular video sequence observing a non-rigid object performing recurrent and possibly repetitive dynamic action. Departing from the traditional idea of using linear low-order or low-rank shape model for the task of NRSfM, our method exploits the property of shape recurrency (i.e., many deforming shapes tend to repeat themselves in time). We show that recurrency is in fact a generalized rigidity. Based on this, we reduce NRSfM problems to rigid ones provided that certain recurrency condition is satisfied. Given such a reduction, standard rigid-SfM techniques are directly applicable (without any change) to the reconstruction of non-rigid dynamic shapes. To implement this idea as a practical approach, this paper develops efficient algorithms for automatic recurrency detection, as well as camera view clustering via a rigidity-check. Experiments on both simulated sequences and real data demonstrate the effectiveness of the method. Since this paper offers a novel perspective on rethinking structure-from-motion, we hope it will inspire other new problems in the field.

    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
    PublisherIEEE Computer Society
    Pages3032-3040
    Number of pages9
    ISBN (Electronic)9781538664209
    DOIs
    Publication statusPublished - 14 Dec 2018
    Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States
    Duration: 18 Jun 201822 Jun 2018

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    ISSN (Print)1063-6919

    Conference

    Conference31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
    Country/TerritoryUnited States
    CitySalt Lake City
    Period18/06/1822/06/18

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