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    Space-Time Co-Segmentation of Articulated Point Cloud Sequences

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    Date
    2016
    Author
    Yuan, Qing
    Li, Guiqing
    Xu, Kai
    Chen, Xudong
    Huang, Hui ORCID
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    Abstract
    Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter-frame variation across the whole sequence. We propose a local-to-global approach to co-segment point cloud sequences of articulated objects into near-rigid moving parts. Our method starts from a per-frame point clustering, derived from a robust voting-based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space-time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co-segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.
    BibTeX
    @article {10.1111:cgf.12843,
    journal = {Computer Graphics Forum},
    title = {{Space-Time Co-Segmentation of Articulated Point Cloud Sequences}},
    author = {Yuan, Qing and Li, Guiqing and Xu, Kai and Chen, Xudong and Huang, Hui},
    year = {2016},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.12843}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12843
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    • 35-Issue 2
    • EG 2016 - Full Papers - CGF 35-Issue 2

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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA