• Login
    View Item 
    •   Eurographics DL Home
    • Eurographics Partner Events
    • VMV: Vision, Modeling, and Visualization
    • VMV14
    • View Item
    •   Eurographics DL Home
    • Eurographics Partner Events
    • VMV: Vision, Modeling, and Visualization
    • VMV14
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    SOAR: Stochastic Optimization for Affine global point set Registration

    Thumbnail
    View/Open
    103-110.pdf (8.492Mb)
    Date
    2014
    Author
    Agus, Marco
    Gobbetti, Enrico ORCID
    Villanueva, Alberto Jaspe
    Mura, Claudio ORCID
    Pajarola, Renato ORCID
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    We introduce a stochastic algorithm for pairwise affine registration of partially overlapping 3D point clouds with unknown point correspondences. The algorithm recovers the globally optimal scale, rotation, and translation alignment parameters and is applicable in a variety of difficult settings, including very sparse, noisy, and outlierridden datasets that do not permit the computation of local descriptors. The technique is based on a stochastic approach for the global optimization of an alignment error function robust to noise and resistant to outliers. At each optimization step, it alternates between stochastically visiting a generalized BSP-tree representation of the current solution landscape to select a promising transformation, finding point-to-point correspondences using a GPU-accelerated technique, and incorporating new error values in the BSP tree. In contrast to previous work, instead of simply constructing the tree by guided random sampling, we exploit the problem structure through a low-cost local minimization process based on analytically solving absolute orientation problems using the current correspondences. We demonstrate the quality and performance of our method on a variety of large point sets with different scales, resolutions, and noise characteristics.
    BibTeX
    @inproceedings {10.2312:vmv.20141282,
    booktitle = {Vision, Modeling & Visualization},
    editor = {Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban},
    title = {{SOAR: Stochastic Optimization for Affine global point set Registration}},
    author = {Agus, Marco and Gobbetti, Enrico and Villanueva, Alberto Jaspe and Mura, Claudio and Pajarola, Renato},
    year = {2014},
    publisher = {The Eurographics Association},
    ISBN = {978-3-905674-74-3},
    DOI = {10.2312/vmv.20141282}
    }
    URI
    http://dx.doi.org/10.2312/vmv.20141282
    Collections
    • VMV14

    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
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    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