Projective least-squares: Global solutions with local optimization

Carl Olsson*, Fredrik Kahl, Richard Hartley

*Corresponding author for this work

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

    15 Citations (Scopus)

    Abstract

    Recent work in multiple view geometry has focused on obtaining globally optimal solutions at the price of computational time efciency. On the other hand, traditional bundle adjustment algorithms have been found to provide good solutions even though there may be multiple local minima. In this paper we justify this observation by giving a simple sufcient condition for global optimality that can be used to verify that a solution obtained from any local method is indeed global. The method is tested on numerous problem instances of both synthetic and real data sets. In the vast majority of cases we are able to verify that the solutions are optimal, in particular for small-scale problems. We also develop a branch and bound procedure that goes beyond verication. In cases where the suffcient condition does not hold, the algorithm returns either of the following two results: (i) a certi cate of global optimality for the local solution or (ii) the global solution.

    Original languageEnglish
    Title of host publication2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
    PublisherIEEE Computer Society
    Pages1216-1223
    Number of pages8
    ISBN (Print)9781424439935
    DOIs
    Publication statusPublished - 2009
    Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
    Duration: 20 Jun 200925 Jun 2009

    Publication series

    Name2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

    Conference

    Conference2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
    Country/TerritoryUnited States
    CityMiami, FL
    Period20/06/0925/06/09

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