L1 rotation averaging using the weiszfeld algorithm

Richard Hartley*, Khurrum Aftab, Jochen Trumpf

*Corresponding author for this work

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

    170 Citations (Scopus)

    Abstract

    We consider the problem of rotation averaging under the L 1 norm. This problem is related to the classic Fermat-Weber problem for finding the geometric median of a set of points in IR n . We apply the classical Weiszfeld algorithm to this problem, adapting it iteratively in tangent spaces of SO(3) to obtain a provably convergent algorithm for finding the L 1 mean. This results in an extremely simple and rapid averaging algorithm, without the need for line search. The choice of L 1 mean (also called geometric median) is motivated by its greater robustness compared with rotation averaging under the L 2 norm (the usual averaging process). We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global L 1 optimum) and multiple rotation averaging (for which no such proof exists). The algorithm is demonstrated to give markedly improved results, compared with L 2 averaging. We achieve a median rotation error of 0.82 degrees on the 595 images of the Notre Dame image set.

    Original languageEnglish
    Title of host publication2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
    PublisherIEEE Computer Society
    Pages3041-3048
    Number of pages8
    ISBN (Print)9781457703942
    DOIs
    Publication statusPublished - 2011

    Publication series

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

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