Consensus set maximization with guaranteed global optimality for robust geometry estimation

Hongdong Li*

*Corresponding author for this work

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

    129 Citations (Scopus)

    Abstract

    Finding the largest consensus set is one of the key ideas used by the original RANSAC for removing outliers in robust-estimation. However, because of its random and non-deterministic nature, RANSAC does not fulfill the goal of consensus set maximization exactly and optimally. Based on global optimization, this paper presents a new algorithm that solves the problem exactly. We reformulate the problem as a mixed integer programming (MIP), and solve it via a tailored branch-and-bound method, where the bounds are computed from the MIP's convex under-estimators. By exploiting the special structure of linear robust-estimation, the new algorithm is also made efficient from a computational point of view.

    Original languageEnglish
    Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
    Pages1074-1080
    Number of pages7
    DOIs
    Publication statusPublished - 2009
    Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
    Duration: 29 Sept 20092 Oct 2009

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision

    Conference

    Conference12th International Conference on Computer Vision, ICCV 2009
    Country/TerritoryJapan
    CityKyoto
    Period29/09/092/10/09

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