Joint optimisation for object class segmentation and dense stereo reconstruction

L. Ladicky, S. Sengupta, C. Russell, P. Sturgess, Yalin Bastanlar, William Clocksin, P.H.S. Torr

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    51 Citations (Scopus)
    75 Downloads (Pure)


    The problems of dense stereo reconstruction and object class segmentation can both be formulated as Conditional Random Field based labelling problems, in which every pixel in the image is assigned a label corresponding to either its disparity, or an object class such as road or building. While these two problems are mutually informative, no attempt has been made to jointly optimise their labellings. In this work we provide a principled energy minimisation framework that unifies the two problems and demonstrate that, by resolving ambiguities in real world data, joint optimisation of the two problems substantially improves performance. To evaluate our method, we augment the street view
    Leuven data set, producing 70 hand labelled object class and disparity maps. We hope that the release of these annotations will stimulate further work in the challenging domain of street-view analysis.
    Original languageEnglish
    Title of host publicationProceedings of the British Machine Vision Conference 2010
    Subtitle of host publicationBMVC
    PublisherBMVA Press
    Pagespaper 104
    Publication statusPublished - 2010
    EventBritish Machine Vision Conf 2010 - Aberystwyth, United Kingdom
    Duration: 31 Aug 20103 Sept 2010


    ConferenceBritish Machine Vision Conf 2010
    Country/TerritoryUnited Kingdom


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