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        Joint optimisation for object class segmentation and dense stereo reconstruction

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        paper104.pdf (PDF, 368Kb)
        Author
        Ladicky, L.
        Sengupta, S.
        Russell, C.
        Sturgess, P.
        Bastanlar, Yalin
        Clocksin, William
        Torr, P.H.S.
        Attention
        2299/7316
        Abstract
        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.
        Publication date
        2010
        Published in
        Proceedings of the British Machine Vision Conference 2010
        Published version
        https://doi.org/10.5244/C.24.104
        Other links
        http://hdl.handle.net/2299/7316
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