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BMVC
2010

Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction

13 years 9 months ago
Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction
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.
Lubor Ladicky, Paul Sturgess, Christopher Russell,
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Lubor Ladicky, Paul Sturgess, Christopher Russell, Sunando Sengupta, Yalin Bastanlar, William Clocksin, Philip H. S. Torr
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