Global Stereo Matching Leveraged by Sparse Ground Control Points

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Global Stereo Matching Leveraged by Sparse Ground Control Points
We present a novel global stereo model that makes use of constraints from points with known depths, i.e., the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel GCPs-based regularization term is naturally integrated into our global optimization framework in a principled way using the Bayes rule. The optimal solution of the inference problem can be approximated via existing energy minimization techniques such as graph cuts used in this paper. Our generic probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate the information from multiple sensors. Quantitative evaluations demonstrate the effectiveness of the proposed formulation for regularizing the ill-posed stereo matching problem and improving reconstruction accuracy.
Liang Wang, Ruigang Yang
Added 08 Apr 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Liang Wang, Ruigang Yang
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