Cross image inference scheme for stereo matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Cost volume-based interactive depth editing in stereo post-processing
Proceedings of the 10th European Conference on Visual Media Production
Stereo matching by using the global edge constraint
Neurocomputing
Journal of Mathematical Imaging and Vision
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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.