Inter-Image Statistics for 3D Environment Modeling
International Journal of Computer Vision
Automatic Mutual Nonrigid Registration of Dense Surfaces by Graphical Model Based Inference
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
3D reconstruction and mapping from stereo pairs with geometrical rectification
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Learning image structures for optimizing disparity estimation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
On Learning Conditional Random Fields for Stereo
International Journal of Computer Vision
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This paper presents a novel approach for estimating parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP) problem, in which both a disparity map and MRF parameters are estimated from the stereo pair itself. We present an iterative algorithm for the MAP estimation that alternates between estimating the parameters while fixing the disparity map and estimating the disparity map while fixing the parameters. The estimated parameters include robust truncation thresholds, for both data and neighborhood terms, as well as a regularization weight. The regularization weight can be either a constant for the whole image, or spatially-varying, depending on local intensity gradients. In the latter case, the weights for intensity gradients are also estimated. Experiments indicate that our approach, as a wrapper for existing stereo algorithms, moves a baseline belief propagation stereo algorithm up six slots in the Middlebury rankings.