Learning Conditional Random Fields for Stereo

@article{Scharstein2007LearningCR,
  title={Learning Conditional Random Fields for Stereo},
  author={Daniel Scharstein and Christopher Joseph Pal},
  journal={2007 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2007},
  pages={1-8}
}
State-of-the-art stereo vision algorithms utilize color changes as important cues for object boundaries. Most methods impose heuristic restrictions or priors on disparities, for example by modulating local smoothness costs with intensity gradients. In this paper we seek to replace such heuristics with explicit probabilistic models of disparities and intensities learned from real images. We have constructed a large number of stereo datasets with ground-truth disparities, and we use a subset of… CONTINUE READING
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