International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Stereo reconstruction using high order likelihood
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a novel regularization model for stereo matching that uses large neighborhood windows. The model is based on the observation that in a local neighborhood there exists a linear relationship between pixel values and disparities. Compared to the traditional boundary preserving regularization models that use adjacent pixels, the proposed model is robust to image noise and captures higher level interactions. We develop a globally optimized stereo matching algorithm based on this regularization model. The algorithm alternates between finding a quadratic upper bound of the relaxed energy function and solving the upper bound using iterative reweighted least squares. To reduce the chance of being trapped in local minima, we propose a progressive convex-hull filter to tighten the data cost relaxation. Our evaluation on the Middlebury datasets shows the effectiveness of our method in preserving boundary sharpness while keeping regions smooth. We also evaluate our method on a wide range of challenging real-world videos. Experimental results show that our method outperforms existing methods in temporal consistency.