Stereo Matching as a Nearest-Neighbor Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Optimal Surface from Stereo
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Correspondence Using Energy Minimization and Mutual Information
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Surfaces with occlusions from layered stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a stereo matching algorithm for obtaining dense disparity maps. Our main contribution is to introduce a new cost aggregation technique of a 3D disparity-space image data, referred to as the Optimal Path Cost Aggregation. The approach is based on the dynamic programming principle, which exactly solves one dimensional optimization problem. Furthermore, the 2D extension of the proposed technique proves an excellent approximation to the global 2D optimization problem. The effectiveness of our approach is demonstrated with several widely used synthetic and real image pairs, including ones with ground-truth value.