Fast stereo matching using reliability-based dynamic programming and consistency constraints

A method for solving binocular and multiview stereo matching problems is presented here. A weak consistency constraint is proposed, which expresses the visibility constraint in the image space. It can be proved that the weak consistency constraint holds for scenes that can be represented by a set of 3D points. As well, also proposed is a new reliability measure for dynamic programming techniques, which evaluates the reliability of a given match. A novel reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparity values to pixels when the reliabilities of the corresponding matches exceed a given threshold. Consistency constraints and the new reliability-based dynamic programming algorithm can be combined in an iterative approach. The experimental results show that the iterative approach can produce dense (60-90%) and reliable (total error rate of 0.1-1.1%) matching for binocular stereo datasets. It can also generate promising disparity maps for trinocular and multiview stereo datasets.

[1]  Yuichi Ohta,et al.  Occlusion detectable stereo-systematic comparison of detection algorithms , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[2]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[3]  Richard Szeliski,et al.  Handling occlusions in dense multi-view stereo , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Takeo Kanade,et al.  A Multiple-Baseline Stereo , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Roberto Manduchi,et al.  Distinctiveness maps for image matching , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[6]  Radim Sára,et al.  Finding the Largest Unambiguous Component of Stereo Matching , 2002, ECCV.

[7]  Minglun Gong,et al.  Genetic-Based Stereo Algorithm and Disparity Map Evaluation , 2002, International Journal of Computer Vision.

[8]  Aaron F. Bobick,et al.  Large Occlusion Stereo , 1999, International Journal of Computer Vision.

[9]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[10]  Takeo Kanade,et al.  A Cooperative Algorithm for Stereo Matching and Occlusion Detection , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Vladimir Kolmogorov,et al.  Multi-camera Scene Reconstruction via Graph Cuts , 2002, ECCV.