Global stereo matching leveraged by sparse ground control points

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 3033-3040

  Wang, Liang ^a   Yang, Ruigang ^a  

^a UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GLOBAL OPTIMIZATION; GRAPHIC METHODS; IMAGE SEGMENTATION; MARKOV PROCESSES; PATTERN RECOGNITION; ROCK MECHANICS;

GROUND CONTROL POINTS; INFERENCE PROBLEM; MARKOV RANDOM FIELDS; OPTIMAL SOLUTIONS; OPTIMIZATION FRAMEWORK; PROBABILISTIC FRAMEWORK; QUANTITATIVE EVALUATION; RECONSTRUCTION ACCURACY;

STEREO IMAGE PROCESSING;

EID: 80052910206     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995480     Document Type: Conference Paper

References (34)

1. vision. middlebury.edu/stereo/.
2. http://www.cise.ufl.edu/research/sparse/umfpack/.
3. S. Birchfield and C. Tomasi. A pixel dissimilarity measure that is insensitive to image sampling. PAMI, 20(4):401-406, 1998.
4. S. Birchfield and C. Tomasi. Multiway cut for stereo and motion with slanted surfaces. In ICCV, pages 489-495, 1999.
5. A. F. Bobick and S. S. Intille. Large occlusion stereo. IJCV, 33(3):181-200, 1999.
6. Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization via graph cuts. PAMI, 23(11):1222-1239, November 2001.
7. J. Cech and R. Sara. Efficient sampling of disparity space for fast and accurate matching. In Intl. Workshop on Benchmarking Automated Calibration, Orientation, and Surface Reconstruction from Images, 2007.
8. R. T. Collins. A space-sweep approach to true multi-image matching. In CVPR, 1996.
9. G. Egnal and R. P. Wildes. Detecting binocular halfocclusions: empirical comparisons of five approaches. PAMI, 24(8):1127-1133, August 2002.
10. D. Gallup, J.-M. Frahm, and M. Pollefeys. Piecewise planar and non-planar stereo for urban scene reconstruction. In CVPR, 2010.
11. M. Guttmann, L. Wolf, and D. Cohen-Or. Semi-automatic stereo extraction from video footage. In ICCV, 2009.
12. J. C. Kim, K. M. Lee, B. T. Choi, and S. U. Lee. A dense stereo matchings uing two-pass dynamic programming with generalized ground control points. In CVPR, 2005.
13. A. Klaus, M. Sormann, and K. Karner. Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In ICPR, pages 15-18, 2006.
14. V. Kolmogorov and R. Zabih. Computing visual correspondence with occlusions using graph cuts. In ICCV, 2001.
15. V. Kolmogorov and R. Zabih. Multi-camera scene reconstruction via graph cuts. In ECCV, 2002.
16. A. Levin, D. Lischinski, and Y. Weiss. Colorization using optimization. SIGGRAPH, August 2004.
17. M. Lhuillier and L. Quan. Match propogation for imagebased modeling and rendering. PAMI, 24(8):1140-1146, 2002.
18. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
19. R. Sara. Finding the largest unambiguous component of stereo matching. In ECCV, 2002.
20. D. Scharstein and C. Pal. Learning conditional random fields for stereo. In CVPR, 2007.
21. D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1):7-42, May 2002.
22. D. Scharstein and R. Szeliski. High-accuracy stereo depth maps using structured light. In CVPR, 2003. 30
23. B. M. Smith, L. Zhang, and H. Jin. Stereo matching with nonparametric priors in feature space. In CVPR, 2009.
24. C. Strecha, W. von Hansen, L. V. Gool, P. Fua, and U. Thoennessen. On benchmarking camera calibrarion and multi-view stereo for high resolution imagery. In CVPR, 2008.
25. J. Sun, Y. Li, S. B. Kang, and H.-Y. Shum. Symmetric stereo matching for occlusion handling. In CVPR, 2005.
26. J. Sun, N.-N. Zheng, and H.-Y. Shum. Stereo matching using belief propagation. PAMI, 25(7):787-800, July 2003.
27. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. F. Tappen, and C. Rother. A comparative study of energy minimization methods for markov random fields with smoothness-based priors. PAMI, 30(6):1068-1080, 2008.
28. H. Tao, H. S. Sawhney, and R. Kumar. A global matching framework for stereo computation. In ICCV, 2001.
29. L. Wang, H. Jin, and R. Yang. Search space reduction for mrf stereo. In ECCV, 2008.
30. Y. Wei and L. Quan. Region-based progressive stereo matching. In CVPR, 2004.
31. O. J. Woodford, P. H. S. Torr, I. D. Reid, and A. W. Fitzgibbon. Global stereo reconstruction under second order smoothness priors. In CVPR, 2008.
32. Q. Yang, L. Wang, R. Yang, H. Stewenius, and D. Nister. Stereo matching with color-weighted correlation, hierachical belief propagation and occlusion handling. In CVPR, 2006.
33. K.-J. Yoon and I.-S. Kweon. Locally adaptive supportweight approach for visual correspondence search. In CVPR.
34. Z. Zhang and Y. Shan. A progressive scheme for stereo matching. In Europ. Workshop on 3D Structure from Multiple Images of Large-Scale Environments, July 2000.