Machine Vision and Applications

, Volume 23, Issue 6, pp 1219–1227 | Cite as

Real-time stereo matching based on fast belief propagation

  • Xueqin Xiang
  • Mingmin Zhang
  • Guangxia Li
  • Yuyong He
  • Zhigeng PanEmail author
Original Paper


In this paper, a global optimum stereo matching algorithm based on improved belief propagation is presented which is demonstrated to generate high quality results while maintaining real-time performance. These results are achieved using a foundation based on the hierarchical belief propagation architecture combined with a novel asymmetric occlusion handling model, as well as parallel graphical processing. Compared to the other real-time methods, the experimental results on Middlebury data show the efficiency of our approach.


Stereo matching Hierarchical belief propagation Occlusion handling 


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Supplementary material

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  1. 1.
    Scharstein D., Szeliski R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47(1–3), 7–42 (2002)zbMATHCrossRefGoogle Scholar
  2. 2.
    Sun J., Zheng N.N., Shum H.Y.: Stereo matching using belief propagation. IEEE Trans. PAMI. 25(7), 787–800 (2003)CrossRefGoogle Scholar
  3. 3.
    Boykov Y., Veksler O., Zabih R.: Fast approximate energy minimization via graph cuts. IEEE Trans. PAMI. 23(11), 1227–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    Felzenszwalb P.F., Huttenlocher D.P.: Efficient belief propagation for early vision. IJCV 71(1), 41–54 (2006)CrossRefGoogle Scholar
  5. 5.
    Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., Nistér, D.: Real-time global stereo matching using hierarchical belief propagation. In: Proceedings of BMVC (2006)Google Scholar
  6. 6.
    Sun, J., Zheng, N.-N., Shum, H.-Y.: Symmetric stereo matching for occlusion handling. In: Proceedings of CVPR, vol. II, pp. 399–406 (2005)Google Scholar
  7. 7.
    Yang Q., Wang L., Yang R., Stewénius H., Nistér D.: Stereo Matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE Trans. PAMI. 31(3), 492–504 (2009)CrossRefGoogle Scholar
  8. 8.
    Veksler, O.: Fast variable window for stereo correspondence using integral image. In: Proceedings of CVPR, vol. 1, pp. 556–561 (2003)Google Scholar
  9. 9.
    Gong M., Yang R., Wang L., Gong M.: A performance study on different cost aggregation approaches used in real-time stereo matching. IJCV 75(2), 283–296 (2007)CrossRefGoogle Scholar
  10. 10.
    Criminisi A., Blake A., Rother C.: Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. IJCV 71(1), 89–110 (2007)CrossRefGoogle Scholar
  11. 11.
    Szeliski R., Zabih R., Scharstein D., Veksler O., Kolmogorov V., Agarwala A., Tappen M.F., Rother C.: A comparative study of energy minimization methods for Markov random fields. IEEE Trans. PAMI. 30(6), 1068–1080 (2008)CrossRefGoogle Scholar
  12. 12.
    Tappen, M.F., Freeman, W.T.: Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters. In Proceedings of ICCV, vol. II, pp. 900–906 (2003)Google Scholar
  13. 13.
    Yu, T., Lin, R.-S., Super, B., Tang, B.: Efficient message representations for belief propagation. In: Proceedings of ICCV (2007)Google Scholar
  14. 14.
    Liang, C.-K., Cheng, C.-C., Lai, Y.-C., Chen, L.-G., Chen, H.: Hardware Efficient Belief Propagation. In: Proceedings of CVPR 80–87 (2009)Google Scholar
  15. 15.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusion using graph cuts. In Proceedings of ICCV, vol. II, pp. 508–515 (2001)Google Scholar
  16. 16.
    Ishikawa, H., Geiger. D.: Occlusions, discontinuities, and epipolar lines in stereo. In: Proceedings of ECCV, pp. 232–248 (1998)Google Scholar
  17. 17.
    Bobick A.F., Intille S.S.: Large occlusion stereo. IJCV 33(3), 181–200 (1999)CrossRefGoogle Scholar
  18. 18.
    Yoon K.-J., Kweon I.-S.: Locally adaptive support-weight approach for visual correspondence search. IEEE Trans. PAMI. 28(4), 650–656 (2006)CrossRefGoogle Scholar
  19. 19.
    Birchfield S., Tomasi C.: A pixel dissimilarity measure that is insensitive to image Sampling. IEEE Trans. PAMI. 20(4), 401–406 (1998)CrossRefGoogle Scholar
  20. 20.
    Bishop C.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  21. 21.
    Tseng, Y.-C., Chang, N., Chang, T.-S.: Low memory cost block-based belief propagation for stereo correspondence. In Proceedings of ICME, pp. 1415–1418 (2007)Google Scholar
  22. 22.
    Kraus, M., Strengert, M.: Pyramid filters based on bilinear interpolation. In: Proceedings of GRAPP 2007, vol. GM/R, pp. 21–28 (2007)Google Scholar
  23. 23.
    Min D., Sohn K.: Cost aggregation and occlusion handling with WLS in stereo matching. IEEE Trans. Image Process. 17(8), 1431–1442 (2008)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Scharstein, D., Szeliski, R.: Middlebury stereo vision research page (2010).
  25. 25.
    Gupta, R., Cho, S.-Y.: Real-time stereo matching using adaptive binary window. In: Proceedings of 3DPVT (2010)Google Scholar
  26. 26.
    Zhang, K., Lu, J., Lafruit, G., Lauwereins, R., Van Gool, L.: Real-time accurate stereo with bitwise fast voting on CUDA. In: Proceedings of ICCVW, pp. 794–800 (2009)Google Scholar
  27. 27.
    Tombari, F., Mattoccia, S., Di Stefano, L., Addimanda, E.: Near real-time stereo based on effective cost aggregation. In Proceedings of ICPR, pp. 1–4 (2008)Google Scholar
  28. 28.
    Kosov, S., Thormählen, T., Seidel, H.-P.: Accurate real-time disparity estimation with variational methods. In: Proceedings of ISVC, pp. 796–807 (2009)Google Scholar
  29. 29.
    Wang, L., Liao, M., Gong, M., Yang, R., Nistér, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings of 3DPVT, pp. 798–805 (2006)Google Scholar
  30. 30.
    Gong M., Yang, Y.-H.: Near real-time reliable stereo matching using programmable graphics hardware. In: Proceedings of CVPR, pp. 924–931 (2005)Google Scholar
  31. 31.
    Grauer-Gray, S., Kambhamettu, C.: Hierarchical belief propagation to reduce search space using CUDA for stereo and motion estimation. In Proceedings of WACV, pp. 1–8 (2009)Google Scholar
  32. 32.
    Trinh, H., McAllester. D.: Unsupervised learning of stereo vision with monocular cues. In: Proceedings of BMVC (2009)Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Xueqin Xiang
    • 1
  • Mingmin Zhang
    • 1
  • Guangxia Li
    • 1
  • Yuyong He
    • 1
  • Zhigeng Pan
    • 2
    Email author
  1. 1.State Key Lab of Computer Aided Design and Computer GraphicsHangzhouChina
  2. 2.Digital Media and HCI Research CenterHangzhou Normal UniversityHangzhouChina

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