Comparison of Dense Stereo Using CUDA

  • Ke Zhu
  • Matthias Butenuth
  • Pablo d’Angelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


In this paper, a local and a global dense stereo matching method, implemented using Compute Unified Device Architecture (CUDA), are presented, analyzed and compared. The purposed work shows the general strategy of the parallelization of matching methods on GPUs and the tradeoff between accuracy and run-time on current GPU hardware. Two representative and widely-used methods, the Sum of Absolute Differences (SAD) method and the Semi-Global Matching (SGM) method, are used and their results are compared using the Middlebury test sets.


Shared Memory Global Memory Thread Block Epipolar Line Cost Aggregation 
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  1. 1.
    Butenuth, M., Reinartz, P., Lenhart, D., Rosenbaum, D., Hinz, S.: Analysis of image sequences for the detection and monitoring of moving traffic. Photogrammetrie Fernerkundung Geoinformation 5, 421–430 (2009)CrossRefGoogle Scholar
  2. 2.
    Middlebury Stereo Website (May 2010),
  3. 3.
    Gehrig, S.K., Eberli, F., Meyer, T.: A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Banks, J., Bennamoun, M., Corke, P.: Non-parametric techniques for fast and robust stereo matching (1997)Google Scholar
  5. 5.
    Krik, D.B., Hwa, W.W.: Programming Massively Parallel Processors: A Hands-on Approach (2010)Google Scholar
  6. 6.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47, 7–42 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Hirschmüller, H.: Stereo processing by semi-global matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 328–341 (2008)CrossRefGoogle Scholar
  8. 8.
    Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on image with radiometric differences. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 1582–1599 (2009)CrossRefGoogle Scholar
  9. 9.
    NVIDIA. CUDA Programming Guide Version 3.0 (2010)Google Scholar
  10. 10.
    NVIDIA. OpenCL Best Practices Guide Version 1.0 (2009)Google Scholar
  11. 11.
    Fusiello, A., Trucco, E., Verri, A.: A compact algorithm for rectification of stereo pairs. Machine Vision and Applications 12, 16–22 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ke Zhu
    • 1
  • Matthias Butenuth
    • 2
  • Pablo d’Angelo
    • 3
  1. 1.Remote Sensing TechnologyTechnische Universit”at M”unchenGermany
  2. 2.Active Safety and Driver AssistanceIAV GmbHGermany
  3. 3.Remote Sensing Technology InstituteGerman Aerospace Center (DLR)Germany

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