Inclusion of a Second-Order Prior into Semi-Global Matching

  • Simon Hermann
  • Reinhard Klette
  • Eduardo Destefanis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


Today’s stereo vision algorithms and computing technology allow real-time 3D data analysis, for example for driver assistance systems. A recently developed Semi-Global Matching (SGM) approach by H. Hirschmüller became a popular choice due to performance and robustness. This paper evaluates different parameter settings for SGM, and its main contribution consists in suggesting to include a second order prior into the smoothness term of the energy function. It also proposes and tests a new cost function for SGM. Furthermore, some preprocessing (edge images) proved to be of great value for improving SGM stereo results on real-world sequences, as previously already shown by S. Guan and R. Klette for belief propagation. There is also a performance gain for engineered stereo data (e.g.) as currently used on the Middlebury stereo website. However, the fact that results are not as impressive as on the .enpeda.. sequences indicates that optimizing for engineered data does not neccessarily improve real world stereo data analysis.


Edge Image Stereo Pair Driver Assistance System Secondary Parameter Smoothness Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Badino, H.: A robust approach for ego-motion estimation using a mobile stereo platform. In: Jähne, B., Mester, R., Barth, E., Scharr, H. (eds.) IWCM 2004. LNCS, vol. 3417, pp. 198–208. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Birchfield, S., Tomasi, C.: Birchfield and C. Tomasi. Depth discontinuities by pixel-to-pixel stereo. Int. J. Computer Vision 35, 269–293 (1999)CrossRefGoogle Scholar
  3. 3.
    Gehrig, S., Franke, U.: Improving stereo sub-pixel accuracy for long range stereo. Daimler A.G., Internal Report, Sindelfingen (2007)Google Scholar
  4. 4.
    Guan, S., Klette, R.: Belief-propagation on edge images for stereo analysis of image sequences. In: Sommer, G., Klette, R. (eds.) RobVis 2008. LNCS, vol. 4931, pp. 291–302. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Hirschmüller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: IEEE Conf. Computer Vision Pattern Recognition, vol. 2, pp. 807–814 (2005)Google Scholar
  6. 6.
    Hirschmüller, H.: Stereo vision in structured environments by consistent semi-global matching. In: IEEE Conf. Computer Vision Pattern Recognition, vol. 2, pp. 2386–2393 (2006)Google Scholar
  7. 7.
    Klette, R.: Evaluation of stereo and motion techniques on real-world video sequences. Dagstuhl seminar Statistical and Geometrical Approaches to Visual Motion Analysis (2008),

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Simon Hermann
    • 1
  • Reinhard Klette
    • 1
  • Eduardo Destefanis
    • 2
  1. 1.The .enpeda.. ProjectThe University of AucklandNew Zealand
  2. 2.Facultad Regional CórdobaUniversidad Tecnológica NacionalArgentina

Personalised recommendations