Dense Stereo by Triangular Meshing and Cross Validation

  • Peter Wey
  • Bernd Fischer
  • Herbert Bay
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Dense depth maps can be estimated in a Bayesian sense from multiple calibrated still images of a rigid scene relative to a reference view [1]. This well-established probabilistic framework is extended by adaptively refining a triangular meshing procedure and by automatic cross-validation of model parameters. The adaptive refinement strategy locally adjusts the triangular meshing according to the measured image data. The new method substantially outperforms the competing techniques both in terms of robustness and accuracy.


Cross Validation Input Image Delaunay Triangulation Triangular Meshing Cross Validation Error 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Strecha, C., Fransens, R., van Gool, L.: Wide-baseline stereo from multiple views: a probabilistic account. In: Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. 1, pp. 552–559. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  2. 2.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision 47(1), 7–42 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  4. 4.
    Gargallo, P., Sturm, P.: Bayesian 3D modeling from images using multiple depth maps. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 885–891. IEEE, Los Alamitos (2005)Google Scholar
  5. 5.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  6. 6.
    Faugeras, O.: Three-Dimensional Computer Vision. MIT Press, Cambridge (1993)Google Scholar
  7. 7.
    Hermes, L., Buhmann, J.M.: A minimum entropy approach to adaptive image polygonization. IEEE Transactions on Image Processing 12(10), 1243–1258 (2003)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Wey
    • 1
  • Bernd Fischer
    • 1
  • Herbert Bay
    • 2
  • Joachim M. Buhmann
    • 1
  1. 1.Institute of Computational Science 
  2. 2.Computer Vision Laboratory, ETH ZurichSwitzerland

Personalised recommendations