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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)

Abstract

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.

Keywords

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.

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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

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