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Total Absolute Gaussian Curvature for Stereo Prior

  • Hiroshi Ishikawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4844)

Abstract

In spite of the great progress in stereo matching algorithms, the prior models they use, i.e., the assumptions about the probability to see each possible surface, have not changed much in three decades. Here, we introduce a novel prior model motivated by psychophysical experiments. It is based on minimizing the total sum of the absolute value of the Gaussian curvature over the disparity surface. Intuitively, it is similar to rolling and bending a flexible paper to fit to the stereo surface, whereas the conventional prior is more akin to spanning a soap film. Through controlled experiments, we show that the new prior outperforms the conventional models, when compared in the equal setting.

Keywords

Convex Hull Gaussian Curvature Belief Propagation Prior Model Stereo Vision 
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 2007

Authors and Affiliations

  • Hiroshi Ishikawa
    • 1
  1. 1.Department of Information and Biological Sciences, Nagoya City University, Nagoya 467-8501Japan

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