An Experimental Comparison of Stereo Algorithms

  • Richard Szeliski
  • Ramin Zabih
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1883)


While many algorithms for computing stereo correspondence have been proposed, there has been very little work on experimentally evaluating algorithm performance, especially using real (rather than synthetic) imagery. In this paper we propose an experimental comparison of several different stereo algorithms. We use real imagery, and explore two different methodologies, with different strengths and weaknesses. Our first methodology is based upon manual computation of dense ground truth. Here we make use of a two stereo pairs: one of these, from the University of Tsukuba, contains mostly fronto-parallel surfaces; while the other, which we built, is a simple scene with a slanted surface. Our second methodology uses the notion of prediction error, which is the ability of a disparity map to predict an (unseen) third image, taken from a known camera position with respect to the input pair. We present results for both correlation-style stereo algorithms and techniques based on global methods such as energy minimization. Our experiments suggest that the two methodologies give qualitatively consistent results. Source images and additional materials, such as the implementations of various algorithms, are available on the web from


Prediction Error Ground Truth Stereo Match Stereo Algorithm Occlude Pixel 
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 2000

Authors and Affiliations

  • Richard Szeliski
    • 1
  • Ramin Zabih
    • 2
  1. 1.Microsoft ResearchRedmond
  2. 2.Cornell UniversityIthaca

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