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I'm trying to figure out what are currently the two most efficent algorithms that permit, starting from a Left/Right pair of stereo images created using a traditional camera (so affected by some epipolar lines misalignment), to produce a pair of adjusted images plus their depth information by looking at their disparity.

Actually I've found lots of papers about these two methods, like:

  • "Computing Rectifying Homographies for Stereo Vision" (Zhang - seems one of the best for rectification only)
  • "Three-step image rectification" (Monasse)
  • "Rectification and Disparity" (slideshow by Navab)
  • "A fast area-based stereo matching algorithm" (Di Stefano - seems a bit inaccurate)
  • "Computing Visual Correspondence with Occlusions via Graph Cuts" (Kolmogorov - this one produces a very good disparity map, with also occlusion informations, but is it efficient?)
  • "Dense Disparity Map Estimation Respecting Image Discontinuities" (Alvarez - too long for a first review)

Could someone please give me some advice for diving into this wide topic?

What kind of algorithm/method should I treat first, considering that I'll work on a very simple input: a pair of left and right images and nothing else, no more information (some papers are based on additional, pre-obtained, calibration information)?

Speaking about working implementations, the only interesting results I've seen so far belongs to this piece of software, but only for automatic rectification, not disparity.

I tried the "auto-adjustment" feature and it seems really effective. Too bad there is no source code.

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