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.