I’m trying to figure out what are currently the two most efficent algorithms that permit, starting from a L/R 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 – toooo long for a first review)
Anyone could please give me some advices for orienting 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-taken, calibration infos)?
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: http://stereo.jpn.org/eng/stphmkr/index.html
I tried the “auto-adjustment” feature and seems really effective. Too bad there is no source code…
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Although I don’t have any specific expertise in this field, I can make some recommendations with regard to your research.
A Google Scholar search can be a bit daunting at first, but you can begin to filter the results by noting the number of citations a document has. (Citation count often correlates to how authoritative a given article is. Of course, this is only one metric and isn’t foolproof: older documents tend to have more citations than newer ones simply because they’ve had more time to collect them. But it is a good starting point.)
For example, Di Stefano’s paper has been cited by over 200 authors, so that might put into question your suggestion that his approach as “inaccurate” (unless that is the consensus of the other citations). On the other hand, the following paper has been cited over 4000 times in the last 10 years and so would probably be a good place to start:
- A taxonomy and evaluation of dense two-frame stereo correspondence algorithms (Scharstein, ’02)
Look for papers that offer reviews or aggregated summaries of other approaches, such as
- Review of stereo vision algorithms: from software to hardware (Lazaros, ’08)
I would also recommend looking at some of the standard textbooks in the field, such as:
- An Introduction to 3D Computer Vision Techniques and Algorithms (Cyganek, ’09)
- Multiple View Geometry in Computer Vision (Hartley, ’04; may be more dated)