Hey guys,
Recently I was working a little bit with stereo vision. But I came across a serious problem. I know i was implementing a fairly simple DSI (Disparity Space Image) based Stereo Matching (constructed using Normalized Cross Correlation with RGB as feature) with dynamic programming to minimize error. Pretty simple model, But too many local errors. And there were a lot of things like occlusion, inter scan-line consistency that I ignored.
Observations
But the thing I Observed was that if I use the block matching model, I will never be able to get any information regarding curvature. My disparity map will be like step functions at Intensity edges (since I am using RGB feature for correlation) - Pretty obvious from the primitive energy minimization model. But I have no other choice of choosing other models for two reasons - One, I am not that strong in Math to understand GraphCuts quickly. Two,I need speed more than accuracy, but hey, I am greedy and I would appreciate accuracy too.
Now for the queries :
- If I use other forms of feature vectors obtained from neighborhood relations (or) other Transformations, Its kinda intuitive to give better results as I have more features than just RGB. But what do I choose if I need to get information regarding curvature of a plain cylinder which will have same features all over the surface and (it actually came to my mind now) I think the disparity map will be like steps ( " _/'''\_ " ,didnt get it, did you?). It can never be more than that if I use spatial domain features.So any suggestions on going around that problem?
- If I need some decent accuracy in the estimation of disparity but quickly, Is there any other methods that You can suggest? I have tried graphcut algorithms from Middlebury and It gives awesome results but very slow and I have also had a look at NVIDIA's Push Relabel method which works only if someone has NVIDIA card. Sad.
Expectations
I will be really happy if you (the reader of this long and boring question) can guide me towards some resources, share your experience, suggest a method, etc. I will be happy even if you try to think for a moment for answering this. I don't know whether this question belongs to this forum or not, but I find many people interested in OpenCV here and found people working on stereo too. Moderators can close this question if they feel so. But I thought I should try my luck here. Sorry if I am breaking any rules.
Regarding OpenCV
- Are there GraphCut Energy Minimisation algorithms (implemented / in pipeline) for Stereo in OpenCV?
- Can you guys start a Discussion Forum as well apart from Q&A Forum? Is it feasible?
Thanks for trying to help
Regards,
Prasanna S