There are several typical steps to try to speed up disparity map calculation:
Thank you very much for your answer.
To 1.
I try to get a 3D reconstruction with the highest resolution, so resizing the images is not a suitable option in this case.
To 2. and 3.
I already tried different parameters and figured out the optimums.
To 4.
I already tried the StereoBM (and the CUDA version is indeed very fast!) but unfortunately it is giving very bad results (in comparison of StereoSGBM).
Do you think the StereoSGBM is as well implementable on CUDA (with parallel computation) ?
mirnyy, happy to add my thoughts. It sounds like you have a challenging set of requirements.
High spatial resolution across the entire image necessitates high computation requirements. It is hard to imagine otherwise. Scaling x and y also scales number of disparities and window size by approximately the same factor. Disparity effort scales roughly with x * y * (num_disp+wsz), or O(scale^3)
If any objects needing detailed examination are a small part of the overall scene, then it may be possible to use a couple passes: first, an overall disparity map at low resolution to get the overall scene. Then, a second disparity map, over a small region of interest, to resolve fine detail.
I'm not a CUDA expert so can't comment.
Asked: 2017-02-22 09:20:56 -0600
Seen: 1,395 times
Last updated: Feb 22 '17
Smaller images? For "real-time", resolution is usually VGA or QVGA.
I also did the same, lower resolution images around 600x400 (approx cont remember) also posted https://www.youtube.com/watch?v=T77i_P-7otM (Vlog) its not very clear map but works for realtime