2018-CV-Final-Project
Dependencies
- Pymaxflow (https://github.com/pmneila/PyMaxflow)
- Cupy 2.2.0
- Chainer 3.2.0 (GPU version)
- Keras 2.0.9
Usage
python3 main.py --setting <option>
<option>:
- 0 : Using Pretrained MCCNN cost + Cost Volume Filtering
- 1 : Using Pretrained MCCNN cost + Cost Volume Filtering + Local Expansion for refining (very slow/ better performance)
- 2 : Using MCCNN cost trained by us + Cost Volume Filtering (slow)
- 3 : Using MCCNN cost trained by us + Cost Volume Filtering + Local Expansion for refining (very slow/ better performance)
Evaluate
python3 eval_middleBury.py
Result (option 0/option 1)
Tsukuba:
Venus:
Cones:
>
Teddy:
The oringal data for this challenge is not provided due to license reasons.
References
[1] A. Hosni, C. Rhemann, M. Bleyer, C. Rother, and M. Gelautz. Fast cost-volume filtering for visual correspondence and beyond. IEEE Trans. Pattern Anal. Mach. Intell., 35(2):504–511, 2013.
[2] Zbontar, Jure, and Yann LeCun. "Stereo matching by training a convolutional neural network to compare image patches." Journal of Machine Learning Research 17.1-32 (2016): 2.
[3] Taniai, Tatsunori, et al. "Continuous 3D label stereo matching using local expansion moves." IEEE transactions on pattern analysis and machine intelligence 40.11 (2018): 2725-2739.
[4] Boykov, Yuri, Olga Veksler, and Ramin Zabih. "Fast approximate energy minimization via graph cuts." IEEE Transactions on pattern analysis and machine intelligence 23.11 (2001): 1222-1239.
[5] MCCNN code reference: https://github.com/t-taniai/mc-cnn-chainer