This repository contains MegEngine implementation of our paper:

Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu
CVPR 2022

arXiv | BibTeX

There are two ways to download the dataset(~400GB) proposed in our paper:

• Download using shell scripts dataset_download.sh

sh dataset_download.sh

the dataset will be downloaded and extracted in ./stereo_trainset/crestereo

• Download from BaiduCloud here(Extraction code: aa3g) and extract the tar files manually.

The disparity is saved as .png uint16 format which can be loaded using opencv imread function:

def get_disp(disp_path):
    disp = cv2.imread(disp_path, cv2.IMREAD_UNCHANGED)
    return disp.astype(np.float32) / 32

Other public datasets we use including

• SceneFlow
• Sintel
• Middlebury
• ETH3D
• KITTI 2012/2015
• Falling Things
• InStereo2K
• HR-VS

CUDA Version: 10.1, Python Version: 3.6.9

• MegEngine v1.8.2
• opencv-python v3.4.0
• numpy v1.18.1
• Pillow v8.4.0
• tensorboardX v2.1

python3 -m pip install -r requirements.txt

Download the pretrained MegEngine model from here and run:

python3 test.py --model_path path_to_mge_model --left img/test/left.png --right img/test/right.png --size 1024x1536 --output disparity.png

Modify the configurations in cfgs/train.yaml and run the following command:

python3 train.py

You can launch a TensorBoard to monitor the training process:

tensorboard --logdir ./train_log

and navigate to the page at http://localhost:6006 in your browser.

Part of the code is adapted from previous works:

• RAFT(code base)
• LoFTR(attention module)
• HSMNet(data augmentaion)

We thank all the authors for their awesome repos.

If you find the code or datasets helpful in your research, please cite:

@misc{Li2022PracticalSM,
      title={Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation},
      author={Jiankun Li and Peisen Wang and Pengfei Xiong and Tao Cai and Ziwei Yan and Lei Yang and Jiangyu Liu and Haoqiang Fan and Shuaicheng Liu},
      year={2022},
      eprint={2203.11483},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}