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Official MegEngine implementation of CREStereo(CVPR 2022 Oral).
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# [CVPR 2022] Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation
This repository contains [MegEngine](https://github.com/MegEngine/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 **(Oral)**
**[Paper](https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.pdf) | [ArXiv](https://arxiv.org/abs/2203.11483) | [BibTeX](#citation)**
<img src="img/teaser.jpg">
## Datasets
### The Proposed Dataset
#### Download
There are **two ways** to download the dataset(~400GB) proposed in our paper:
- Download using shell scripts `dataset_download.sh`
```shell
sh dataset_download.sh
```
the dataset will be downloaded and extracted in `./stereo_trainset/crestereo`
- Download from BaiduCloud [here](https://pan.baidu.com/s/1iB96-ftCgPFTlrj220qw8Q)(Extraction code: `aa3g`) and extract the tar files manually.
#### Disparity Format
The disparity is saved as `.png` uint16 format which can be loaded using opencv `imread` function:
```python
def get_disp(disp_path):
disp = cv2.imread(disp_path, cv2.IMREAD_UNCHANGED)
return disp.astype(np.float32) / 32
```
### Other Public Datasets
Other public datasets we use including
- [SceneFlow](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
- [Sintel](http://sintel.is.tue.mpg.de/stereo)
- [Middlebury](https://vision.middlebury.edu/stereo/data/)
- [ETH3D](https://www.eth3d.net/datasets#low-res-two-view-training-data)
- [KITTI 2012/2015](http://www.cvlibs.net/datasets/kitti/eval_stereo.php)
- [Falling Things](https://research.nvidia.com/publication/2018-06_Falling-Things)
- [InStereo2K](https://github.com/YuhuaXu/StereoDataset)
- [HR-VS](https://drive.google.com/file/d/1SgEIrH_IQTKJOToUwR1rx4-237sThUqX/view)
## Dependencies
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
```bash
python3 -m pip install -r requirements.txt
```
We also provide docker to run the code quickly:
```bash
docker run --gpus all -it -v /tmp:/tmp ylmegvii/crestereo
shotwell /tmp/disparity.png
```
## Inference
Download the pretrained MegEngine model from [here](https://drive.google.com/file/d/1Wx_-zDQh7BUFBmN9im_26DFpnf3AkXj4/view) and run:
```shell
python3 test.py --model_path path_to_mge_model --left img/test/left.png --right img/test/right.png --size 1024x1536 --output disparity.png
```
## Training
Modify the configurations in `cfgs/train.yaml` and run the following command:
```shell
python3 train.py
```
You can launch a TensorBoard to monitor the training process:
```shell
tensorboard --logdir ./train_log
```
and navigate to the page at `http://localhost:6006` in your browser.
## Acknowledgements
Part of the code is adapted from previous works:
- [RAFT](https://github.com/princeton-vl/RAFT)(code base)
- [LoFTR](https://github.com/zju3dv/LoFTR)(attention module)
- [HSMNet](https://github.com/gengshan-y/high-res-stereo)(data augmentaion)
We thank all the authors for their awesome repos.
## Citation
If you find the code or datasets helpful in your research, please cite:
```
@inproceedings{li2022practical,
title={Practical stereo matching via cascaded recurrent network with adaptive correlation},
author={Li, Jiankun and Wang, Peisen and Xiong, Pengfei and Cai, Tao and Yan, Ziwei and Yang, Lei and Liu, Jiangyu and Fan, Haoqiang and Liu, Shuaicheng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={16263--16272},
year={2022}
}
```
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