The implementations of CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous Driving. We achieve state-of-the-art accuracy on KITTI-2015 flow benchmark.
- 2022-5 [NEW:fire:] CSFlow is accepted to IV 2022 as an ORAL PRESENTATION. (Top 10%)
- 2022-4 [NEW:fire:] CSFlow is accepted to 2022 33rd IEEE Intelligent Vehicles Symposium (IV).
python setup.py develop
The pretrained model that the paper used can be found there:
Download link 1 (Tencent WeiYun):
https://share.weiyun.com/5t6TadPB
Download link 2 (Baidu Cloud):
https://pan.baidu.com/s/1Hcj-sm5t0h6lYckOBiWjzA?pwd=6kur
Download link 3 (Google Drive):
https://drive.google.com/drive/folders/1_Fb4eLfT4bZo3g8hCX5cO28lMsDKuHvE?usp=sharing
The content in the above links are consistent, if you encounter network problems, you can try switching to the other link. They can also be found in the Github Releases tab.
To train, use the following command format:
python ./tools/train.py
--model CSFlow
--dataset Chairs
--data_root $YOUR_DATA_PATH$
--batch_size 1
--name csflow-test
--validation Sintel
--val_Sintel_root $YOUR_DATA_PATH$
--num_steps 100
--lr 0.0004
--image_size 368 496
--wdecay 0.0001
To eval, use the following command format:
python ./tools/eval.py
--model CSFlow
--restore_ckpt ./checkpoints/CSFlow-kitti.pth
--eval_iters 24
--validation KITTI
--val_KITTI_root $YOUR_DATA_PATH$
For more details, please check the code or refer our paper.
* local: you should create this folder in your local repository and these folders will not upload to remote repository.
├── data (local) # Store test/training data
├── checkpoints (local) # Store the checkpoints
├── runs (local) # Store the training log
├── opticalflow # All source code
| ├─ api # Called by tools
| ├─ core # Core code call by other directorys. Provide dataset, models ....
| | ├─ dataset # I/O of each dataset
| | ├─ model # Models, includeing all the modules that derive nn.module
| | ├─ util # Utility functions
├── tools # Scripts for test and train
├── work_dirs (local) # For developers to save thier own codes and assets
If you find our project helpful in your research, please cite with:
@article{shi2022csflow,
title={CSFlow: Learning optical flow via cross strip correlation for autonomous driving},
author={Shi, Hao and Zhou, Yifan and Yang, Kailun and Yin, Xiaoting and Wang, Kaiwei},
journal={arXiv preprint arXiv:2202.00909},
year={2022}
}
Hao Shi，YiFan Zhou
If you have any questions, welcome to e-mail me: [email protected], and I will try my best to help you. =)