SUNet: Symmetric Undistortion Network for Rolling Shutter Correction
This repository contains the source code for the paper: SUNet: Symmetric Undistortion Network for Rolling Shutter Correction (ICCV2021)
Installation
Install the dependent packages:
pip install -r requirements.txt
The code is tested with PyTorch 1.6.0 with CUDA 10.2.89.
Note that in our implementation, we borrowed some modules from DeepUnrollNet.
Install correlation package
cd ./package_correlation
python setup.py install
Install differentiable forward warping package
cd ./package_forward_warp
python setup.py install
Install core package
cd ./package_core
python setup.py install
Demo with our pretrained model
You can now test our model with the provided images in the demo
folder.
To do this, simply run
sh demo.sh
The visualization results will be stored in the experiments
folder. Other examples in the dataset can be tested similarly.
Datasets
- Carla-RS and Fastec-RS: Download them to your local computer from here.
Training and evaluating
You can run following commands to re-train the network.
# !! Please update the corresponding paths in 'train.sh' with #
# !! your own local paths, before run following command!! #
sh train.sh
You can run following commands to obtain the quantitative evaluations.
bash download_pretrained_models.sh
# !! Please update the path to test data in 'inference.sh'
# !! with your own local path, before run following command!!
sh inference.sh
Citations
Please cite our paper if necessary:
@inproceedings{fan_SUNet_ICCV21,
title={SUNet: Symmetric Undistortion Network for Rolling Shutter Correction},
author={Fan, Bin and Dai, Yuchao and He, Mingyi},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={4541--4550},
year={2021}
}
Contact
Please drop me an email for further problems or discussion: [email protected]