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Context-Aware Video Reconstruction for Rolling Shutter Cameras

This repository contains the source code for the paper: Context-Aware Video Reconstruction for Rolling Shutter Cameras (CVPR2022). Given two rolling shutter frames at adjacent times 0 and 1, the proposed CVR can synthesize a high-quality intermediate global shutter frame corresponding to any time 0<t<1, i.e., generating a smooth and coherent global shutter video.

From left to right: Overlayed rolling shutter images, recovered global shutter videos by RSSR (ICCV2021) and our CVR (this paper), respectively.

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 the baseline of our CVR network comes from RSSR. Similarly, we first need to configure the following packages:

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

Install reblur_package

cd ./reblur_package
python setup.py install

Demo with our pretrained model

Please download the pretrained model, including network models of RSSR, CVR, and CVR*, respectively. Then unzip these three subfolders to the model_weights folder of the main directory.

You can now test our method with the provided images in the demo folder.

Note that our CVR can be tested directly. To test CVR*, you need to change the weight's path (--log_dir) and the model's type (--model_type) in files demo.sh, demo_video.sh, and inferencce.sh.

To generate the global shutter images corresponding to time steps 0.5 and 1.0, simply run

sh demo.sh

To generate multiple global shutter video frames (stored in .fig format), e.g. 10× temporal upsampling, please run

sh demo_video.sh

The visualization results will be stored in the experiments folder. Note that additional examples in the dataset can be tested similarly.

Datasets

  • Carla-RS and Fastec-RS: Download these two datasets 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_carla.sh' and 'train_fastec.sh' with  #
# !! your own local paths, before run following command!!      #

sh train_carla.sh
sh train_fastec.sh

You can run following commands to obtain the quantitative evaluations.

# !! Please update the path to test data in 'inference.sh'
# !! with your own local path, before run following command!!

sh inference.sh

Note that --load_1st_GS=0 denotes the correction evaluation corresponding to time 1.0, and --load_1st_GS=1 denotes the correction evaluation corresponding to time 0.5.

Citations

Please cite our paper if necessary:

@inproceedings{fan_CVR_CVPR22,
  title={Context-Aware Video Reconstruction for Rolling Shutter Cameras},
  author={Fan, Bin and Dai, Yuchao and Zhang, Zhiyuan and Liu, Qi and He, Mingyi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

@inproceedings{fan_RSSR_ICCV21,
  title={Inverting a rolling shutter camera: bring rolling shutter images to high framerate global shutter video},
  author={Fan, Bin and Dai, Yuchao},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4228--4237},
  year={2021}
}

Statement

This project is for research purpose only, please contact us for the licence of commercial use. For any other questions or discussion please contact: binfan@mail.nwpu.edu.cn