Paper

This is the official PyTorch implementation. This works aims at removing various types of waterdrops for driving cars on rainy days. We also provide a large-scale synthetic dataset for the video waterdrop removal task.

Qiang Wen, Yue Wu, Qifeng Chen
The Hong Kong University of Science and Technology
IEEE International Conference on Robotics and Automation (ICRA), 2023

• Pytorch 1.9
• OpenCV-Python

If conda has been installed, you can directly build the running environment via:

conda env create -f environment.yaml

An environment named "th" will be created.

• Download the training dataset, and put it in dataset/;
• To train a model:

$ bash train.sh

You can also use the command tensorboard --logdir=runs to visually check the training results.

• Download the pretrained model, and put it in checkpoints_waterdrop/;
• Download the test dataset, and unzip it in dataset/;
• To test:

$ bash test.sh

You can choose the test on the synthetic dataset or real-world dataset by specifying --data_type

If you find this repository useful for your research, please cite the following work.

@inproceedings{wen2023video,
  title={Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes},
  author={Wen, Qiang and Wu, Yue and Chen, Qifeng},
  booktitle={International Conference on Robotics and Automation (ICRA)},
  year={2023},
  organization={IEEE}
}