This repo is for the paper "Learning from Synthetic Shadows for Shadow Detection and Removal". We present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and pipeline to synthesize it. We further show how to use SynShadow for robust and efficient shadow detection and removal.

In this repo, we provide

• SynShadow dataset: ./datasets
• SP+M implementation: ./src
• Trained models and results: below

If you find this code or dataset useful for your research, please cite our paper:

@article{inoue2021learning,
  title={{Learning from Synthetic Shadows for Shadow Detection and Removal}},
  author={Inoue, Naoto and Yamasaki, Toshihiko},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2021},
  volume={31},
  number={11},
  pages={4187-4197},
  doi={10.1109/TCSVT.2020.3047977}
}

We provide the models for shadow detection and removal for convenience. Downloaded models should be placed under ./checkpoints.

ALl the results are in 480x640. BER is reported for 480x640 images. Below are results evaluated on ISTD test set. DSDNet++ is a modified variant of DSDNet.

ALl the results are in 480x640. For the pre-trained weights, we only provide SP+M weights, since this repository has full implementation of it. RMSE is reported for 480x640 images.

Model: SP+M

Model: DHAN

Note: we have accidentially removed some files and cannot provide some results.