From Shadow Generation to Shadow Removal.
This code uses the following libraries
Generate the training data using the original ISTD dataset and the code gettraindata.m
or download the training data from: GoogleDrive: ISTD | BaiduNetdisk: ISTD (Access code: 1111)
Your ~/PAISTD8/
folder should look like this
PAISTD8
├── train/
├── train_A/
│ └── 90-1.png
│ └── ...
├── train_B/
│ └── ...
└── ...
GoogleDrive: ISTD
BaiduNetdisk: ISTD (Access code: 1111)
Train
train.py
train.py
Test
(netG_1.pth)
and (netG_2.pth)
in test.py
test.py
evaluate.m
evaluate.m
GoogleDrive: ISTD
BaiduNetdisk: ISTD (Access code: 1111)
GoogleDrive: ISTD
BaiduNetdisk: ISTD (Access code: 1111)
GoogleDrive: ISTD
BaiduNetdisk: ISTD (Access code: 1111)
Method | Shadow Region | Non-shadow Region | All |
---|---|---|---|
Le & Samaras (ECCV20) | 11.3 | 3.7 | 4.8 |
G2R-ShadowNet (Ours) | 9.6 | 3.8 | 4.7 |
Results in shadow and non-shadow regions are computed on each image first and then compute the average of all images in terms of RMSE.
Code is implemented based on Mask-ShadowGAN and LG-ShadowNet.
All codes will be released to public soon.
@inproceedings{liu2021from,
title={From Shadow Generation to Shadow Removal},
author={Liu, Zhihao and Yin, Hui and Wu, Xinyi and Wu, Zhenyao and Mi, Yang and Wang, Song},
booktitle={CVPR},
year={2021}
}