SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Zhengxue Wang, Zhiqiang Yan✉, Jian Yang✉
PCA Lab, Nanjing University of Science and Technology, China
Python==3.11.5
PyTorch==2.1.0
numpy==1.23.5
torchvision==0.16.0
scipy==1.11.3
thop==0.1.1.post2209072238
Pillow==10.0.1
tqdm==4.65.0
All pretrained models can be found here. Please note that some variable names in the initial pretrained .pth files are not consistent with those in the latest code. Therefore, we have reuploaded the new .pth files for completeness, named xxx_R.pth.
python train.py --scale 4 --num_feats 48
x8 DSR
python train.py --scale 8 --num_feats 40
x16 DSR
python train.py --scale 16 --num_feats 40
Train on real-world RGB-D-D
python train.py --scale 4 --num_feats 24
python test.py --scale 4 --num_feats 48
x8 DSR
python test.py --scale 8 --num_feats 40
x16 DSR
python test.py --scale 16 --num_feats 40
Test on real-world RGB-D-D
python test.py --scale 4 --num_feats 24
Train & test on real-world RGB-D-D:
Train & test on synthetic NYU-v2 (x16):
Train on NYU-v2, test on RGB-D-D (x16):
We thank all reviewers for their professional and instructive suggestions.
We thank these repos sharing their codes: DKN and SUFT.
If our method proves to be of any assistance, please consider citing:
@inproceedings{wang2024sgnet,
title={Sgnet: Structure guided network via gradient-frequency awareness for depth map super-resolution},
author={Wang, Zhengxue and Yan, Zhiqiang and Yang, Jian},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={6},
pages={5823--5831},
year={2024}
}