Upload README.md

README.md

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-<p align="center">
-<h3 align="center"> DORNet: A Degradation Oriented and Regularized Network for <br> Blind Depth Super-Resolution
-<br>
-:star2: CVPR 2025 (Oral Presentation) :star2:
-</h3>
-  
-<p align="center"><a href="https://scholar.google.com/citations?user=VogTuQkAAAAJ&hl=zh-CN">Zhengxue Wang</a><sup>1*</sup>, 
-<a href="https://yanzq95.github.io/">Zhiqiang Yan✉</a><sup>1*</sup>, 
-<a href="https://jspan.github.io/">Jinshan Pan</a><sup>1</sup>,
-<a href="https://guangweigao.github.io/">Guangwei Gao</a><sup>2</sup>,
-<a href="https://cszn.github.io/">Kai Zhang</a><sup>3</sup>,
-  <a href="https://scholar.google.com/citations?user=6CIDtZQAAAAJ&hl=zh-CN">Jian Yang✉</a><sup>1</sup>  <!--&Dagger;-->
-</p>
-
-<p align="center">
-  <sup>*</sup>Equal contribution&nbsp;&nbsp;&nbsp;
-  <sup>✉</sup>Corresponding author&nbsp;&nbsp;&nbsp;<br>
-  <sup>1</sup>Nanjing University of Science and Technology&nbsp;&nbsp;&nbsp;
-  <br>
-  <sup>2</sup>Nanjing University of Posts and Telecommunications&nbsp;&nbsp;&nbsp;
-  <sup>3</sup>Nanjing University&nbsp;&nbsp;&nbsp;
-</p>
-
-<p align="center">
-<img src="Figs/Pipeline.png", width="800"/>
-</p>
-
-
-Overview of DORNet. Given $\boldsymbol D_{up}$ as input, the degradation learning first encodes it to produce degradation representations $\boldsymbol {\tilde{D}}$  and $\boldsymbol D $. Then, $\boldsymbol {\tilde{D}}$,  $\boldsymbol D $, $\boldsymbol D_{lr} $, and $\boldsymbol I_{r}$ are fed into multiple degradation-oriented feature transformation (DOFT) modules, generating the HR depth $\boldsymbol D_{hr}$. Finally, $\boldsymbol D$ and $\boldsymbol D_{hr}$ are sent to the degradation regularization to obtain $\boldsymbol D_{d}$, which is used as input for the degradation loss $\mathcal L_{deg}$ and the contrastive loss $\mathcal L_{cont}$. The degradation regularization only applies during training and adds no extra overhead in testing.
-
-## Dependencies
-
-```bash
-Python==3.11.5
-PyTorch==2.1.0
-numpy==1.23.5 
-torchvision==0.16.0
-scipy==1.11.3
-Pillow==10.0.1
-tqdm==4.65.0
-scikit-image==0.21.0
-mmcv-full==1.7.2
-```
-
-## Datasets
-
-[RGB-D-D](https://github.com/lingzhi96/RGB-D-D-Dataset)
-
-[TOFDSR](https://yanzq95.github.io/projectpage/TOFDC/index.html)
-
-[NYU-v2](https://cs.nyu.edu/~fergus/datasets/nyu_depth_v2.html)
-
-## Models
-
-Pretrained models can be found in  <a href="https://github.com/yanzq95/DORNet/tree/main/checkpoints">checkpoints</a>.
-
-
-## Training
-
-For the RGB-D-D and NYU-v2 datasets, we use a single GPU to train our DORNet. For the larger TOFDC dataset, we employ multiple GPUs to accelerate training.
-
-### DORNet
-```
-Train on real-world RGB-D-D
-> python train_nyu_rgbdd.py
-Train on real-world TOFDSR
-> python -m torch.distributed.launch --nproc_per_node 2 train_tofdsr.py --result_root 'experiment/TOFDSR'
-Train on synthetic NYU-v2
-> python train_nyu_rgbdd.py
-```
-
-### DORNet-T
-```
-Train on real-world RGB-D-D
-> python train_nyu_rgbdd.py --tiny_model
-Train on real-world TOFDSR
-> python -m torch.distributed.launch --nproc_per_node 2 train_tofdsr.py --result_root 'experiment/TOFDSR_T' --tiny_model
-Train on synthetic NYU-v2
-> python train_nyu_rgbdd.py --tiny_model
-```
-
-## Testing
-
-### DORNet
-```
-Test on real-world RGB-D-D
-> python test_nyu_rgbdd.py
-Test on real-world TOFDSR
-> python test_tofdsr.py
-Test on synthetic NYU-v2
-> python test_nyu_rgbdd.py
-```
-
-### DORNet-T
-```
-Test on real-world RGB-D-D
-> python test_nyu_rgbdd.py --tiny_model
-Test on real-world TOFDSR
-> python test_tofdsr.py --tiny_model
-Test on synthetic NYU-v2
-> python test_nyu_rgbdd.py --tiny_model
-```
-
-## Experiments
-
-### Quantitative comparison
-
-<p align="center">
-<img src="Figs/Params_Time.png", width="500"/>
-<br>
-Complexity on RGB-D-D (w/o Noisy) tested by a 4090 GPU. A larger circle diameter indicates a higher inference time.
-</p>
-
-
-
-### Visual comparison
-
-<p align="center">
-<img src="Figs/RGBDD.png", width="1000"/>
-<br>
-Visual results on the real-world RGB-D-D dataset (w/o Noise).
-</p>
-
-
-## Citation
-
-If our method proves to be of any assistance, please consider citing:
-```
-@inproceedings{wang2025dornet,
-  title={DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution},
-  author={Wang, Zhengxue and Yan, Zhiqiang and Pan, Jinshan and Gao, Guangwei and Zhang, Kai and Yang, Jian},
-  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
-  pages={15813--15822},
-  year={2025}
-}
-```
-
+---
+title: DORNet
+emoji: 🌍
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false
+---
+[DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution](https://arxiv.org/pdf/2410.11666)