SIGGRAPH 2024 Conference Paper: Deep Fourier-based Arbitrary-scale Super-resolution for Real-time Rendering
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Our work has been selected as SIGGRAPH 2024 Technical Papers Trailer !
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The Trailer
Project page:https://iamxym.github.io/DFASRR.github.io/
git clone https://github.com/iamxym/Deep-Fourier-based-Arbitrary-scale-Super-resolution-for-Real-time-Rendering.git
cd Deep-Fourier-based-Arbitrary-scale-Super-resolution-for-Real-time-Rendering
pip3 install -r requirements.txt
Note: In our experiments, the version of Pytorch is 2.0.1+cu117.
nvidia-cublas-cu11==11.10.3.66
nvidia-cublas-cu12==12.3.4.1
nvidia-cuda-cupti-cu11==11.7.101
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-nvrtc-cu12==12.3.107
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cuda-runtime-cu12==12.3.101
nvidia-cudnn-cu11==8.5.0.96
nvidia-cudnn-cu12==8.9.7.29
nvidia-cufft-cu11==10.9.0.58
nvidia-curand-cu11==10.2.10.91
nvidia-cusolver-cu11==11.4.0.1
nvidia-cusparse-cu11==11.7.4.91
nvidia-nccl-cu11==2.14.3
nvidia-nvtx-cu11==11.7.91
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Download the pretrained models and the dataset example for inference from here
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Unzip and make the file structure as follows:
Deep-Fourier-based-Arbitrary-scale-Super-resolution-for-Real-time-Rendering
|__ data
|__ res
|__ Model
|__ README.md
|__ inference.py
|__ Loaders.py
...
python3 inference.py
And then you will found the super-resolution result in res/
If you think this project is helpful, please feel free to leave a star or cite our paper:
@inproceedings{zhang2024deep,
title={Deep Fourier-based Arbitrary-scale Super-resolution for Real-time Rendering},
author={Zhang, Haonan and Guo, Jie and Zhang, Jiawei and Qin, Haoyu and Feng, Zesen and Yang, Ming and Guo, Yanwen},
booktitle={ACM SIGGRAPH 2024 Conference Papers},
pages={1--11},
year={2024}
}