Ping-Hu / NSRD

[CVPR 2024] Neural Super-Resolution for Real-time Rendering with Radiance Demodulation

Neural Super-Resolution for Real-time Rendering with Radiance Demodulation (CVPR 2024)

Paper | Datasets

Installation

Tested on Windows + CUDA 11.3 + Pytorch 1.12.1

Install environment:

git clone https://github.com/riga2/NSRD.git
cd NSRD
conda create -n NSRD python=3.9
conda activate NSRD
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

The following training and testing take the Bistro scene (X4) as an example.

Training

1. Make a folder named "dataset", and then download the dataset and put it inside.

|--configs
|--dataset
    |--Bistro
        |--train
            |---GT
                |--0
                |--1
                ...
            |---X4
                |--0
                |--1
                ...
        |---test
            |---GT
                ...
            |---X4
                ...

2. Use the Anaconda Prompt to run the following commands to train. The trained model is stored in "experiment\Bistro_X4\model".

cd src
.\script\BistroX4_train.bat

Testing

1. Run the following commands to perform super-resolution on the LR lighting components. The SR results are stored in "experiment\Bistro_X4\sr_results_x4".

cd src
.\test_script\BistroX4_test.bat

2. Run the following commands to perform remodulation on the SR lighting components. The final results are stored in "experiment\Bistro_X4\final_results_x4".

cd src
python remodulation.py --exp_dir ../experiment/Bistro_X4 --gt_dir ../dataset/Bistro/test/GT

Citation

@inproceedings{li2024nsrd,
  title={Neural Super-Resolution for Real-time Rendering with Radiance Demodulation},
  author={Li, Jia and Chen, Ziling and Wu, Xiaolong and Wang, Lu and Wang, Beibei and Zhang, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}