NeRFReN: Neural Radiance Fields with Reflections
This is the code release for our CVPR2022 paper, NeRFReN: Neural Radiance Fields with Reflections.
- Project Page
- Paper (Conference, ArXiv)
- Video
Update
- 07/28/2022: Initial code release.
- 08/01/2022: Pretrained models for all RFFR scenes are released.
Setup
- Install PyTorch>=1.8
- Install other dependencies:
pip install -r requirements.txt - Download our Real Forward Facing with Reflections (RFFR) dataset from Google Drive, and extract to
load/ - (Optional) Download pretrained models from Google Drive, and extract to
checkpoints/
The correct file structure should be like:
checkpoints/
|
-- art1_pretrain/
|
-- ...
load/
|
-- rffr/
|
-- art1/
|
-- ...
Training
We provide training scripts for all the 6 RFFR scenes in scripts/nerfren. Run the scripts to perform training:
sh scripts/nerfren/train_art1.sh
To train the NeRF baseline, run scripts/nerf/train.sh and specify the scene as arguments:
sh scripts/nerf/train.sh art1
The training process by default uses all available GPUs. Set CUDA_VISIBLE_DEVICES environment variable to specify the GPUs to be used.
The network checkpoints and visualizations are stored in checkpoints/ by default, and tensorboard logs can be found in runs/.
Testing
The testing process generates images from spiral poses for visualization. To test a pretrained model, run scripts/nerfren/test_pretrain.sh and specify the scene as arguments:
sh scripts/nerfren/test_pretrain.sh art1
To test on our pretrained models, please make sure you have downloaded the checkpoints and organized the files correctly as demonstrated in the Setup section.
The testing results are saved to results/ by default.
Citation
If you find our work useful, please cite:
@InProceedings{Guo_2022_CVPR,
author = {Guo, Yuan-Chen and Kang, Di and Bao, Linchao and He, Yu and Zhang, Song-Hai},
title = {NeRFReN: Neural Radiance Fields With Reflections},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {18409-18418}
}
Acknowledgement
Part of the code is borrowed or adapted from the following great codebases: