Official Implementation of SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
- Project page still in the works.
- Create conda environment
conda create -n smurf python=3.8
- Activate environment
conda activate smurf
- Clone the repository
git clone https://github.com/Jho-Yonsei/SMURF.git
cd SMURF
- Install packages
pip3 install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip3 install -r requirements.txt
- Check where your environment path is (
$ conda env list
), and copy the files infor_chrono_view_embedding
directory totorchdiffeq
package
cp ./for_chrono_view_embedding/* {environment_path}/lib/python3.8/site-packages/torchdiffeq/_impl/
Dataset is from Deblur-NeRF and you can download the dataset from HERE.
Download synthetic_camera_motion_blur
and real_camera_motion_blur
directories from the drive, and put them into the following sturcture:
- SMURF/
- data/
- synthetic_camera_motion_blur/
- blurcozy2room/
- blurfactory/
...
- real_camera_motion_blur/
- blurball/
- blurbasket/
...
We provide the config files of all scenes in synthetic (5) and real-world (10) scene dataset.
So, if you want to train factory
scene from synthetic dataset on GPU 0:
python3 train.py --config ./configs/camera_motion_blur_synthetic/factory.txt --device 0
If you want to train girl
scene from real-world dataset on GPU 1:
python3 train.py --config ./configs/camera_motion_blur_real/girl.txt --device 1
You can adjust the hyperparameters to conduct ablative experiments:
--num_warp {N}
: number of warped rays (Default: 8
)
--chrono_view False
: only with time embedding (Default: True
)
--res_momentum False
: deactivate residual momentum (Default: True
)
If you have trained for factory
scene and want to render the test images and spiral video:
python3 train.py --config ./configs/camera_motion_blur_synthetic/factory.txt --device 0 --ckpt ./work_dir/camera_motion_blur_synthetic/factory/factory.th --render_only 1
To be released.
Please cite this work if you find it useful:
@article{lee2024smurf,
title={SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields},
author={Lee, Jungho and Lee, Dogyoon and Lee, Minhyeok and Kim, Donghyung and Lee, Sangyoun},
journal={arXiv preprint arXiv:2403.07547},
year={2024}
}
This repo is based on TensoRF, Deblur-NeRF, and our work is hugely influenced by Neural-ODE.
Thanks to the original authors for their awesome works!
- Release source code.
- Update README file.
- Upload pretrained weights.