NeuroFluid

Code reposity for this paper:
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields.
Shanyan Guan, Huayu Deng, Yunbo Wang^†, Xiaokang Yang
ICML 2022
[Paper] [Project Page]

Please cite our paper if you find this code useful:

@inproceedings{guan2022neurofluid,
  title={NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields},
  author={Guan, Shanyan and Deng, Huayu and Wang, Yunbo and Yang, Xiaokang},
  booktitle={ICML},
  year={2022}
}

Dependencies

NeuroFluid is implemented and tested on Ubuntu 18.04 with python == 3.7. To run NeuroFluid, please install dependencies as follows:

Create an environment

conda create -n fluid-env python=3.7
conda activate fluid-env

Install Open3D.

git clone https://github.com/isl-org/Open3D-ML.git
cd Open3D-ML
pip install open3d
pip install -r requirements.txt
pip install -r requirements-torch-cuda.txt

Install Pytorch3D

conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install -c bottler nvidiacub
git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d && pip install -e .

install other dependencies
```
pip install -r requirements.txt
```

Fetch data

Download dataset from this link. I try to train models better. Pretrained models will be released soon.

Generate data

See this guide to generate fluid data.

Running the pretrained model

Evaluate NeuroFluid:

python eval_e2e.py --resume_from $MODEL_PATH --dataset DATASET_NAME

DATASET_NAME is one element of [bunny, watercube, watersphere, honeycone]. To compute PSNR/SSIM/LPIPS results, run utils/evaluate_images.ipynb

Evaluate the transition model

python eval_transmodel.py --resume_from $MODEL_PATH

Evaluate the renderer

python eval_renderer.py --resume_from $MODEL_PATH --dataset DATASET_NAME

DATASET_NAME is one element of [bunny, watercube, watersphere, honeycone].

Run the training script

To be down.

Acknowledgement

The implementation of transition model is borrowed from DeepLagrangianFluids. Please consider cite their paper if you use their code snippet:

@inproceedings{Ummenhofer2020Lagrangian,
        title     = {Lagrangian Fluid Simulation with Continuous Convolutions},
        author    = {Benjamin Ummenhofer and Lukas Prantl and Nils Thuerey and Vladlen Koltun},
        booktitle = {International Conference on Learning Representations},
        year      = {2020},
}

We refer to nerf_pl to implement our renderer. Thank D-NeRF for providing the script of computing PSNR/SSIM/LPIPS.

tianyuzelin/NeuroFluid