4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

ArXiv Preprint

Project Page| arXiv Paper

Guanjun Wu^1*, Taoran Yi^2*, Jiemin Fang^3‡, Lingxi Xie³,
Xiaopeng Zhang³, Wei Wei¹,Wenyu Liu², Qi Tian³ , Xinggang Wang^2‡✉

¹School of CS, HUST   ²School of EIC, HUST   ³Huawei Inc.  

^* Equal Contributions. ^$\ddagger$ Project Lead. ^✉ Corresponding Author.

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Our method converges very quickly and achieves real-time rendering speed.

Colab demo: (Thanks camenduru.)

Light Gaussian implementation: This link (Thanks pablodawson)

Environmental Setups

Please follow the 3D-GS to install the relative packages.

git clone https://github.com/hustvl/4DGaussians
cd 4DGaussians
git submodule update --init --recursive
conda create -n Gaussians4D python=3.7 
conda activate Gaussians4D

pip install -r requirements.txt
pip install -e submodules/depth-diff-gaussian-rasterization
pip install -e submodules/simple-knn

In our environment, we use pytorch=1.13.1+cu116.

Data Preparation

For synthetic scenes:
The dataset provided in D-NeRF is used. You can download the dataset from dropbox.

For real dynamic scenes:
The dataset provided in HyperNeRF is used. You can download scenes from Hypernerf Dataset and organize them as Nerfies. Meanwhile, Plenoptic Dataset could be downloaded from their official websites. To save the memory, you should extract the frames of each video and then organize your dataset as follows.

├── data
│   | dnerf 
│     ├── mutant
│     ├── standup 
│     ├── ...
│   | hypernerf
│     ├── interp
│     ├── misc
│     ├── virg
│   | dynerf
│     ├── cook_spinach
│       ├── cam00
│           ├── images
│               ├── 0000.png
│               ├── 0001.png
│               ├── 0002.png
│               ├── ...
│       ├── cam01
│           ├── images
│               ├── 0000.png
│               ├── 0001.png
│               ├── ...
│     ├── cut_roasted_beef
|     ├── ...

Training

For training synthetic scenes such as bouncingballs, run

python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 

You can customize your training config through the config files.

Rendering

Run the following script to render the images.

python render.py --model_path "output/dnerf/bouncingballs/"  --skip_train --configs arguments/dnerf/bouncingballs.py  &

Evaluation

You can just run the following script to evaluate the model.

python metrics.py --model_path "output/dnerf/bouncingballs/" 

Custom Datasets

Install nerfstudio and follow their colmap pipeline.

pip install nerfstudio
ns-process-data images --data data/your-data --output-dir data/your-ns-data
python train.py -s data/your-ns-data --port 6017 --expname "custom" --configs arguments/hypernerf/default.py 

Scripts

There are some helpful scripts in , please feel free to use them.

vis_point.py: get all points clouds at each timestamps.

usage:

export exp_name="hypernerf"
python vis_point.py --model_path output/$exp_name/interp/aleks-teapot --configs arguments/$exp_name/default.py 

weight_visualization.ipynb:

visualize the weight of Multi-resolution HexPlane module.

merge_many_4dgs.py: merge your trained 4dgs. usage:

export exp_name="dynerf"
python merge_many_4dgs.py --model_path output/$exp_name/sear_steak

colmap.sh: generate point clouds from input data

bash colmap.sh data/hypernerf/virg/vrig-chicken hypernerf 
bash colmap.sh data/dynerf/sear_steak llff

Blender format seems doesn't work. Welcome to raise a pull request to fix it.

downsample_point.py :downsample generated point clouds by sfm.

python scripts/downsample_point.py data/dynerf/sear_steak/colmap/dense/workspace/fused.ply data/dynerf/sear_steak/points3D_downsample2.ply

In my paper, I always use colmap.sh to generate dense point clouds and downsample it to less than 40000 points.

Here are some codes maybe useful but never adopted in my paper, you can also try it.

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Contributions

This project is still under development. Please feel free to raise issues or submit pull requests to contribute to our codebase.

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Some source code of ours is borrowed from 3DGS, k-planes,HexPlane, TiNeuVox. We sincerely appreciate the excellent works of these authors.

Acknowledgement

We would like to express our sincere gratitude to @zhouzhenghong-gt for his revisions to our code and discussions on the content of our paper.

Citation

Some insights about neural voxel grids and dynamic scenes reconstruction originate from TiNeuVox. If you find this repository/work helpful in your research, welcome to cite these papers and give a ⭐.

@article{wu20234dgaussians,
  title={4D Gaussian Splatting for Real-Time Dynamic Scene Rendering},
  author={Wu, Guanjun and Yi, Taoran and Fang, Jiemin and Xie, Lingxi and Zhang, Xiaopeng and Wei Wei and Liu, Wenyu and Tian, Qi and Wang Xinggang},
  journal={arXiv preprint arXiv:2310.08528},
  year={2023}
}

@inproceedings{TiNeuVox,
  author = {Fang, Jiemin and Yi, Taoran and Wang, Xinggang and Xie, Lingxi and Zhang, Xiaopeng and Liu, Wenyu and Nie\ss{}ner, Matthias and Tian, Qi},
  title = {Fast Dynamic Radiance Fields with Time-Aware Neural Voxels},
  year = {2022},
  booktitle = {SIGGRAPH Asia 2022 Conference Papers}
}