161 lines
6.6 KiB
Markdown
161 lines
6.6 KiB
Markdown
# 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
|
|
|
|
## arXiv Preprint
|
|
|
|
### [Project Page](https://guanjunwu.github.io/4dgs/index.html)| [arXiv Paper](https://arxiv.org/abs/2310.08528)
|
|
|
|
|
|
[Guanjun Wu](https://guanjunwu.github.io/)<sup>1*</sup>, [Taoran Yi](https://github.com/taoranyi)<sup>2*</sup>,
|
|
[Jiemin Fang](https://jaminfong.cn/)<sup>3‡</sup>, [Lingxi Xie](http://lingxixie.com/)<sup>3</sup>, </br>[Xiaopeng Zhang](https://scholar.google.com/citations?user=Ud6aBAcAAAAJ&hl=zh-CN)<sup>3</sup>, [Wei Wei](https://www.eric-weiwei.com/)<sup>1</sup>,[Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/)<sup>2</sup>, [Qi Tian](https://www.qitian1987.com/)<sup>3</sup> , [Xinggang Wang](https://xwcv.github.io)<sup>2‡✉</sup>
|
|
|
|
<sup>1</sup>School of CS, HUST   <sup>2</sup>School of EIC, HUST   <sup>3</sup>Huawei Inc.  
|
|
|
|
<sup>\*</sup> Equal Contributions. <sup>$\ddagger$</sup> Project Lead. <sup>✉</sup> Corresponding Author.
|
|
|
|
---------------------------------------------------
|
|
|
|
|
|
Our method converges very quickly and achieves real-time rendering speed.
|
|
|
|
Colab demo:[](https://colab.research.google.com/github/hustvl/4DGaussians/blob/master/4DGaussians.ipynb) (Thanks [camenduru](https://github.com/camenduru/4DGaussians-colab).)
|
|
|
|
|
|
|
|
## Environmental Setups
|
|
Please follow the [3D-GS](https://github.com/graphdeco-inria/gaussian-splatting) to install the relative packages.
|
|
```bash
|
|
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](https://github.com/albertpumarola/D-NeRF) is used. You can download the dataset from [dropbox](https://www.dropbox.com/s/0bf6fl0ye2vz3vr/data.zip?dl=0).
|
|
|
|
**For real dynamic scenes:**
|
|
The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used. You can download scenes from [Hypernerf Dataset](https://github.com/google/hypernerf/releases/tag/v0.1) and organize them as [Nerfies](https://github.com/google/nerfies#datasets). Meanwhile, [Plenoptic Dataset](https://github.com/facebookresearch/Neural_3D_Video) 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/"
|
|
```
|
|
## Scripts
|
|
|
|
There are some helpful scripts in , please feel free to use them.
|
|
|
|
`vis_point.py`:
|
|
get all points clouds at each timestamps.
|
|
|
|
usage:
|
|
```python
|
|
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:
|
|
```python
|
|
export exp_name="dynerf"
|
|
python merge_many_4dgs.py --model_path output/$exp_name/flame_salmon_1
|
|
```
|
|
|
|
`colmap.sh`:
|
|
generate point clouds from input data
|
|
```bash
|
|
bash colmap.sh data/hypernerf/virg/vrig-chicken hypernerf
|
|
bash colmap.sh data/dynerf/flame_salmon_1 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
|
|
python scripts/downsample_point.py data/dynerf/sear_steak/points3D_downsample.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.
|
|
|
|
---
|
|
## Contributions
|
|
|
|
**This project is still under development. Please feel free to raise issues or submit pull requests to contribute to our codebase.**
|
|
|
|
---
|
|
Some source code of ours is borrowed from [3DGS](https://github.com/graphdeco-inria/gaussian-splatting), [k-planes](https://github.com/Giodiro/kplanes_nerfstudio),[HexPlane](https://github.com/Caoang327/HexPlane), [TiNeuVox](https://github.com/hustvl/TiNeuVox). We sincerely appreciate the excellent works of these authors.
|
|
|
|
## Acknowledgement
|
|
|
|
We would like to express our sincere gratitude to [@zhouzhenghong-gt](https://github.com/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](https://github.com/hustvl/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}
|
|
}
|
|
``` |