116 lines
4.7 KiB
Markdown
116 lines
4.7 KiB
Markdown
# 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
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## Arxiv Preprint
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### [Project Page](https://guanjunwu.github.io/4dgs/index.html)| [Arxiv Paper](https://arxiv.org/abs/2310.08528)
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[Guanjun Wu](https://guanjunwu.github.io/)<sup>1*</sup>, [Taoran Yi](https://github.com/taoranyi)<sup>2*</sup>,
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[Jiemin Fang](https://jaminfong.cn/)<sup>3</sup>, [Lingxi Xie](http://lingxixie.com/)<sup>3</sup>, </br>[Xiaopeng Zhang](https://sites.google.com/site/zxphistory/)<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://scholar.google.com/citations?hl=en&user=61b6eYkAAAAJ)<sup>3</sup> , [Xinggang Wang](https://xinggangw.info/)<sup>2✉</sup>
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<sup>1</sup>School of CS, HUST   <sup>2</sup>School of EIC, HUST   <sup>3</sup>Huawei Inc.  
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---------------------------------------------------
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Our method converges very quickly. And achieves real-time rendering speed.
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<video width="320" height="240" controls>
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<sourc src="assets/teaservideo.mp4" type="video/mp4">
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</video>
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<video width="320" height="240" controls>
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<source src="assets/cut_roasted_beef_time.mp4" type="video/mp4">
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</video>
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## Environmental Setups
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Please follow the [3D-GS](https://github.com/graphdeco-inria/gaussian-splatting) to install the relative packages.
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```bash
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git clone https://github.com/hustvl/4DGaussians
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cd 4DGaussians
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conda create -n Gaussians4D python=3.7
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conda activate Gaussians4D
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pip install -r requirements.txt
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cd submodules
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git clone https://github.com/ingra14m/depth-diff-gaussian-rasterization
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pip install -e depth-diff-gaussian-rasterization
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```
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In our environment, we use pytorch=1.13.1+cu116.
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## Data Preparation
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**For synthetic scenes:**
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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).
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**For real dynamic scenes:**
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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 offical websites, to save the memory, you should extract the frames of each video, them organize your dataset as follows.
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```
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├── data
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│ | dnerf
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│ ├── mutant
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│ ├── standup
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│ ├── ...
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│ | hypernerf
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│ ├── interp
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│ ├── misc
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│ ├── virg
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│ | dynerf
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│ ├── cook_spinach
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│ ├── cam00
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│ ├── images
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│ ├── 0000.png
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│ ├── 0001.png
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│ ├── 0002.png
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│ ├── ...
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│ ├── cam01
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│ ├── images
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│ ├── 0000.png
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│ ├── 0001.png
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│ ├── ...
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│ ├── cut_roasted_beef
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| ├── ...
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```
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## Training
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For training synthetic scenes such as `lego`, run
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```
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python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py
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```
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You can custom your training config through the config files.
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## Rendering
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Run the following script to render the images.
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```
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python render.py --model_path "output/dnerf/bouncingballs/" --skip_train --configs arguments/dnerf/bouncingballs.py &
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```
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## Evaluation
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Run the following script to evaluate the model.
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```
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python metrics.py --model_path "output/dnerf/bouncingballs/"
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```
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## Scripts
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There are some helpful scripts in `scripts/`, please feel free to use them.
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---
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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.
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## Acknowledgement
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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.
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## Citation
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If you find this repository/work helpful in your research, welcome to cite the paper and give a ⭐.
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```
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@article{wu20234dgaussians,
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title={4D Gaussian Splatting for Real-Time Dynamic Scene Rendering},
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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},
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journal={arXiv preprint arXiv:2310.08528},
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year={2023}
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}
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``` |