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README.md
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README.md
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# 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering
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## ArXiv Preprint
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## CVPR 2024
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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://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>`
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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://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>
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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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<sup>1</sup>School of CS, HUST   <sup>2</sup>School of EIC, HUST   <sup>3</sup>Huawei Inc.  
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`<sup>`\*`</sup>` Equal Contributions. `<sup>`$\ddagger$`</sup>` Project Lead. `<sup>`✉`</sup>` Corresponding Author.
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<sup>\*</sup> Equal Contributions. <sup>$\ddagger$</sup> Project Lead. <sup>✉</sup> Corresponding Author.
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---
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---------------------------------------------------
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Our method converges very quickly and achieves real-time rendering speed.
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Colab demo:[](https://colab.research.google.com/github/hustvl/4DGaussians/blob/master/4DGaussians.ipynb) (Thanks [camenduru](https://github.com/camenduru/4DGaussians-colab).)
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Light Gaussian implementation: [This link](https://github.com/pablodawson/4DGaussians) (Thanks [pablodawson](https://github.com/pablodawson))
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## Further works
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We sincerely thank the authors and their fantastic works for other applications based on our code.
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[MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes](https://md-splatting.github.io/)
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[4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency](https://vita-group.github.io/4DGen/)
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[DreamGaussian4D: Generative 4D Gaussian Splatting](https://github.com/jiawei-ren/dreamgaussian4d)
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[EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction](https://github.com/yifliu3/EndoGaussian)
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[EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting](https://github.com/HKU-MedAI/EndoGS)
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[Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting](https://arxiv.org/abs/2401.16416)
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## News
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2024.02: We delete some logging settings for debugging, the corrected training time is only **8 mins** (20 mins before) in D-NeRF datasets and **30 mins** (1 hour before) in HyperNeRF datasets. The rendering quality is not affected.
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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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@ -35,13 +55,17 @@ pip install -r requirements.txt
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pip install -e submodules/depth-diff-gaussian-rasterization
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pip install -e submodules/simple-knn
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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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**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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**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 official websites. To save the memory, you should extract the frames of each video and then organize your dataset as follows.
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```
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├── data
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│ | dnerf
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@ -69,46 +93,56 @@ The dataset provided in [HyperNeRF](https://github.com/google/hypernerf) is used
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| ├── ...
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```
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## Training
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For training synthetic scenes such as `bouncingballs`, run
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```
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For training synthetic scenes such as `bouncingballs`, 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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```
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You can customize your training config through the config files.
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# Checkpoint
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Checkpoint
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Also, you can training your model with checkpoint.
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```python
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python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --checkpoint_iterations 200 # change it.
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```
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Then load checkpoint with:
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```python
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python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --start_checkpoint "output/dnerf/bouncingballs/chkpnt_coarse_200.pth"
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# finestage: --start_checkpoint "output/dnerf/bouncingballs/chkpnt_fine_200.pth"
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```
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## Rendering
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Run the following script to render the images.
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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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You can just run the following script to evaluate the model.
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You can just 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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## Custom Datasets
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Install nerfstudio and follow their colmap pipeline.
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```
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pip install nerfstudio
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ns-process-data images --data data/your-data --output-dir data/your-ns-data
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python train.py -s data/your-ns-data --port 6017 --expname "custom" --configs arguments/hypernerf/default.py
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cp -r data/your-ns-data/images data/your-ns-data/colmap/images
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python train.py -s data/your-ns-data/colmap --port 6017 --expname "custom" --configs arguments/hypernerf/default.py
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```
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@ -120,6 +154,7 @@ There are some helpful scripts in , please feel free to use them.
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get all points clouds at each timestamps.
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usage:
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```python
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export exp_name="hypernerf"
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python vis_point.py --model_path output/$exp_name/interp/aleks-teapot --configs arguments/$exp_name/default.py
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@ -132,6 +167,7 @@ visualize the weight of Multi-resolution HexPlane module.
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`merge_many_4dgs.py`:
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merge your trained 4dgs.
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usage:
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```python
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export exp_name="dynerf"
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python merge_many_4dgs.py --model_path output/$exp_name/sear_steak
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@ -139,6 +175,7 @@ python merge_many_4dgs.py --model_path output/$exp_name/sear_steak
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`colmap.sh`:
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generate point clouds from input data
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```bash
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bash colmap.sh data/hypernerf/virg/vrig-chicken hypernerf
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bash colmap.sh data/dynerf/sear_steak llff
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@ -147,26 +184,33 @@ bash colmap.sh data/dynerf/sear_steak llff
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**Blender** format seems doesn't work. Welcome to raise a pull request to fix it.
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`downsample_point.py` :downsample generated point clouds by sfm.
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```python
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python scripts/downsample_point.py data/dynerf/sear_steak/colmap/dense/workspace/fused.ply data/dynerf/sear_steak/points3D_downsample2.ply
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```
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In my paper, I always use `colmap.sh` to generate dense point clouds and downsample it to less than 40000 points.
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Here are some codes maybe useful but never adopted in my paper, you can also try it.
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---
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## Contributions
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**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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---
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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](https://github.com/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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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 ⭐.
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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 ⭐.
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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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iterations = 20000,
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pruning_interval = 8000,
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percent_dense = 0.01,
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render_process=True,
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render_process=False,
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# no_do=False,
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# no_dshs=False
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@ -11,7 +11,7 @@ ModelHiddenParams = dict(
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plane_tv_weight = 0.0002,
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time_smoothness_weight = 0.001,
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l1_time_planes = 0.0001,
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render_process=True
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render_process=False
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)
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OptimizationParams = dict(
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# dataloader=True,
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@ -1 +1 @@
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Subproject commit e49506654e8e11ed8a62d22bcb693e943fdecacf
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Subproject commit f2d8fa9921ea9a6cb9ac1c33a34ebd1b11510657
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4
train.py
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train.py
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parser.add_argument('--port', type=int, default=6009)
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parser.add_argument('--debug_from', type=int, default=-1)
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parser.add_argument('--detect_anomaly', action='store_true', default=False)
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parser.add_argument("--test_iterations", nargs="+", type=int, default=[500*i for i in range(100)])
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parser.add_argument("--save_iterations", nargs="+", type=int, default=[1000, 3000, 4000, 5000, 6000, 7_000, 9000, 10000, 12000, 14000, 20000, 30_000, 45000, 60000])
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parser.add_argument("--test_iterations", nargs="+", type=int, default=[3000,14000,20000])
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parser.add_argument("--save_iterations", nargs="+", type=int, default=[3000,14000,20000, 30_000, 45000, 60000])
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parser.add_argument("--quiet", action="store_true")
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parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
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parser.add_argument("--start_checkpoint", type=str, default = None)
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