Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis

Project Page | Paper | Video | Viewer & Pre-Trained Models

This is an official implementation of the paper "Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis".
Zhan Li^1,2, Zhang Chen^1,†, Zhong Li^1,†, Yi Xu¹
¹ OPPO US Research Center, ² Portland State University
^† Corresponding authors

Installation
Preprocess Datasets
Training
Testing
Real-Time Viewer
Creating Your Gaussians
License Infomration
Acknowledgement
Citations

Installation

Clone the source code of this repo.

git clone https://github.com/oppo-us-research/SpacetimeGaussians.git
cd SpacetimeGaussians

Then run the following command to install the environments with conda. Note we will create two environments, one for preprocessing with colmap (colmapenv) and one for training and testing (feature_splatting). Training, testing and preprocessing have been tested on Ubuntu 20.04.

bash script/setup.sh

Note that you may need to manually install the following packages if you encounter errors during the installation of the above command.

conda activate feature_splatting
pip install thirdparty/gaussian_splatting/submodules/gaussian_rasterization_ch9
pip install thirdparty/gaussian_splatting/submodules/gaussian_rasterization_ch3
pip install thirdparty/gaussian_splatting/submodules/forward_lite

Processing Datasets

Neural 3D Dataset

Download the dataset from here. After downloading the dataset, you can run the following command to preprocess the dataset.

conda activate colmapenv
python script/pre_n3d.py --videopath <location>/<scene>

<location> is the path to the dataset root folder, and <scene> is the name of a scene in the dataset.

For example if you put the dataset at /home/Neural3D, and want to preprocess the cook_spinach scene, you can run the following command

conda activate colmapenv
python script/pre_n3d.py --videopath /home/Neural3D/cook_spinach/

Our codebase expects the following directory structure for the Neural 3D Dataset after preprocessing:


<location>
|---cook_spinach
|   |---colmap_<0>
|   |---colmap_<...>
|   |---colmap_<299>
|---flame_salmon1

Technicolor Dataset

Please reach out to the authors of the paper "Dataset and Pipeline for Multi-View Light-Field Video" for access to the Technicolor dataset.
Our codebase expects the following directory structure for this dataset before preprocessing:


<location>
|---Fabien
|   |---Fabien_undist_<00257>_<08>.png
|   |---Fabien_undist_<.....>_<..>.png
|---Birthday

Then run the following command to preprocess the dataset.

conda activate colmapenv
python script/pre_technicolor.py --videopath <location>/<scene>

Google Immersive Dataset (coming soon)

Training

You can train our model by running the following command:

conda activate feature_splatting
python train.py --quiet --eval --config config/<dataset>_<lite|full>/<scene>.json --model_path <path to save model> --source_path <location>/<scene>/colmap_0

In the argument to --config, <dataset> can be n3d (for Neural 3D Dataset) or techni (for Technicolor Dataset), and you can choose between full model or lite model.
You need 24GB GPU memory to train on the Neural 3D Dataset.
You need 48GB GPU memory to train on the Technicolor Dataset.
The large memory requirement is because training images are loaded into GPU memory.

For example, if you want to train the lite model on the first 50 frames of the cook_spinach scene in the Neural 3D Dataset, you can run the following command

python train.py --quiet --eval --config configs/n3d_lite/cook_spinach.json --model_path log/cook_spinach_lite --source_path <location>/cook_spinach/colmap_0

If you want to train the full model, you can run the following command

python train.py --quiet --eval --config configs/n3d_full/cook_spinach.json --model_path log/cook_spinach/colmap_0 --source_path <location>/cook_spinach/colmap_0

Please refer to the .json config files for more options.

Testing

Test model on Neural 3D Dataset

python test.py --quiet --eval --skip_train --valloader colmapvalid --configpath config/n3d_<lite|full>/<scene>.json --model_path <path to model> --source_path <location>/<scene>/colmap_0

Test model on Technicolor Dataset

python test.py --quiet --eval --skip_train --valloader technicolorvalid --configpath config/techni_<lite|full>/<scene>.json --model_path <path to model> --source_path <location>/<scenename>/colmap_0

Test model on Google Immersive Dataset (coming soon)

Real-Time Viewer

The viewer is based on SIBR and Gaussian Splatting.

Pre-built Windows Binary

Download the viewer binary from this link and unzip it. The binary works for Windows with CUDA >= 11.0. We also provide pre-trained models in the link. For example, n3d_sear_steak_lite_allcam.zip contains the lite model that uses all views during training for the sear_steak scene in the Neural 3D Dataset.

Installation from Source (coming soon)

Running the Real-Time Viewer

After downloading the pre-built binary or installing from source, you can use the following command to run the real-time viewer. Adjust --iteration to match the training iterations of model.

./<SIBR install dir>/bin/SIBR_gaussianViewer_app_rwdi.exe --iteration 25000 -m <path to trained model>

The above command has beed tested on Nvidia RTX 3050 Laptop GPU + Windows 10.

For 8K rendering, you can use the following command.

./<SIBR install dir>/bin/SIBR_gaussianViewer_app_rwdi.exe --iteration 25000 --rendering-size 8000 4000 --force-aspect-ratio 1 -m <path to trained model>

8K rendering has been tested on Nvidia RTX 4090 + Windows 11.

Create Your New Representations and Rendering Pipeline

If you want to customize our codebase for your own models, you can refer to the following steps

Step 1: Create a new Gaussian representation in this folder. You can use oursfull.py or ourslite.py as a template.
Step 2: Create a new rendering pipeline in this file. You can use the train_ours_full function as a template.
Step 3 (For new dataset, optional): Create a new dataloader in this file and this file.
Step 4: Update the intermidiate API in getmodel (for Step 1) and getrenderpip (for Step 2) functions in helper_train.py.

License Information

The code in this repository (except the thirdparty folder) is licensed under MIT licence, see LICENSE. thirdparty/gaussian_splatting is licensed under Gaussian-Splatting license, see thirdparty/gaussian_splatting/LICENSE.md. thirdparty/colmap is licensed under new BSD license, see thirdparty/colmap/LICENSE.txt.

Acknowledgement

We sincerely thank the owners of the following source code repos, which are used by our released codes: Gaussian Splatting, Colmap. Some parts of our code referenced the following repos: Gaussian Splatting with Depth, SIBR, fisheye-distortion.

Citations

Please cite our paper if you find it useful for your research.

@article{li2023spacetimegaussians,
  title={Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis},
  author={Li, Zhan and Chen, Zhang and Li, Zhong and Xu, Yi},
  journal={arXiv preprint arXiv:2312.16812},
  year={2023}
}

Please also cite the following paper if you use Gaussian Splatting.

@Article{kerbl3Dgaussians,
      author       = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
      title        = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
      journal      = {ACM Transactions on Graphics},
      number       = {4},
      volume       = {42},
      month        = {July},
      year         = {2023},
      url          = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
}

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