This repository contains the official code for the research paper titled "Event 3D Gaussian Splatting: Event-based Novel View Rendering of Large-scale Scenes using 3D Gaussians". The code uses 3D Gaussian Splatting (3DGS) as it's base. Please refer to README_3DGS.md in this repository for the readme provided with the original repository of 3DGS.

Cloning the Repository

The repository contains submodules, thus please check it out with

# SSH
git clone git@github.com:graphdeco-inria/gaussian-splatting.git --recursive

or

# HTTPS
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive

Installation

You can setup the environment for this code base by running the following bash commands:

conda env create --yes --file environment.yml
conda activate splat
conda install pytorch3d -c pytorch3d
pip install submodules/diff-gaussian-rasterization
pip install submodules/simple-knn
pip install opencv-python pandas piq scipy numba tensorboard matplotlib lpips

The compilation of submodules is dependant on the debian version. Hence to have a working environment for slurm, submit the slurm script named setup_env.sh after adjusing paths as per your conda installation. The script will install the necesssary dependancies into splat environment.

Training

Firstly download the sample dataset link and extract it into a directory. The full model can then be trained by running the following command:

python train.py -s /path/to/data/dir/shot_009 -m /path/to/model/output/dir --pose_lr 0.001 --sh_degree 1

Inference

To render the novel views from the test set, run the following command:

python render.py -s /path/to/data/dir/shot_009 -m /path/to/model/output/dir --skip_train

Remove the --skip_train flag if training views are also required to be rendered.

The rendered images can then be found in the model output directory.

Full list of experiments for the paper

The script used to run the entire list of experiments listed in the paper can be found with the name of run_experiment.sh. It can also be used for general understanding of the experimentation setting.

Citation

Please cite our work if you use the code.

@article{zahid2025e3dgs,
  title={E-3DGS: Event-based Novel View Rendering of Large-scale Scenes Using 3D Gaussian Splatting},
  author={Zahid, Sohaib and Rudnev, Viktor and Ilg, Eddy and Golyanik, Vladislav},
  journal={3DV},
  year={2025}
}