Junoh Lee
·
ChangYeon Won
·
Hyunjun Jung
·
Inhwan Bae
·
Hae-Gon Jeon
NeurIPS 2024
Project Page
NeurIPS Paper
Arxiv Paper
Source Code
Related Works
Summary: 4D Gaussian Splatting with static & dynamic separation using an incrementally extensible, keyframe-based model
Clone the source code of this repo.
git clone https://github.com/juno181/Ex4DGS.git
cd Ex4DGS
Installation through pip is recommended. First, set up your Python environment:
conda create -n Ex4DGS python=3.9
conda activate Ex4DGS
Make sure to install CUDA and PyTorch versions that match your CUDA environment. We've tested on RTX 4090 GPU with PyTorch version 2.12. Please refer https://pytorch.org/ for further information.
pip install torch
The remaining packages can be installed with:
pip install --upgrade setuptools cython wheel
pip install -r requirements.txt
For dataset preprocessing, we follow STG.
First, download the dataset from here. You will need colmap environment for preprocess. To setup dataset preprocessing environment, run scrips:
./scripts/env_setup.sh
To preprocess dataset, run script:
./scripts/preprocess_all_n3v.sh <path to dataset>
Download the dataset from here. To setup dataset preprocessing environment, run scrips:
./scripts/preprocess_all_techni.sh <path to dataset>
Please refer STG for further information.
Run command:
python train.py --config configs/<some config name>.json --model_path <some output folder> --source_path <path to dataset>
Run command:
python render.py --model_path <path to trained model> --source_path <path to dataset> --skip_train --iteration <trained iter>
We provide pretrained models in release.
@inproceedings{lee2024ex4dgs,
title={Fully Explicit Dynamic Guassian Splatting},
author={Lee, Junoh and Won, ChangYeon and Jung, Hyunjun and Bae, Inhwan and Jeon, Hae-Gon},
booktitle={Proceedings of the Neural Information Processing Systems},
year={2024}
}