A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting
Jiaxu Wang, Junhao He, Ziyi Zhang, Mingyuan Sun, Jingkai Sun, Renjing Xu*
Fig 1. The main pipeline overview of the proposed EvGGS framework.
conda env create --file environment.yml
conda activate evggs
Then, compile the diff-gaussian-rasterization in 3DGS repository:
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive
cd gaussian-splatting/
pip install -e submodules/diff-gaussian-rasterization
cd ..
Download the pretrained models from OneDrive that are placed at \pretrain_ckpt
. This directory includes two warmup ckpts and a pretrained ckpts on the synthetic dataset.
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Ev3D-S
A large-scale synthetic Event-based dataset with varying textures and materials accompanied by well-calibrated frames, depth, and groundtruths.
You can download the dataset from OneDrive and unzip it. A 50 GB of storage space is necessary.
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EV3D-R
A large-scale realistic Event-based 3D dataset containing various objects captured by a real event camera DVXplore.
Due to some licensing reasons, we currently need your private application to use this dataset.
python train_gs.py
python eval_gs.py
In configs\Ev3D_pretrain
, several primary settings are defined such as experimental name, customized dataset path, please check.
please cite our work if you use this dataset.
@misc{wang2024evggscollaborativelearningframework,
title={EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting},
author={Jiaxu Wang and Junhao He and Ziyi Zhang and Mingyuan Sun and Jingkai Sun and Renjing Xu},
year={2024},
eprint={2405.14959},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2405.14959},
}
EventNeRF: https://github.com/r00tman/EventNeRF?tab=readme-ov-file. 3D Gaussian Splatting: https://github.com/graphdeco-inria/gaussian-splatting. GPS-GS: https://github.com/aipixel/GPS-Gaussian PAEvD3d: https://github.com/Mercerai/PAEv3d