Dynamic Gaussian splatting has led to impressive scene reconstruction and image synthesis advances in novel views. Existing methods, however, heavily rely on pre-computed poses and Gaussian initialization by Structure from Motion (SfM) algorithms or expensive sensors. For the first time, this paper addresses this issue by integrating self-supervised VO into our pose-free dynamic Gaussian method (VDG) to boost pose and depth initialization and static-dynamic decomposition. Moreover, VDG can work with only RGB image input and construct dynamic scenes at a faster speed and larger scenes compared with the pose-free dynamic view-synthesis method. We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods. Additional video and source code will be posted on our project page at https://3d-aigc.github.io/VDG.
@article{arxiv.2406.18198,
title = {VDG: Vision-Only Dynamic Gaussian for Driving Simulation},
author = {Hao Li and Jingfeng Li and Dingwen Zhang and Chenming Wu and Jieqi Shi and Chen Zhao and Haocheng Feng and Errui Ding and Jingdong Wang and Junwei Han},
journal= {arXiv preprint arXiv:2406.18198},
year = {2024}
}