VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Hao Li, Jingfeng Li, Dingwen Zhang, Chenming Wu, Jieqi Shi, Chen Zhao, Haocheng Feng, Errui Ding
Abstract
Recent advances in dynamic Gaussian splatting have significantly improved scene reconstruction and novel-view synthesis. However, existing methods often rely on pre-computed camera poses and Gaussian initialization using Structure from Motion (SfM) or other costly sensors, limiting their scalability. In this letter, we propose Vision-only Dynamic Gaussian (VDG), a novel method that, for the first time, integrates self-supervised visual odometry (VO) into a pose-free dynamic Gaussian splatting framework. Given the reason that estimated poses are not accurate enough to perform self-decomposition for dynamic scenes, we specifically design motion supervision, enabling precise static-dynamic decomposition and modeling of dynamic objects via dynamic Gaussians. Extensive experiments on urban driving datasets, including KITTI and Waymo, show that VDG consistently outperforms state-of-the-art dynamic view synthesis methods in both reconstruction accuracy and pose prediction with only image input.
BibTeX
@inproceedings{ral2025_vdgvisiononlydyn,
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},
booktitle = {RA-L 2025},
year = {2025}
}