VDG: Vision-Only Dynamic Gaussian for Driving Simulation
<|reference_start|>VDG: Vision-Only Dynamic Gaussian for Driving Simulation: 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.<|reference_end|>
@article{li2024vdg:,
    title={VDG: Vision-Only Dynamic Gaussian for Driving Simulation},
    author={Hao Li, Jingfeng Li, Dingwen Zhang, Chenming Wu, Jieqi Shi, Chen Zhao,
  Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han},
    journal={arXiv preprint arXiv:2406.18198},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18198},
    primaryClass={cs.CV}
}