VDG: Vision-Only Dynamic Gaussian for Driving Simulation

• URL: http://arxiv.org/abs/2406.18198v1
• Date: Wed, 26 Jun 2024 09:29:21 GMT
• Title: VDG: Vision-Only Dynamic Gaussian for Driving Simulation
• Authors: Hao Li, Jingfeng Li, Dingwen Zhang, Chenming Wu, Jieqi Shi, Chen Zhao, Haocheng Feng, Errui Ding, Jingdong Wang, Junwei Han,
• Abstract summary: We introduce self-supervised VO into our pose-free dynamic Gaussian method (VDG) 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. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods.
• Score: 112.6139608504842
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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