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
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将自我监督视觉里程计（VO）集成到动态高斯方法（VDG）中，从而提升了定位和深度初始化以及静态 - 动态分解，VDG 可以只使用 RGB 图像输入，在更快的速度和更大的场景下构建动态场景，并在大量定量与定性实验中展示了对于现有动态视图合成方法的优越性能。