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
this https URL

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。

VDG：基于视觉的动态高斯驾驶仿真

VDG: Vision-Only Dynamic Gaussian for Driving Simulation

Performing language-conditioned robotic manipulation tasks in unstructured
environments is highly demanded for general intelligent robots. Conventional
robotic manipulation methods usually learn semantic representation of the
observation for action prediction, which ignores the scene-level spatiotemporal
dynamics for human goal completion. In this paper, we propose a dynamic
Gaussian Splatting method named ManiGaussian for multi-task robotic
manipulation, which mines scene dynamics via future scene reconstruction.
Specifically, we first formulate the dynamic Gaussian Splatting framework that
infers the semantics propagation in the Gaussian embedding space, where the
semantic representation is leveraged to predict the optimal robot action. Then,
we build a Gaussian world model to parameterize the distribution in our dynamic
Gaussian Splatting framework, which provides informative supervision in the
interactive environment via future scene reconstruction. We evaluate our
ManiGaussian on 10 RLBench tasks with 166 variations, and the results
demonstrate our framework can outperform the state-of-the-art methods by 13.1\%
in average success rate.

在这篇论文中，我们提出了一种名为 ManiGaussian 的动态高斯斑点方法，用于多任务机器人操作，该方法通过未来场景重建来挖掘场景动态性。我们首先制定了动态高斯斑点框架，推断高斯嵌入空间中的语义传播，利用语义表示来预测最佳机器人动作。然后，我们建立了一个高斯世界模型来参数化我们的动态高斯斑点框架中的分布，通过未来场景重建在交互环境中提供信息化监督。我们在 10 个 RLBench 任务的 166 个变体上评估了 ManiGaussian，并且结果表明我们的框架在平均成功率上比现有最先进方法提高了 13.1%。