We propose a method for dynamic scene reconstruction using deformable 3D Gaussians that is tailored for monocular video. Building upon the efficiency of Gaussian splatting, our approach extends the representation to accommodate dynamic elements via a deformable set of Gaussians residing in a canonical space, and a time-dependent deformation field defined by a multi-layer perceptron (MLP). Moreover, under the assumption that most natural scenes have large regions that remain static, we allow the MLP to focus its representational power by additionally including a static Gaussian point cloud. The concatenated dynamic and static point clouds form the input for the Gaussian Splatting rasterizer, enabling real-time rendering. The differentiable pipeline is optimized end-to-end with a self-supervised rendering loss. Our method achieves results that are comparable to state-of-the-art dynamic neural radiance field methods while allowing much faster optimization and rendering.
我们提出了一种适用于单目视频的动态场景重建方法，该方法使用可变形的3D高斯函数。基于高斯飞溅效率，我们的方法将表征扩展到通过位于规范空间中的一组可变形高斯函数和由多层感知器（MLP）定义的时变形变场来适应动态元素。此外，基于大多数自然场景有大面积静态区域的假设，我们允许MLP通过额外包括一个静态高斯点云来聚焦其表征能力。连接的动态和静态点云形成了高斯飞溅光栅化器的输入，实现实时渲染。这种可微分的管道通过自我监督的渲染损失进行端到端优化。我们的方法在与最先进的动态神经辐射场方法相当的情况下，实现了更快的优化和渲染。