We present the first application of 3D Gaussian Splatting to incremental 3D
reconstruction using a single moving monocular or RGB-D camera. Our
Simultaneous Localisation and Mapping (SLAM) method, which runs live at 3fps,
utilises Gaussians as the only 3D representation, unifying the required
representation for accurate, efficient tracking, mapping, and high-quality
rendering. Several innovations are required to continuously reconstruct 3D
scenes with high fidelity from a live camera. First, to move beyond the
original 3DGS algorithm, which requires accurate poses from an offline
Structure from Motion (SfM) system, we formulate camera tracking for 3DGS using
direct optimisation against the 3D Gaussians, and show that this enables fast
and robust tracking with a wide basin of convergence. Second, by utilising the
explicit nature of the Gaussians, we introduce geometric verification and
regularisation to handle the ambiguities occurring in incremental 3D dense
reconstruction. Finally, we introduce a full SLAM system which not only
achieves state-of-the-art results in novel view synthesis and trajectory
estimation, but also reconstruction of tiny and even transparent objects.

使用单个移动单目或 RGB-D 相机，我们首次应用 3D 高斯扩散技术于增量 3D 重建。我们的同时定位与建图（SLAM）方法以高质量渲染作为所需的准确、高效跟踪和建图的唯一 3D 表示，通过几个创新实现连续重建高保真度的 3D 场景，包括通过直接优化对 3D 高斯器应用相机跟踪，利用高斯器的明确性质引入几何验证和规则化以处理增量 3D 密集重建中出现的模糊性，以及引入一个全面的 SLAM 系统，实现在新视角合成和轨迹估计中的最新结果，甚至对微小和甚至透明的物体进行重建。