3D Gaussian Splatting has emerged as a powerful representation of geometry
and appearance for RGB-only dense Simultaneous Localization and Mapping (SLAM),
as it provides a compact dense map representation while enabling efficient and
high-quality map rendering. However, existing methods show significantly worse
reconstruction quality than competing methods using other 3D representations,
e.g. neural points clouds, since they either do not employ global map and pose
optimization or make use of monocular depth. In response, we propose the first
RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes
all benefits of globally optimized tracking by adapting dynamically to keyframe
pose and depth updates by actively deforming the 3D Gaussian map. Moreover, we
find that refining the depth updates in inaccurate areas with a monocular depth
estimator further improves the accuracy of the 3D reconstruction. Our
experiments on the Replica, TUM-RGBD, and ScanNet datasets indicate the
effectiveness of globally optimized 3D Gaussians, as the approach achieves
superior or on par performance with existing RGB-only SLAM methods methods in
tracking, mapping and rendering accuracy while yielding small map sizes and
fast runtimes. The source code is available at
this https URL

使用全局优化的 3D 高斯地图表示，结合单目深度估计、动态自适应关键帧姿态和深度更新，我们提出了首个只使用 RGB 的 SLAM 系统，实现了优化的跟踪、建图和渲染精度，并获得小型地图与快速运行时。