标题： 具有二维高斯散射的全局一致RGB-D SLAM 显示英文标题

标题： Globally Consistent RGB-D SLAM with 2D Gaussian Splatting

摘要： 近期，基于3D高斯点样式的RGB-D SLAM展示了高保真3D重建的卓越性能。 然而，深度渲染一致性不足和高效的回环检测限制了其几何重建的质量以及在线执行全局一致映射的能力。 在本文中，我们提出了2DGS-SLAM，一种使用2D高斯点样式作为地图表示的RGB-D SLAM系统。 通过利用2D变体的深度一致性渲染特性，我们提出了一种准确的相机位姿优化方法，并实现了几何精确的3D重建。 此外，我们通过利用MASt3R（一种3D基础模型）实现高效的回环检测和相机重定位，并通过维护局部活跃地图实现高效的地图更新。 实验表明，与现有的基于渲染的SLAM方法相比，我们的2DGS-SLAM方法在跟踪精度、表面重建质量和全局地图重建一致性方面表现出色，同时保持了高保真图像渲染和改进的计算效率。 显示英文摘要

摘要： Recently, 3D Gaussian splatting-based RGB-D SLAM displays remarkable performance of high-fidelity 3D reconstruction. However, the lack of depth rendering consistency and efficient loop closure limits the quality of its geometric reconstructions and its ability to perform globally consistent mapping online. In this paper, we present 2DGS-SLAM, an RGB-D SLAM system using 2D Gaussian splatting as the map representation. By leveraging the depth-consistent rendering property of the 2D variant, we propose an accurate camera pose optimization method and achieve geometrically accurate 3D reconstruction. In addition, we implement efficient loop detection and camera relocalization by leveraging MASt3R, a 3D foundation model, and achieve efficient map updates by maintaining a local active map. Experiments show that our 2DGS-SLAM approach achieves superior tracking accuracy, higher surface reconstruction quality, and more consistent global map reconstruction compared to existing rendering-based SLAM methods, while maintaining high-fidelity image rendering and improved computational efficiency.