用2D高斯点云实现高保真、全局一致的实时建图
Globally Consistent RGB-D SLAM with 2D Gaussian Splatting
- 以2D高斯点云为地图表示，提升深度渲染一致性
- 相比现有方法，定位更准、表面重建更精细、全局地图更连贯
- 适合需要高精度3D建图的机器人与AR应用
基于3D高斯点云的RGB-D SLAM在高保真三维重建方面表现优异，但缺乏深度渲染一致性及高效的闭环检测，限制了其几何重建质量与在线全局一致映射能力。本文提出2DGS-SLAM，采用2D高斯点云作为地图表示。利用2D变体的深度一致性渲染特性，设计了精确的相机位姿优化方法，实现了几何准确的三维重建。同时，通过MASt3R这一3D基础模型实现高效闭环检测与相机重定位，并维护局部活跃地图以实现高效地图更新。实验表明，2DGS-SLAM在跟踪精度、表面重建质量及全局地图一致性方面优于现有基于渲染的SLAM方法，同时保持高保真图像渲染和更优计算效率。
原文摘要 · Abstract (English)
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.
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