Recent advances in dense 3D reconstruction have demonstrated strong capability in accurately capturing local geometry. However, extending these methods to incremental global reconstruction, as required in SLAM systems, remains challenging. Without explicit modeling of global geometric consistency, existing approaches often suffer from accumulated drift, scale inconsistency, and suboptimal local geometry. To address these issues, we propose SING3R-SLAM, a globally consistent Gaussian-based monocular indoor SLAM framework. Our approach represents the scene with a Global Gaussian Map that serves as a persistent, differentiable memory, incorporates local geometric reconstruction via submap-level global alignment, and leverages global map's consistency to further refine local geometry. This design enables efficient and versatile 3D mapping for multiple downstream applications. Extensive experiments show that SING3R-SLAM achieves state-of-the-art performance in pose estimation, 3D reconstruction, and novel view rendering. It improves pose accuracy by over 10%, produces finer and more detailed geometry, and maintains a compact and memory-efficient global representation on real-world datasets.

翻译：近年来，稠密三维重建在精确捕捉局部几何方面展现出强大能力。然而，将这些方法扩展到SLAM系统所要求的增量式全局重建仍具挑战。由于缺乏对全局几何一致性的显式建模，现有方法常受累积漂移、尺度不一致及次优局部几何的影响。为解决这些问题，我们提出SING3R-SLAM——一个基于高斯分布的全局一致单目室内SLAM框架。本方法通过全局高斯地图（Global Gaussian Map）表示场景，该地图作为持久可微内存，通过子图级全局对齐整合局部几何重建，并利用全局地图的一致性进一步优化局部几何。该设计能够为多种下游应用实现高效且通用的三维建图。大量实验表明，SING3R-SLAM在姿态估计、三维重建和新视角渲染方面均达到最优性能。其在真实世界数据集上姿态精度提升超10%，生成更精细、更详细的几何结构，并保持紧凑且内存高效的全局表示。