Recent advances in dense 3D reconstruction enable the accurate capture of local geometry; however, integrating them into SLAM is challenging due to drift and redundant point maps, which limit efficiency and downstream tasks, such as novel view synthesis. To address these issues, we propose SING3R-SLAM, a globally consistent and compact Gaussian-based dense RGB SLAM framework. The key idea is to combine locally consistent 3D reconstructions with a unified global Gaussian representation that jointly refines scene geometry and camera poses, enabling efficient and versatile 3D mapping for multiple downstream applications. SING3R-SLAM first builds locally consistent submaps through our lightweight tracking and reconstruction module, and then progressively aligns and fuses them into a global Gaussian map that enforces cross-view geometric consistency. This global map, in turn, provides feedback to correct local drift and enhance the robustness of tracking. Extensive experiments demonstrate that SING3R-SLAM achieves state-of-the-art tracking, 3D reconstruction, and novel view rendering, resulting in over 12% improvement in tracking and producing finer, more detailed geometry, all while maintaining a compact and memory-efficient global representation on real-world datasets.

翻译：近年来，稠密三维重建技术的进展使得局部几何结构的精确捕捉成为可能；然而，由于漂移和冗余点云地图的存在，将其集成到SLAM中仍面临挑战，这限制了系统效率及下游任务（如新视角合成）的性能。为解决这些问题，我们提出了SING3R-SLAM——一种全局一致且紧凑的基于高斯表示的稠密RGB SLAM框架。其核心思想是将局部一致的三维重建与统一的全局高斯表征相结合，联合优化场景几何与相机位姿，从而为多种下游应用实现高效且通用的三维建图。SING3R-SLAM首先通过轻量级跟踪与重建模块构建局部一致的子地图，随后逐步对齐并融合至全局高斯地图中，以强化跨视角几何一致性。该全局地图进而反馈修正局部漂移，提升跟踪鲁棒性。大量实验表明，SING3R-SLAM在跟踪精度、三维重建质量与新视角渲染效果上均达到最先进水平：跟踪性能提升超过12%，生成更精细、细节更丰富的几何结构，同时在真实世界数据集上保持紧凑且内存高效的全局表征。