用全局高斯地图提升单目室内建图精度与一致性
SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors
- 以子地图为单位进行全局对齐，构建可微的持久化高斯地图
- 相比现有方法，位姿精度提升超10%，几何细节更丰富
- 适合需要高精度三维重建的机器人导航与数字孪生场景
近期密集三维重建技术在捕捉局部几何方面表现出强大能力，但将其扩展到增量式全局重建（如SLAM系统）仍具挑战。由于缺乏全局几何一致性建模，现有方法常出现累积漂移、尺度不一致和局部几何次优等问题。为此，我们提出SING3R-SLAM，一种基于高斯的单目室内SLAM框架。该方法通过全局高斯地图作为持续可微的记忆体，结合子地图级别的全局对齐实现局部几何重建，并利用全局地图的一致性进一步优化局部结构。该设计支持高效且多功能的三维建图，适用于多种下游任务。大量实验表明，SING3R-SLAM在位姿估计、三维重建和新视角渲染方面均达到当前最优性能：位姿精度提升超过10%，生成更精细丰富的几何结构，并在真实数据集上保持紧凑且内存高效的全局表示。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。