解决室外单目SLAM中尺度漂移问题，实现高精度三维重建与渲染。
Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps
- 基于3D高斯点云构建自洽跟踪模块，避免累积误差。
- 引入基于区块的动态映射机制，提升复杂户外场景精度。
- 仅用图像实现端到端建图，适合真实世界自动驾驶场景。
3D高斯溅射（3DGS）因高保真和实时新视角合成性能，成为SLAM领域的热门方案。然而，现有3DGS SLAM方法在跟踪中依赖可微渲染管道，缺乏户外场景的几何先验；部分方法虽引入独立跟踪模块，但在大范围运动下仍存在显著尺度漂移。为此，本文提出一种鲁棒的纯RGB室外3DGS SLAM方法S3PO-GS：技术上，建立锚定于3DGS点云的自洽跟踪模块，避免累积尺度漂移，实现更精确且稳定的跟踪，迭代次数更少；同时设计基于区块的点云动态映射模块，在不引入尺度歧义的前提下融入几何先验。该方法显著提升跟踪精度与场景重建质量，特别适用于复杂户外环境。在Waymo、KITTI和DL3DV数据集上的实验表明，S3PO-GS在新视角合成方面达到当前最优性能，并在跟踪精度上超越其他3DGS SLAM方法。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has become a popular solution in SLAM due to its high-fidelity and real-time novel view synthesis performance. However, some previous 3DGS SLAM methods employ a differentiable rendering pipeline for tracking, lack geometric priors in outdoor scenes. Other approaches introduce separate tracking modules, but they accumulate errors with significant camera movement, leading to scale drift. To address these challenges, we propose a robust RGB-only outdoor 3DGS SLAM method: S3PO-GS. Technically, we establish a self-consistent tracking module anchored in the 3DGS pointmap, which avoids cumulative scale drift and achieves more precise and robust tracking with fewer iterations. Additionally, we design a patch-based pointmap dynamic mapping module, which introduces geometric priors while avoiding scale ambiguity. This significantly enhances tracking accuracy and the quality of scene reconstruction, making it particularly suitable for complex outdoor environments. Our experiments on the Waymo, KITTI, and DL3DV datasets demonstrate that S3PO-GS achieves state-of-the-art results in novel view synthesis and outperforms other 3DGS SLAM methods in tracking accuracy. Project page: https://3dagentworld.github.io/S3PO-GS/.
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