具有全局尺度一致3D高斯点图的户外单目SLAM
Outdoor Monocular SLAM with Global Scale-Consistent 3D Gaussian Pointmaps
AI总结：
针对户外3DGS SLAM的几何先验缺失与尺度漂移问题，本文提出仅用RGB的S3PO-GS，通过点图锚定自洽跟踪和分块动态建图实现SOTA新视角合成与更优跟踪精度。
摘要：
3D Gaussian Splatting（3DGS，3D高斯泼溅）因其高保真、实时的新视角合成表现，已成为SLAM中的一种流行方案。然而，此前一些3DGS SLAM方法在跟踪中采用可微渲染流水线，在户外场景中缺乏几何先验。另一些方法引入独立的跟踪模块，但在相机发生显著运动时会累积误差，从而导致尺度漂移。为应对这些挑战，我们提出一种稳健的仅使用RGB的户外3DGS SLAM方法：S3PO-GS。在技术上，我们建立了以3DGS点图为锚点的自洽跟踪模块，可避免累积尺度漂移，并以更少迭代实现更精确、更稳健的跟踪。此外，我们设计了基于块的点图动态建图模块，在引入几何先验的同时避免尺度歧义。这显著提升了跟踪精度和场景重建质量，使其尤其适用于复杂户外环境。我们在Waymo、KITTI和DL3DV数据集上的实验表明，S3PO-GS在新视角合成方面取得了最先进结果，并在跟踪精度上优于其他3DGS SLAM方法。项目页面：https://3dagentworld.github.io/S3PO-GS/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
