用二维点云片优化三维定位与建图，精度更高更高效。
$S^3$LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping
- 以二维高斯点云片为基本单元，替代传统三维高斯椭球，提升建模效率。
- 在真实与合成数据集上均达到当前最优精度，尤其在视角受限时表现优异。
- 适合需要高精度几何重建的机器人导航、AR/VR等场景使用。
我们提出 $S^3$LAM，一种新型的RGB-D SLAM系统，利用二维表面点云片（surfel splatting）实现高精度的几何表示，用于同时定位与建图。与依赖三维高斯椭球的传统3DGS-based SLAM方法不同，$S^3$LAM采用二维高斯表面点云作为基本单元，聚焦于场景中物体的表面信息，从而实现高质量的几何重建，同时提升建图与跟踪效果。针对SLAM中视角受限下的实时优化难题，我们引入一种自适应表面渲染策略，在保持计算效率的同时提高建图精度。此外，我们直接从二维表面点云片公式推导出相机位姿的雅可比矩阵，凸显几何精确表示对跟踪收敛性的关键作用。在合成与真实世界数据集上的大量实验验证了 $S^3$LAM 的先进性能。代码将公开。
原文摘要 · Abstract (English)
We propose $S^3$LAM, a novel RGB-D SLAM system that leverages 2D surfel splatting to achieve highly accurate geometric representations for simultaneous tracking and mapping. Unlike existing 3DGS-based SLAM approaches that rely on 3D Gaussian ellipsoids, we utilize 2D Gaussian surfels as primitives for more efficient scene representation. By focusing on the surfaces of objects in the scene, this design enables $S^3$LAM to reconstruct high-quality geometry, benefiting both mapping and tracking. To address inherent SLAM challenges including real-time optimization under limited viewpoints, we introduce a novel adaptive surface rendering strategy that improves mapping accuracy while maintaining computational efficiency. We further derive camera pose Jacobians directly from 2D surfel splatting formulation, highlighting the importance of our geometrically accurate representation that improves tracking convergence. Extensive experiments on both synthetic and real-world datasets validate that $S^3$LAM achieves state-of-the-art performance. Code will be made publicly available.
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