$S^3$LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping
This work addresses the need for geometrically accurate tracking and mapping in SLAM systems, particularly for applications requiring real-time optimization and high-quality reconstructions, though it appears incremental as it builds on existing 3DGS-based approaches.
The authors tackled the problem of achieving highly accurate geometric representations in RGB-D SLAM by proposing $S^3$LAM, which uses 2D surfel splatting instead of 3D Gaussian ellipsoids, resulting in state-of-the-art performance validated on synthetic and real-world datasets.
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.