S3LAM: Surfel Splatting SLAM for Geometrically Accurate Tracking and Mapping

Abstract

We propose S3LAM, a novel RGB-D SLAM system that leverages 2D surfel splatting to achieve geometrically accurate scene 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 S3LAM 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 demonstrate that S3LAM achieves state-of-the-art performance. Our code is available at https://github.com/FanryZ/S3LAM.