Dense Monocular SLAM in Real-Time with Structured Gaussian Representation

Abstract

Monocular dense SLAM faces significant challenges in low-texture environments and under rapid camera motions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems employ end-to-end optimization frameworks, which often struggle to achieve both efficient camera tracking and high-quality scene reconstruction simultaneously. To address this challenge, we propose a dense decoupled SLAM system that seamlessly integrates traditional visual odometry with 3DGS within a unified framework. Our system utilizes dense direct image alignment using pseudo-depth maps rendered from a global model, which is represented by an octree-managed structured Gaussian representation. This structured Gaussian supports fast rendering and efficient mesh extraction. Furthermore, we adopt a stereo 3D reconstruction model to generate dense depth maps from visual odometry for optimizing the 3D Gaussians. Experimental results demonstrate that our framework achieves state-of-the-art performance in both tracking robustness and reconstruction outperforming to existing monocular Gaussian-based SLAM systems, while maintaining real-time efficiency.