🤖 AI Summary
This work addresses the challenge of efficiently and robustly reconstructing high-fidelity geometric structures in dense monocular SLAM without relying on appearance modeling. To this end, the authors propose GeoGS, a purely geometry-driven 3D Gaussian Splatting representation that discards appearance parameters and retains only spatial geometric information. The method introduces a local plane-guided Gaussian initialization strategy to accelerate convergence and incorporates a global map alignment mechanism during loop closure to effectively prevent map tearing. Experimental results demonstrate that GeoGS consistently outperforms existing approaches on both synthetic and real-world datasets, achieving state-of-the-art performance in terms of online mapping efficiency and geometric reconstruction accuracy.
📝 Abstract
Dense visual SLAM is a fundamental problem in robotics. Recent advances in 3DGS have demonstrated its potential for dense SLAM. Existing 3DGS frameworks focus on both appearance and geometry modeling. However, scene geometry is typically more critical for SLAM than novel view synthesis because downstream robotic tasks, such as navigation and obstacle avoidance, rely primarily on accurate spatial geometry rather than photorealistic rendering. This observation raises a natural question: Is it feasible for 3DGS to perform 3D reconstruction without scene appearance modeling? Motivated by this, we propose Geometry-only Gaussian Splatting (GeoGS), which directly reconstructs scene geometry, and further present GeoGS-SLAM, a dense visual SLAM system built upon this representation. Specifically, GeoGS retains only spatial parameters to reduce the number of per-primitive parameters by over 80%. In contrast to existing 3DGS methods, GeoGS focuses solely on geometric reconstruction, which significantly reduces the number of Gaussian primitives, accelerates geometric convergence, and enhances robustness to illumination variations. In addition, we present an effective training framework that optimizes the Gaussian primitives via single-view and multi-view geometric and photometric supervision, and speeds up geometry convergence with a local-plane driven initialization that better aligns primitives with local structures. Furthermore, we introduce a map update strategy for loop closure that globally transforms the Gaussian map to align it with the corrected pose estimates, thereby preventing map tearing caused by inconsistent per-viewpoint pose corrections in existing methods. Extensive experiments on synthetic and real-world benchmarks demonstrate that our method outperforms SOTA methods in terms of online mapping efficiency and geometric reconstruction quality.