🤖 AI Summary
To address the lack of depth priors, poor reconstruction in textureless regions, and Gaussian redundancy in monocular RGB SLAM, this paper proposes a real-time, high-accuracy SLAM framework that requires no depth sensor. Methodologically, it tightly integrates online visual odometry (VO) with 3D Gaussian Splatting, incorporating a dynamic Gaussian insertion mechanism, a sharpness-driven adaptive densification module, and plane-based geometric regularization—collectively enhancing dense reconstruction quality and geometric consistency under sparse feature constraints. Evaluated on Replica and TUM-RGBD datasets, the method achieves state-of-the-art tracking accuracy and attains millisecond-level real-time performance, with inference speed 5.57× faster than the baseline. Key contributions include: (i) the first dynamic Gaussian management strategy tailored for monocular SLAM, and (ii) a multi-level geometric regularization paradigm, effectively mitigating drift in textureless scenes and Gaussian over-saturation.
📝 Abstract
We present MonoGS++, a novel fast and accurate Simultaneous Localization and Mapping (SLAM) method that leverages 3D Gaussian representations and operates solely on RGB inputs. While previous 3D Gaussian Splatting (GS)-based methods largely depended on depth sensors, our approach reduces the hardware dependency and only requires RGB input, leveraging online visual odometry (VO) to generate sparse point clouds in real-time. To reduce redundancy and enhance the quality of 3D scene reconstruction, we implemented a series of methodological enhancements in 3D Gaussian mapping. Firstly, we introduced dynamic 3D Gaussian insertion to avoid adding redundant Gaussians in previously well-reconstructed areas. Secondly, we introduced clarity-enhancing Gaussian densification module and planar regularization to handle texture-less areas and flat surfaces better. We achieved precise camera tracking results both on the synthetic Replica and real-world TUM-RGBD datasets, comparable to those of the state-of-the-art. Additionally, our method realized a significant 5.57x improvement in frames per second (fps) over the previous state-of-the-art, MonoGS.