MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM

• URL: http://arxiv.org/abs/2504.02437v1
• Date: Thu, 03 Apr 2025 09:51:51 GMT
• Title: MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM
• Authors: Renwu Li, Wenjing Ke, Dong Li, Lu Tian, Emad Barsoum,
• Abstract summary: We present MonoGS++, a novel fast and accurate Simultaneous localization and Mapping (SLAM) method.<n>Our approach reduces the hardware dependency and only requires RGB input, leveraging online visual odometry (VO) to generate sparse point clouds in real-time.<n>Our method realized a significant 5.57x improvement in frames per second (fps) over the previous state-of-the-art, MonoGS.
• Score: 9.37281948308712
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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