MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM


Ren-Wu Li (AMD), Wenjing Ke (AMD), Dong Li (AMD), Lu Tian (AMD), Emad Barsoum (AMD)
The 35th British Machine Vision Conference
PDFPosterSupplementary

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.

Citation

@inproceedings{Li_2024_BMVC,
author    = {Ren-Wu Li and Wenjing Ke and Dong Li and Lu Tian and Emad Barsoum},
title     = {MonoGS++: Fast and Accurate Monocular RGB Gaussian SLAM},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0133.pdf}
}


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