AMG-SLAM: Adaptive Monocular Gaussian SLAM for Efficient Surface Reconstruction

Youqi Pan, Wugen Zhou, Hongbin Zha

Abstract:

Dense SLAM with a monocular camera remains a highly challenging task.
In this paper, we present AMG-SLAM, a novel dense monocular SLAM system that tightly couples sparse tracking with dense Gaussian mapping to achieve fully online and high-quality surface reconstruction.
In the frontend, learning-based modules enable efficient pose tracking and Gaussian proposal with sparse depth initialization. Specifically, we propose a fidelity-aware Gaussian proposal strategy that adaptively adds new Gaussians based on reconstruction completeness, effectively avoiding redundancy.
In the backend, we propose a focus-and-balance online refinement strategy, which adaptively selects under-optimized Gaussians for focused refinement while ensuring globally balanced optimization by maximizing scene view coverage.
We evaluated our method on synthetic and real-world datasets, including Replica, ScanNet, and EuRoC. Thanks to efficient system coupling and adaptive Gaussian proposal and refinement, our system achieves trajectory accuracy, rendering precision, and geometric accuracy comparable to or exceeding current state-of-the-art methods, while also demonstrating high efficiency.