MGS-SLAM: Monocular 3D Gaussian Splatting SLAM with Significance-Guided Pruning

3D Gaussian splatting demonstrates significant advantages in high-fidelity rendering for offline reconstruction of objects or scenes. However, its integration with existing simultaneous localization and mapping (SLAM) systems, especially in monocular scenarios, still suffers from limited localization accuracy and poor rendering quality. In this paper, we propose a new monocular 3D Gaussian splatting SLAM, which achieves high fidelity online 3D Gaussian splatting based reconstruction given monocular video input with significance-guided pruning. Our key idea is to maintain structural compactness when optimizing the 3D Gaussians, by adaptively pruning them using a global significance evaluation based on multidimensional cues such as visibility, opacity, and volume coefficient. Specifically, we use a frame-to-model pipeline that jointly optimizes camera poses and 3D Gaussians within a sliding-window framework, ensuring a globally consistent 3D Gaussian representation for high-fidelity rendering with significance-guided pruning. Furthermore, an online monocular depth estimation model is incorporated to extract depth priors from input images, to effectively initialize the 3D Gaussian attributes for better camera tracking. Extensive experiments on Replica and TUM datasets demonstrate that our approach substantially improves both tracking performance and rendering fidelity, and thus provides state-of-the-art results.