We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.
View on arXiv@article{murai2025_2412.12392,
title={ MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors },
author={ Riku Murai and Eric Dexheimer and Andrew J. Davison },
journal={arXiv preprint arXiv:2412.12392},
year={ 2025 }
}