MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
Abstract
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.
Cite
Text
Murai et al. "MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.01556Markdown
[Murai et al. "MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/murai2025cvpr-mast3rslam/) doi:10.1109/CVPR52734.2025.01556BibTeX
@inproceedings{murai2025cvpr-mast3rslam,
title = {{MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors}},
author = {Murai, Riku and Dexheimer, Eric and Davison, Andrew J.},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2025},
pages = {16695-16705},
doi = {10.1109/CVPR52734.2025.01556},
url = {https://mlanthology.org/cvpr/2025/murai2025cvpr-mast3rslam/}
}