Paper Detail
Ziren Gong, Xiaohan Li, Fabio Tosi, Ninghui Xu, Stefano Mattoccia, Jianfei Cai, Matteo Poggi
This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on a feed-forward model from the 3R family to process RGB videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-agent and inter-agent levels to obtain the final global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift occurring along the feed-forward pipeline. We evaluate MAGiSt3R on both synthetic and real-world datasets, demonstrating its superior reconstruction and camera tracking accuracy compared to state-of-the-art approaches.
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@article{gong2026magist3r,
title = {MAGiSt3R: Multi-Agent Feed-forward 3D Reconstruction from Monocular RGB Videos},
author = {Ziren Gong and Xiaohan Li and Fabio Tosi and Ninghui Xu and Stefano Mattoccia and Jianfei Cai and Matteo Poggi},
year = {2026},
abstract = {This paper presents MAGiSt3R, a multi-agent 3D reconstruction framework performing reconstruction and camera tracking for monocular RGB videos at almost 10 FPS. MAGiSt3R relies on a feed-forward model from the 3R family to process RGB videos and regress local point maps, and on a merging model, MAGMA, that combines local maps at both intra-agent and inter-agent levels to obtain the final global point map. Furthermore, MAGiSt3R performs pose graph optimization to mitigate cumulative camera drift },
url = {https://arxiv.org/abs/2607.15211},
keywords = {cs.CV},
eprint = {2607.15211},
archiveprefix = {arXiv},
}
{}