We propose MoGe-2, an advanced open-domain geometry estimation model that recovers a metric scale 3D point map of a scene from a single image. Our method builds upon the recent monocular geometry estimation approach, MoGe, which predicts affine-invariant point maps with unknown scales. We explore effective strategies to extend MoGe for metric geometry prediction without compromising the relative geometry accuracy provided by the affine-invariant point representation. Additionally, we discover that noise and errors in real data diminish fine-grained detail in the predicted geometry. We address this by developing a unified data refinement approach that filters and completes real data from different sources using sharp synthetic labels, significantly enhancing the granularity of the reconstructed geometry while maintaining the overall accuracy. We train our model on a large corpus of mixed datasets and conducted comprehensive evaluations, demonstrating its superior performance in achieving accurate relative geometry, precise metric scale, and fine-grained detail recovery -- capabilities that no previous methods have simultaneously achieved.
Rankings in comprehensive evaluations. Our method achieves accurate Relative Geometry (RG), precise Metric Geometry (MG), and Sharp Detail (SD) – capabilities not simultaneously achieved by previous approaches. * Methods do not predict camera intrinsics and are evaluated on depth only. † MoGe does not predict metric scale. For details of evaluations, please refer to our paper.
● Zoom - Scroll mouse wheel or pinch
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● Pan - Right drag
● Pick a point - Left click
Comparison with two other state-of-the-art monocular metric geoemtry estimation models.
(Results are visualized as raw, unfiltered point clouds here to fairly assess output quality.)
@misc{wang2025moge2accuratemonoculargeometry,
title={MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details},
author={Ruicheng Wang and Sicheng Xu and Yue Dong and Yu Deng and Jianfeng Xiang and Zelong Lv and Guangzhong Sun and Xin Tong and Jiaolong Yang},
year={2025},
eprint={2507.02546},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.02546},
}