MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

By Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, Jiaolong Yang. Computer Vision and Pattern Recognition, 2024

305 citations recorded by the source corpus.

We present MoGe, a powerful model for recovering 3D geometry from monocular open-domain images. Given a single image, our model directly predicts a 3D point map of the captured scene with an affine-invariant representation, which is agnostic to true global scale and shift. This new representation precludes ambiguous supervision in training and facilitates effective geometry learning. Furthermore, we propose a set of novel global and local geometry supervision techniques that empower the model to learn high-quality geometry. These include a robust, optimal, and efficient point cloud alignment solver for accurate global shape learning, and a multi-scale local geometry loss promoting precise local geometry supervision. We train our model on a large, mixed dataset and demonstrate its strong generalizability and high accuracy. In our comprehensive evaluation on diverse unseen datasets, our model significantly outperforms state-of-the-art methods across all tasks, including monocular estimation of 3D point map, depth map, and camera field of view.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5354957b11727f2ec86abe4587ce7dfa18de795d

Citations in the corpus

• Continuous 3D Perception Model with Persistent State (2025, unknown): “Extensive research [7, 27, 42, 55, 58, 59, 85, 92] focuses on estimating monocular depth.”
• Depth Anything 3: Recovering the Visual Space from Any Views (2025, unknown): “For geometric supervision, in addition to a standard depth-gradient loss, we adopt ROE alignment with the global–local loss introduced in [95].”