MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
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Cited by 20 in World models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
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.
Key facts. Cited by 250 papers in the map (data through 7 Oct 2026). Builds on 73 of its 83 references that are in the map. First posted on arXiv 3 Jul 2025.
Builds on
73 of 83 references.
References · 73
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- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2023Segment Anythingcited by 8,158
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- 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
- 2021Vision Transformers for Dense Predictioncited by 1,789
- 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
38 more
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- 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,346
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
- 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
- 2024Grounding Image Matching in 3D with MASt3Rcited by 960
- 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 747
- 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
- 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
- 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
- 2021DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Camerascited by 673
- 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 667
- 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 646
- 2018Taskonomy: Disentangling Task Transfer Learningcited by 631
- 2020AdaBins: Depth Estimation using Adaptive Binscited by 596
- 2020Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understandingcited by 593
- 2025SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Modelcited by 502
- 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 494
- 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 487
- 2021ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Datacited by 442
- 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
- 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 395
- 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 389
- 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 375
- 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
- 2023Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Imagecited by 357
- 2017Sparsity Invariant CNNscited by 325
- 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 323
- 20243D-VLA: A 3D Vision-Language-Action Generative World Modelcited by 320
- 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 290
- 2019Structured3D: A Large Photo-realistic Dataset for Structured 3D Modelingcited by 285
- 2024GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Imagecited by 283
- 2018DeepMVS: Learning Multi-view Stereopsiscited by 277
- 2019DIODE: A Dense Indoor and Outdoor DEpth Datasetcited by 236
- 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
- 2024MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videoscited by 224
- 2020Learning to Recover 3D Scene Shape from a Single Imagecited by 214
- 2024WonderWorld: Interactive 3D Scene Generation from a Single Imagecited by 197
- 2020A2D2: Audi Autonomous Driving Datasetcited by 189
50 most cited of 73 All 73 (JSON)
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Citing papers per year: 2025: 53; 2026: 197.
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