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MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision
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MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision
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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 facilitate effective geometry learning. Furthermore, we propose a set of novel global and local geometry supervisions 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. Code and models can be found on our project page.
Forward citations
Cited by 12 Pith papers
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MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement
Iterative sparse-3D-convolution refinement in a log-depth voxel shell, instead of 2D image-plane refinement, sharply improves fine-detail geometry in monocular point maps and sets state of the art on local fine-detail...
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SeeGroup: Multi-Layer Depth Estimation of Transparent Surfaces via Self-Determined Grouping
SeeGroup formulates per-pixel multi-layer depth as a point process with permutation-invariant likelihood to support arbitrary groupings, raising quadruplet relative depth accuracy from 61.34% to 70.09% on the LayeredD...
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X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras
X-Lens fuses arbitrary calibrated fisheye and pinhole views into real-time metric depth at 41 FPS with a 0.04B-parameter model and a new 266K-frame synthetic dataset.
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HandsOnWorld: Unconstrained Egocentric Video Generation with Camera-Disentangled Hand Control
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VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
VEGA reconstructs local geometry from monocular egocentric video to create supervised trajectories that train a flow-matching VLA policy, yielding lower collision rates on a new benchmark and in real-world tests.
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VEGA: Learning Navigation VLAs from In-the-Wild Egocentric Video with Geometric Trajectory Supervision
Unlabeled egocentric video can be converted into obstacle-aware goal-conditioned navigation supervision through monocular geometry, ESDFs, and MPPI-planned trajectories.
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SPEAR-1 combines a 3D-enriched VLM with embodied control to match or exceed existing robotic foundation models using 20 times fewer robot demonstrations.
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ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device
A 6.1M-parameter monocular depth network, distilled from Depth Anything v2-Large over 14.1M multi-domain images, achieves the best zero-shot accuracy–efficiency trade-off among lightweight models across five benchmark...
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