arXiv:2601.22054v2 Announce Type: replace-cross Abstract: Scaling has powered recent advances in vision foundation models, yet extending this paradigm to metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data. We introduce Metric Anything, a simple and scalable pretraining framework that learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures. Central to our approach is the Sparse Metric Prompt, created by r
This development leverages the recent trend of scaling foundation models to address a longstanding challenge in 3D vision, particularly with heterogeneous data sources, bridging a critical gap for practical applications.
Improving metric depth estimation with noisy, diverse 3D sources without extensive human supervision or specific hardware unlocks more robust and versatile AI applications in robotics, autonomous systems, and AR/VR.
The ability to learn accurate metric depth from varied, imperfect 3D data sources will accelerate the development and deployment of real-world AI systems that rely on precise 3D understanding, making them more adaptable and less reliant on pristine datasets.
- · AI/Robotics Developers
- · Autonomous Vehicle Industry
- · AR/VR Industry
- · 3D Data Providers
- · Vendors of highly specialized 3D sensors
- · Companies reliant on pristine, homogeneous 3D datasets
More accurate and resilient 3D computer vision models will become widely available, enhancing current AI applications.
This will enable more sophisticated autonomous agents and robotic systems that can operate effectively in complex, unstructured real-world environments.
The reduced dependency on carefully curated 3D data could lead to a proliferation of new AI applications previously constrained by data quality and availability, democratizing access to advanced 3D perception.
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Read at arXiv cs.AI