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• Metric Anything introduces Sparse Metric Prompt, a universal interface that decouples spatial reasoning from sensor and camera biases by randomly masking depth maps.method 90%
• Metric Anything demonstrates for the first time a clear scaling trend in the metric depth track using about 20M image-depth pairs across 10000 camera models.result 90%
• The pretrained model excels at prompt-driven tasks including depth completion, super-resolution and Radar-camera fusion.result 85%
• The distilled prompt-free student achieves state-of-the-art results on monocular depth estimation, camera intrinsics recovery, single/multi-view metric 3D reconstruction, and VLA planning.result 85%
• Using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence.result 80%
• Metric depth estimation can benefit from the same scaling laws that drive modern foundation models.technical 85%
• The framework learns metric depth from noisy, diverse 3D sources without manually engineered prompts, camera-specific modeling, or task-specific architectures.method 90%
• Scaling metric depth estimation remains challenging due to heterogeneous sensor noise, camera-dependent biases, and metric ambiguity in noisy cross-source 3D data.technical 90%

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