MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Scaling has driven major advances in vision foundation mod-els, yet extending it to metric depth estimation remains challenging dueto heterogeneous sensor noise, camera-dependent biases, and metric am-biguity in noisy cross-source 3D data. We introduce MetricAnything,a simple and scalable pretraining framework that learns metric depthfrom diverse, noisy 3D sources without manual prompts, camera-specificmodeling, or task-specific architectures. At its core is the Sparse Met-ric Prompt, formed by randomly masking depth maps, which acts asa universal interface to decouple spatial reasoning from sensor and cam-era biases. Pretrained on ∼20M image–depth pairs from reconstructed,captured, and rendered 3D data across 10,000+ camera models, Met-ricAnything demonstrates—for the first time—a clear scaling trend inmetric depth. The model excels at prompt-driven tasks such as depthcompletion, super-resolution, and Radar-camera fusion, while its distilledprompt-free student achieves state-of-the-art results in monocular depthestimation, camera intrinsics recovery, single/multi-view metric 3D re-construction, and VLA planning. Using its pretrained ViT as a visualencoder also significantly enhances Multimodal Large Language Modelsin spatial intelligence. These results show that metric depth estimationcan follow the same scaling laws as modern foundation models, paving ascalable and efficient path toward real-world metric perception. Projectpage: https://metric-anything.github.io/metric-anything-io/