MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
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 randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its 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. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at http://metric-anything.github.io/metric-anything-io/ to support community research.
697c3fd1a67238fac88cc1d6
Baorui Ma
697c3fd1a67238fac88cc1d7
Jiahui Yang
claimed_verified
2026-01-30T09:35:33.255Z
67053e91bacadba833e4ffff
/avatars/365025ea4330a2e46a2c47f65c3637c4.svg
jhyang
yjh001
user
yjh001
697c3fd1a67238fac88cc1d8
Donglin Di
697c3fd1a67238fac88cc1d9
Xuancheng Zhang
697c3fd1a67238fac88cc1da
Jianxun Cui
697c3fd1a67238fac88cc1db
Hao Li
697c3fd1a67238fac88cc1dc
Yan Xie
697c3fd1a67238fac88cc1dd
Wei Chen
MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources
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 randomly masking depth maps, which serves as a universal interface that decouples spatial reasoning from sensor and camera biases. Using about 20M image-depth pairs spanning reconstructed, captured, and rendered 3D data across 10000 camera models, we demonstrate-for the first time-a clear scaling trend in the metric depth track. The pretrained model excels at prompt-driven tasks such as depth completion, super-resolution and Radar-camera fusion, while its 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. We also show that using pretrained ViT of Metric Anything as a visual encoder significantly boosts Multimodal Large Language Model capabilities in spatial intelligence. These results show that metric depth estimation can benefit from the same scaling laws that drive modern foundation models, establishing a new path toward scalable and efficient real-world metric perception. We open-source MetricAnything at http://metric-anything.github.io/metric-anything-io/ to support community research.
Metric Anything presents a scalable pretraining framework for metric depth estimation that leverages diverse 3D data and sparse metric prompts to achieve superior performance across multiple vision tasks.
vision foundation models metric depth estimation sparse metric prompt pretraining framework depth completion super-resolution Radar-camera fusion monocular depth estimation camera intrinsics recovery 3D reconstruction VLA planning visual encoder Multimodal Large Language Model spatial intelligence