Perception

Depth Anything

Depth Anything is a family of monocular depth estimation foundation models, first released in 2024, trained on a combination of labeled datasets and tens of millions of unlabeled images via large-scale pseudo-labeling. It produces robust relative depth across diverse scenes, with fine-tuned variants providing metric depth. Depth Anything V2 improved detail and robustness using synthetic labels and larger teacher models.

Why it matters for physical AI

Reliable depth from a single RGB camera reduces sensor cost and calibration burden on robot platforms. Foundation-model depth priors increasingly feed grasping, navigation, and 3D scene reconstruction pipelines without per-robot training.