Perception

Monocular Depth Estimation

Monocular depth estimation is the prediction of per-pixel scene depth from a single RGB image, an ill-posed problem solved by learning statistical regularities of scene geometry from data. Foundation models such as MiDaS and Depth Anything, trained on large mixed datasets, produce robust relative depth zero-shot, while metric variants recover absolute scale. Self-supervised training from video with photometric consistency avoids the need for ground-truth depth.

Why it matters for physical AI

Extracting geometry from cheap RGB cameras removes the need for depth sensors in many manipulation and navigation stacks, and dense depth priors strengthen grasping, obstacle avoidance, and 3D scene representations.