Perception

Depth Estimation

Depth Estimation is the inference of scene distance from camera imagery, either by triangulating correspondences across calibrated stereo pairs or by predicting depth from a single image with a learned model. Monocular methods, from early supervised networks to foundation models like MiDaS and Depth Anything, exploit pictorial cues and large-scale training to generalize across scenes, though they typically recover depth only up to an unknown scale unless metrically fine-tuned.

Why it matters for physical AI

Cameras are the cheapest, densest sensors on a robot, and turning their pixels into 3D structure enables navigation and manipulation without dedicated depth hardware. Scale ambiguity and calibration remain the key deployment caveats.