Perception

Optical Flow

Optical flow is the apparent per-pixel motion field between consecutive images, induced by camera and scene motion. Classical estimators include Lucas-Kanade for sparse features and Horn-Schunck for dense fields, while learned models such as RAFT define the modern state of the art. Flow supports visual odometry, moving-object detection, and motion-based representations for policy learning.

Why it matters for physical AI

Motion cues complement static appearance: flow-based features help robots track dynamic scenes, estimate ego-motion, and, in recent work, transfer manipulation skills through motion rather than pixels.