Perception

Instance Segmentation

Instance segmentation is the computer vision task of detecting each object in an image and delineating it with a pixel-accurate mask, distinguishing individual instances of the same class. Mask R-CNN established the dominant two-stage approach, and the Segment Anything Model (SAM) introduced promptable, class-agnostic segmentation that transfers zero-shot to novel objects and domains.

Why it matters for physical AI

Per-object masks let manipulation systems isolate a grasp target from clutter, estimate object extents, and track items through a scene, forming the perceptual front end of most pick-and-place stacks.