Perception

Semantic Segmentation

Semantic segmentation is the pixel-wise classification of an image into semantic categories, assigning every pixel a label such as table, robot, or floor without distinguishing object instances. Fully convolutional networks (Long et al., 2015) established the modern approach, followed by architectures like DeepLab, U-Net, and transformer-based SegFormer and Mask2Former. Instance and panoptic segmentation extend the task to separate individual objects.

Why it matters for physical AI

Dense per-pixel semantics feed nearly every downstream robotic capability — traversability estimation, object-aware mapping, grasp target isolation — and remain a standard intermediate representation in modular perception stacks.