Perception

Geometric Primitive Fitting

Geometric primitive fitting is the estimation of parametric shapes, such as planes, spheres, cylinders, cones, and superquadrics, from 3D sensor data like point clouds, typically using RANSAC-style robust sampling, Hough transforms, or least-squares refinement. Fitted primitives compress noisy geometry into compact, semantically useful structure: tabletops become planes, cans become cylinders. Primitives support grasp synthesis, collision geometry, and scene abstraction where full mesh reconstruction is unnecessary.

Why it matters for physical AI

Abstracting clutter into simple shapes gives planners tractable collision models and grasp candidates from partial views, remaining a fast, dependable complement to learned perception in structured picking systems.