Perception
Rectification
Rectification is the warping of a stereo image pair so that corresponding epipolar lines become horizontal and row-aligned, reducing stereo correspondence search from two dimensions to one. Computed from calibrated camera intrinsics, extrinsics, and lens distortion models, it is the standard preprocessing step before block matching or learned stereo disparity estimation. The term also covers single-image undistortion to an ideal pinhole model.
Why it matters for physical AI
Nearly every depth camera and stereo pipeline on a robot rectifies images before matching, and calibration drift that invalidates rectification is a common silent cause of degraded depth and downstream grasp failures.
Build physical AI
Put these concepts to work on real hardware
Axol is a dual-arm robot built for physical AI — teleoperate it, collect demonstrations, and deploy learned policies out of the box.