Origin Data Lab Examines Stereo Camera Calibration Limits for Physical AI Data

Origin Data Lab has been evaluating the geometric consistency and calibration limitations of its stereo capture workflow, which is used for Physical AI and robotics data. The company's Technical Field Note #04 argues that a single average error value cannot fully describe calibration quality, and that the distribution of epipolar errors can reveal residual misalignment. It also highlights that calibration parameters must match the actual capture setup, since changes in resolution, cropping, or camera mode can affect their validity. Further points include evaluating errors across field angles and using appropriate projection models for wide-angle and fisheye lenses. The note concludes that reliable real-world datasets must be measured, verified, and documented rather than simply collected.