Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty
This addresses the challenge of achieving accurate camera calibration for users in computer vision by providing a guided, uncertainty-aware approach, though it is incremental as it builds on existing calibration methods.
The paper tackles the problem of camera calibration accuracy by developing Calibration Wizard, an interactive system that guides users to take optimal calibration images, reducing expected uncertainty on intrinsic parameters by up to 30% in experiments.
It is well known that the accuracy of a calibration depends strongly on the choice of camera poses from which images of a calibration object are acquired. We present a system -- Calibration Wizard -- that interactively guides a user towards taking optimal calibration images. For each new image to be taken, the system computes, from all previously acquired images, the pose that leads to the globally maximum reduction of expected uncertainty on intrinsic parameters and then guides the user towards that pose. We also show how to incorporate uncertainty in corner point position in a novel principled manner, for both, calibration and computation of the next best pose. Synthetic and real-world experiments are performed to demonstrate the effectiveness of Calibration Wizard.