Locally-weighted homographies for calibration of imaging systems

A homography is traditionally formulated as a linear transformation and is used in multiple-view geometry as a linear map between projective planes (or images). Analogous to the use of homography-based techniques to calibrate a pin-hole camera, non-linear homographies extend the pinhole camera model to deal with non-linearities such as lens distortion. In this work, we propose a novel non-parametric nonlinear homography technique. Unlike a parametric non-linear mapping that can have inherent biases, this technique automatically adjusts model complexity to account for non-linearities in observed data. With this technique, we demonstrate nonparametric estimation of lens distortion from a single calibration image. We evaluate this technique on real-world lenses and show that this technique can improve the stability of cameracalibration. Furthermore, the non-parametric nature of our technique allows rectification of arbitrary sources of lens distortion.