Non-Parametric Self-Calibration

David Nistér, Henrik Stewénius, Etienne Grossmann

Abstract: In this paper, we develop a theory of non-parametric self-calibration. Recently, schemes have been devised for non-parametric laboratory calibration, but not for self-calibration. We allow an arbitrary warp to model the intrinsic mapping, with the only restriction that the camera is central and that the intrinsic mapping has a well-defined non-singular matrix derivative at a finite number of points under study. We give a number of theoretical results, both for infinitesimal motion and finite motion, for a finite number of observations and when observing motion over a dense image, for rotation and translation. Our main result is that through observing the flow induced by three instantaneous rotations at a finite number of points of the distorted image, we can perform projective reconstruction of those image points on the undistorted image. We present some results with synthetic and real data.