Abstract

The importance of precise homography estimation is often underestimated even though it plays a crucial role in various vision applications such as plane or planarity detection, scene degeneracy tests, camera motion classifification, image stitching, and many more. Ignoring the radial distortion component in homography estimation—even for classical perspective cameras—may lead to signifificant errors or totally wrong estimates. In this paper, we fifill the gap among the homography estimation methods by presenting two algorithms for estimating homography between two cameras with different radial distortions. Both algorithms can handle planar scenes as well as scenes where the relative motion between the cameras is a pure rotation. The fifirst algorithm uses the minimal number of fifive image point correspondences and solves a nonlinear system of polynomial equations using Grobner basis method. The second algo- ¨ rithm uses a non-minimal number of six image point correspondences and leads to a simple system of two quadratic equations in two unknowns and one system of six linear equations. The proposed algorithms are fast, stable, and can be effificiently used inside a RANSAC loop