Kruppa equation based camera calibration from homography induced by remote plane
Camera calibration is an indispensable step in retrieving 3D metric information from 2D images. One classical self-calibration method is based on Kruppa equation derived from pairwise image correspondences. However, the calibration constraints derived from Kruppa equation are quadratic, which are computationally intensive and difficult to obtain initial values. In this paper, we propose a new initialization algorithm to estimate the unknown scalar in the equation, thus the camera parameters can be initialized linearly in a closed form and then optimized iteratively via the Kruppa constraints. We prove that the scalar can be uniquely recovered from the infinite homography and propose a practical method to estimate the homography from a physical or virtual plane located at far distance to the camera. Extensive experiments on synthetic and real images validate the effectiveness of the proposed method.
kruppa equation computer vision camera self-calibration plane induced homography 3d reconstruction