An optimization framework for efficient self-calibration and motion determination

Abstract

The problem of calibrating a camera is extremely important for practical applications. While classical work is based on the use of a calibration pattern whose 3D model is a priori known, self-calibration methods have also been investigated. These methods require only point matches obtained during unknown motions, without any a priori knowledge of the scenes. However, the method initially presented by Faugeras, Luong and Maybank (1992) was computationally expensive and sensitive to noise. In this paper, we propose an alternative method to compute at the same time camera calibration and motion, which is robust and efficient. This method allows also to take into account the important trinocular constraints. The practical applicability of our algorithm is illustrated with numerous real examples, which includes 3D reconstruction.