Camera self-calibration and analysis of singular cases

Abstract

Obtaining a 3D model for the world is one of main goals of computer vision. The task to achieve this goal is usually divided into several modules. Camera self-calibration, which is one key step among them, links the so-called projective and metric reconstruction. However, a lot of existed self-calibration algorithms are fairly unstable and thus fail to take up this role. The main reason is that singular cases are not rigorously detected. In this thesis, a new camera self-calibration approach based on Kruppa's equations is proposed. We assume only the focal length is unknown and constant, the Kruppa's equations are then decomposed as two linear and one quadratic equations. All of generic singular cases, which are nearly correspondent to algebraically singular cases for those equations are fully derived and analyzed. We then thoroughly carry out experiments and find the algorithm is quite stable and easy to implement when the generic singular cases are excluded.