A stratified approach to metric self-calibration
Camera calibration is essential to many computer vision applications. In practice this often requires cumbersome calibration procedures to be carried out regularly. In the last few years a lot of work has been done on self-calibration of cameras, ranging from weak calibration to metric calibration. It has been shown that a metric calibration of the camera setup (up to scale) was possible based on the rigidity of the scene only. In this paper a stratified approach is proposed which gradually retrieves the metric calibration of the camera setup. Starting from an uncalibrated image sequence the projective calibration is retrieved first. In projective space the plane at infinity is then identified yielding the affine calibration. This is achieved using a constraint which can be formulated between any two arbitrary images of the sequence. Once the affine calibration is known the upgrade to metric is easily obtained through linear equations.