Towards Generic Self-Calibration of Central Cameras

We consider the self-calibration problem for the generic imaging model that assigns projection rays to pixels without a parametric mapping. In this paper, we consider the central variant of this model, which encompasses all camera models with a single effective viewpoint. Self-calibration refers to calibrating a camera’s projection rays, purely from matches between images, i.e. without knowledge about the scene such as using a calibration grid. This paper presents our first steps towards generic self-calibration; we consider specific camera motions, concretely, pure translations and rotations, although without knowing rotation angles etc. Knowledge of the type of motion, together with image matches, gives geometric constraints on the projection rays. These constraints are formulated and we show for example that with translational motions alone, self-calibration can already be performed, but only up to an affine transformation of the set of projection rays. We then propose a practical algorithm for full metric self-calibration, that uses rotational and translational motions.