Generic self-calibration of central cameras - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Computer Vision and Image Understanding Year : 2010

Generic self-calibration of central cameras


We consider the self-calibration problem for a generic imaging model that assigns projection rays to pixels without a parametric mapping. 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. In order to do this we consider specific camera motions, concretely, pure translations and rotations, although without the knowledge of rotation and translation parameters (rotation angles, axis of rotation, translation vector). Knowledge of the type of motion, together with image matches, gives geometric constraints on the projection rays. 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 algorithms for full metric self-calibration, that use rotational and translational motions or just rotational motions.

Dates and versions

inria-00523989 , version 1 (06-10-2010)



Srikumar Ramalingam, Peter Sturm, Suresh K. Lodha. Generic self-calibration of central cameras. Computer Vision and Image Understanding, 2010, Special issue on Omnidirectional Vision, Camera Networks and Non-conventional Cameras, 114 (2), pp.210-219. ⟨10.1016/j.cviu.2009.07.007⟩. ⟨inria-00523989⟩
143 View
0 Download



Gmail Facebook X LinkedIn More