Calibration of fisheye camera using entrance pupil
This addresses calibration accuracy for fisheye cameras in computer vision applications, but it is incremental as it builds on existing thin lens models.
The paper tackles the problem of unreliable intrinsic camera calibration for fisheye lenses, which often violate the single viewpoint assumption, by proposing a new formation model that corrects non-single viewpoint systems to maintain a single viewpoint using entrance pupil variation. The result is slightly better re-projection error and better-estimated camera parameters compared to traditional methods.
Most conventional camera calibration algorithms assume that the imaging device has a Single Viewpoint (SVP). This is not necessarily true for special imaging device such as fisheye lenses. As a consequence, the intrinsic camera calibration result is not always reliable. In this paper, we propose a new formation model that tends to relax this assumption so that a Non-Single Viewpoint (NSVP) system is corrected to always maintain a SVP, by taking into account the variation of the Entrance Pupil (EP) using thin lens modeling. In addition, we present a calibration procedure for the image formation to estimate these EP parameters using non linear optimization procedure with bundle adjustment. From experiments, we are able to obtain slightly better re-projection error than traditional methods, and the camera parameters are better estimated. The proposed calibration procedure is simple and can easily be integrated to any other thin lens image formation model.