This paper gives a practical algorithm for the selfcalibration of a camera from several views. The method involves non-iterative methods for finding an initial calibration for the camera, followed by leastsquares iteration to an optimum solution. At the same time, a scaled Euclidean reconstruction of the scene appearing in the images is computed.

The paper gives a practical rapid algorithm for doing projective reconstruction of a scene consisting of a set of lines seen in three or more images with uncalibrated cameras. The algorithm is evaluated on real and ideal data to determine its performance in the presence of varying degrees of noise. By carefully consideration of sources of error, it is possible to get accurate reconstruction with realistic levels of noise. The algorithm can be applied to images from different cameras or the same camera. For images with the same camera with unknown calibration, it is possible to do a complete Euclidean reconstruction of the image. This extends to the case of uncalibrated cameras previous results of Spetsakis and Aloimonos on scene reconstruction from lines.

Abstract