Camera calibration and the search for infinity
This paper considers the problem of self-calibration of a camera from an image sequence in the case where the camera's internal parameters (most notably focal length) may change. The problem of camera self-calibration from a sequence of images has proven to be a difficult one in practice, due to the need ultimately to resort to non-linear methods, which have often proven to be unreliable. In a stratified approach to self-calibration, a projective reconstruction is obtained first and this is successively refined first to an affine and then to a Euclidean (or metric) reconstruction. It has been observed that the difficult step is to obtain the affine reconstruction, or equivalently to locate the plane at infinity in the projective coordinate frame. The problem is inherently non-linear and requires iterative methods that risk not finding the optimal solution. The present paper overcomes this difficulty by imposing chirality constraints to limit the search for the plane at infinity to a 3-dimensional cubic region of parameter space. It is then possible to carry out a dense search over this cube in reasonable time. For each hypothesised placement of the plane at infinity, the calibration problem is reduced to one of calibration of a nontranslating camera, for which fast non-iterative algorithms exist. A cost function based on the result of the trial calibration is used to determine the best placement of the plane at infinity. Because of the simplicity of each trial, speeds of over 10,000 trials per second are achieved on a 256 MHz processor. It is shown that this dense search allows one to avoid areas of local minima effectively and find global minima of the cost function.