The objective of stereo camera calibration is to estimate the internal and external parameters of each camera. Using these parameters, the 3-D position of a point in the scene, which is identified and matched in two stereo images, can be determined by the method of triangulation. In this paper, we present a camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions. The proposed calibration procedure consists of two steps. In the first step, the calibration parameters are estimated using a closed-form solution based on a distortion-free camera model. In the second step, the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. We introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of our calibration procedure are tested with both synthetic data and real images taken by tele-and wide-angle lenses. The results consistently show significant improvements over less complete camera models.

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