  The reconstruction of a scene via a stereo-camera system is a two-steps
process, where at first images from different cameras are matched to identify
the set of point-to-point correspondences that then will actually be
reconstructed in the three dimensional real world. The performance of the
system strongly relies of the calibration procedure, which has to be carefully
designed to guarantee optimal results. We implemented three different
calibration methods and we compared their performance over 19 datasets. We
present the experimental evidence that, due to the image noise, a single set of
parameters is not sufficient to achieve high accuracy in the identification of
the correspondences and in the 3D reconstruction at the same time. We propose
to calibrate the system twice to estimate two different sets of parameters: the
one obtained by minimizing the reprojection error that will be used when
dealing with quantities defined in the 2D space of the cameras, and the one
obtained by minimizing the reconstruction error that will be used when dealing
with quantities defined in the real 3D world.
