Efficient stereo matching for moving cameras and decalibrated rigs

In vehicular applications based on motion-stereo using monocular side-looking cameras, pairs of images must usually be rectified very well, to allow the application of dense stereo methods. But also long-term installations of stereo rigs in vehicles require approaches that cope with the decalibration of the cameras. The need for such methods is further underlined by the fact that offline camera calibration is a costly and time-consuming procedure at vehicle production sites. In this paper we propose an approach for dense stereo matching that overcomes issues arising from inaccurately rectified images. For this, we significantly increase the search range for correspondences, but still preserve a high efficiency of the method to allow operation on platforms with highly limited processing resources. We demonstrate the performance of our ideas quantitatively using well known stereo datasets and qualitatively using real video sequences of a motion-stereo application.