Robustness evaluation of stereo algorithms on long stereo sequences

This paper presents an approach to test stereo algorithms against long stereo sequences (say, 100+ image pairs). Stereo sequences of this length have not been quantitatively evaluated in the past, even though they are the input data of a vision-based driver assistance system. Using stereo sequences allows one to exploit the temporal information, which is, in general, not well used currently. The presented approach focuses on evaluating the robustness of algorithms against differing noise parameters (Gaussian noise, brightness differences, and blurring).