A hierarchical stereo matching algorithm based on adaptive support region aggregation method

Stereo matching is a fundamental process in many application fields. An accurate depth information is useful for stereo systems to separate occluding image components. In the conception of stereo matching algorithms, various works rely on the Census Transform (CT) as a cost computation step, due to its robustness against radiometric changes. In this paper, we propose a new variant of the CT cost function which incorporates edge side information for further compensating radiometric changes. We demonstrate that the proposed variant matching cost improves significantly the quality of the disparity results. In addition, an aggregation method, based on the adaptive support region, is implemented into a hierarchical fusion processing scheme, by incorporating low frequency information into high frequency using a robust exponential function. This allows a multi-scale interaction within the adaptive cost aggregation. Hence, significant erroneous disparities are reduced, especially in textureless regions. Experiments were conducted on the KITTI benchmark and the obtained results have shown the potential merits of the proposed framework.