An unsupervised stereo matching cost based on sparse representation

Abstract

Supervised stereo matching costs need to learn model parameters from public datasets with ground truth disparity maps. However, it is not so easy to obtain the ground truth disparity maps, thus making the supervised stereo matching costs difficult to apply in practice. This paper proposes an unsupervised stereo matching cost based on sparse representation (USMCSR). This method does not rely on the ground truth disparity maps, besides, it also can reduce the effects of the illumination and exposure changes, thus making it suitable for measuring similarity between pixels in stereo matching. In order to achieve higher computational efficiency, we further propose an efficient parallel method for solving sparse representation coefficients. The extended experimental results on three commonly used datasets demonstrate the effectiveness of the proposed method. Finally, the verification results on the monocular video clip show the USMCSR can also work well without ground truth disparity maps.