Visually Imbalanced Stereo Matching

Yicun Liu, Jimmy Ren, Jiawei Zhang, Jianbo Liu, Mude Lin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2029-2038

Understanding of human vision system (HVS) has inspired many computer vision algorithms. Stereo matching, which borrows the idea from human stereopsis, has been extensively studied in the existing literature. However, scant attention has been drawn on a typical scenario where binocular inputs are qualitatively different (e.g., high-res master camera and low-res slave camera in a dual-lens module). Recent advances in human optometry reveal the capability of the human visual system to maintain coarse stereopsis under such visually imbalanced conditions. Bionically aroused, it is natural to question that: do stereo machines share the same capability? In this paper, we carry out a systematic comparison to investigate the effect of various imbalanced conditions on current popular stereo matching algorithms. We show that resembling the human visual system, those algorithms can handle limited degrees of monocular downgrading but also prone to collapses beyond a certain threshold. To avoid such collapse, we propose a solution to recover the stereopsis by a joint guided-view-restoration and stereo-reconstruction framework. We show the superiority of our framework on KITTI dataset and its extension on real-world applications.