Visually Imbalanced Stereo Matching Yicun Liu∗13† Jimmy Ren∗1 Jiawei Zhang1 Jianbo Liu12 Mude Lin1 SenseTime Research1 The Chinese University of Hong Kong2 Columbia University3 {rensijie,zhangjiawei,linmude}@sensetime.com1
[email protected] [email protected] Abstract Understanding of human vision system (HVS) has in- spired many computer vision algorithms. Stereo matching, which borrows the idea from human stereopsis, has been ex- tensively studied in the existing literature. However, scant attention has been drawn on a typical scenario where binoc- ular 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 capabil- ity of the human visual system to maintain coarse stereop- sis 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 match- Figure 1. Illustration of visually imbalanced scenarios: (a) input left view (b) Input downgraded right view, from top to bottom: ing algorithms. We show that resembling the human vi- monocular blur, monocular blur with rectification error, monocular sual system, those algorithms can handle limited degrees of noise. (c) Disparity predicted from mainstream monocular depth monocular downgrading but also prone to collapses beyond (only left view as input)/stereo matching (stereo views as input) a certain threshold. To avoid such collapse, we propose a algorithms: from top to bottom are DORN [9], PSMNet [7] and solution to recover the stereopsis by a joint guided-view- CRL [31].