  We introduce Double Cost Volume Stereo Matching Network(DCVSMNet) which is a
novel architecture characterised by by two small upper (group-wise) and lower
(norm correlation) cost volumes. Each cost volume is processed separately, and
a coupling module is proposed to fuse the geometry information extracted from
the upper and lower cost volumes. DCVSMNet is a fast stereo matching network
with a 67 ms inference time and strong generalization ability which can produce
competitive results compared to state-of-the-art methods. The results on
several bench mark datasets show that DCVSMNet achieves better accuracy than
methods such as CGI-Stereo and BGNet at the cost of greater inference time.
