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.

Double Cost Volume Stereo Matching Network (DCVSMNet) 是一种新颖的架构，具有两个小型的上部（分组）和下部（规范相关）成本体积。每个成本体积分别处理，并提出了一种耦合模块来融合从上部和下部成本体积中提取的几何信息。DCVSMNet 是一种快速的立体匹配网络，推理时间为 67 毫秒，具有强大的泛化能力，与最先进的方法相比，可以产生具有竞争力的结果。在几个基准数据集上的结果显示，DCVSMNet 在较大推理时间成本的情况下实现了比 CGI-Stereo 和 BGNet 等方法更好的准确性。