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。

DCVSMNet：双成本体素匹配网络

DCVSMNet: Double Cost Volume Stereo Matching Network

We design a multiscopic vision system that utilizes a low-cost monocular RGB
camera to acquire accurate depth estimation. Unlike multi-view stereo with
images captured at unconstrained camera poses, the proposed system controls the
motion of a camera to capture a sequence of images in horizontally or
vertically aligned positions with the same parallax. In this system, we propose
a new heuristic method and a robust learning-based method to fuse multiple cost
volumes between the reference image and its surrounding images. To obtain
training data, we build a synthetic dataset with multiscopic images. The
experiments on the real-world Middlebury dataset and real robot demonstration
show that our multiscopic vision system outperforms traditional two-frame
stereo matching methods in depth estimation. Our code and dataset are available
at this https URL

本研究提出了一种基于多视角视觉系统的方法， 使用低成本单目 RGB 相机获得准确的深度估计， 通过多张水平或垂直对齐的图像并利用新的启发式方法和鲁棒学习方法来融合多个代价体之间的数据以提高深度估计的性能，实验结果表明该方法优于传统的二帧立体匹配方法。