Stereo matching estimates the disparity between a rectified image pair, which
is of great importance to depth sensing, autonomous driving, and other related
tasks. Previous works built cost volumes with cross-correlation or
concatenation of left and right features across all disparity levels, and then
a 2D or 3D convolutional neural network is utilized to regress the disparity
maps. In this paper, we propose to construct the cost volume by group-wise
correlation. The left features and the right features are divided into groups
along the channel dimension, and correlation maps are computed among each group
to obtain multiple matching cost proposals, which are then packed into a cost
volume. Group-wise correlation provides efficient representations for measuring
feature similarities and will not lose too much information like full
correlation. It also preserves better performance when reducing parameters
compared with previous methods. The 3D stacked hourglass network proposed in
previous works is improved to boost the performance and decrease the inference
computational cost. Experiment results show that our method outperforms
previous methods on Scene Flow, KITTI 2012, and KITTI 2015 datasets. The code
is available at this https URL

本文提出了使用分组相关来构建成本体积，并改进了三维堆叠沙漏网络，以用于立体匹配的深度感知，通过实验证明在 Scene Flow、KITI 2012 和 KITTI 2015 数据集上，该方法优于之前的方法。