  In this paper, we present a novel approach to detect ground control points
(GCPs) for stereo matching problem. First of all, we train a convolutional
neural network (CNN) on a large stereo set, and compute the matching confidence
of each pixel by using the trained CNN model. Secondly, we present a ground
control points selection scheme according to the maximum matching confidence of
each pixel. Finally, the selected GCPs are used to refine the matching costs,
and we apply the new matching costs to perform optimization with semi-global
matching algorithm for improving the final disparity maps. We evaluate our
approach on the KITTI 2012 stereo benchmark dataset. Our experiments show that
the proposed approach significantly improves the accuracy of disparity maps.
