  Stereo matching is one of the widely used techniques for inferring depth from
stereo images owing to its robustness and speed. It has become one of the major
topics of research since it finds its applications in autonomous driving,
robotic navigation, 3D reconstruction, and many other fields. Finding pixel
correspondences in non-textured, occluded and reflective areas is the major
challenge in stereo matching. Recent developments have shown that semantic cues
from image segmentation can be used to improve the results of stereo matching.
Many deep neural network architectures have been proposed to leverage the
advantages of semantic segmentation in stereo matching. This paper aims to give
a comparison among the state of art networks both in terms of accuracy and in
terms of speed which are of higher importance in real-time applications.
