  Three-dimensional (3-D) scene reconstruction is one of the key techniques in
Augmented Reality (AR), which is related to the integration of image processing
and display systems of complex information. Stereo matching is a computer
vision based approach for 3-D scene reconstruction. In this paper, we explore
an improved stereo matching network, SLED-Net, in which a Single Long
Encoder-Decoder is proposed to replace the stacked hourglass network in PSM-Net
for better contextual information learning. We compare SLED-Net to
state-of-the-art methods recently published, and demonstrate its superior
performance on Scene Flow and KITTI2015 test sets.
