  A robust solution for semi-dense stereo matching is presented. It utilizes
two CNN models for computing stereo matching cost and performing
confidence-based filtering, respectively. Compared to existing CNNs-based
matching cost generation approaches, our method feeds additional global
information into the network so that the learned model can better handle
challenging cases, such as lighting changes and lack of textures. Through
utilizing non-parametric transforms, our method is also more self-reliant than
most existing semi-dense stereo approaches, which rely highly on the adjustment
of parameters. The experimental results based on Middlebury Stereo dataset
demonstrate that the proposed approach outperforms the state-of-the-art
semi-dense stereo approaches.
