  Computational stereo is one of the classical problems in computer vision.
Numerous algorithms and solutions have been reported in recent years focusing
on developing methods for computing similarity, aggregating it to obtain
spatial support and finally optimizing an energy function to find the final
disparity. In this paper, we focus on the feature extraction component of
stereo matching architecture and we show standard CNNs operation can be used to
improve the quality of the features used to find point correspondences.
Furthermore, we propose a simple space aggregation that hugely simplifies the
correlation learning problem. Our results on benchmark data are compelling and
show promising potential even without refining the solution.
