Widening siamese architectures for stereo matching.

Highlights • We propose the use of pooling and deconvolution operations in CNNs in order to greatly increase their receptive fields. • We highlight characteristics specific to the stereo matching problem and how they can relate to the way that CNNs operate. • We use a simple feature space transformation that allow us to better evaluate the quality of the features extracted. • We present several easy to train models capable of accurate stereo matching, even without spatial regularization. Abstract 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 use a simple space aggregation that hugely simplifies the correlation learning problem, allowing us to better evaluate the quality of the features extracted. Our results on benchmark data are compelling and show promising potential even without refining the solution. [ABSTRACT FROM AUTHOR]