  Depth estimation from a stereo image pair has become one of the most explored
applications in computer vision, with most of the previous methods relying on
fully supervised learning settings. However, due to the difficulty in acquiring
accurate and scalable ground truth data, the training of fully supervised
methods is challenging. As an alternative, self-supervised methods are becoming
more popular to mitigate this challenge. In this paper, we introduce the H-Net,
a deep-learning framework for unsupervised stereo depth estimation that
leverages epipolar geometry to refine stereo matching. For the first time, a
Siamese autoencoder architecture is used for depth estimation which allows
mutual information between the rectified stereo images to be extracted. To
enforce the epipolar constraint, the mutual epipolar attention mechanism has
been designed which gives more emphasis to correspondences of features which
lie on the same epipolar line while learning mutual information between the
input stereo pair. Stereo correspondences are further enhanced by incorporating
semantic information to the proposed attention mechanism. More specifically,
the optimal transport algorithm is used to suppress attention and eliminate
outliers in areas not visible in both cameras. Extensive experiments on
KITTI2015 and Cityscapes show that our method outperforms the state-ofthe-art
unsupervised stereo depth estimation methods while closing the gap with the
fully supervised approaches.
