  The success of existing deep-learning based multi-view stereo (MVS)
approaches greatly depends on the availability of large-scale supervision in
the form of dense depth maps. Such supervision, while not always possible,
tends to hinder the generalization ability of the learned models in
never-seen-before scenarios. In this paper, we propose the first unsupervised
learning based MVS network, which learns the multi-view depth maps from the
input multi-view images and does not need ground-truth 3D training data. Our
network is symmetric in predicting depth maps for all views simultaneously,
where we enforce cross-view consistency of multi-view depth maps during both
training and testing stages. Thus, the learned multi-view depth maps naturally
comply with the underlying 3D scene geometry. Besides, our network also learns
the multi-view occlusion maps, which further improves the robustness of our
network in handling real-world occlusions. Experimental results on multiple
benchmarking datasets demonstrate the effectiveness of our network and the
excellent generalization ability.
