  In many fields, self-supervised learning solutions are rapidly evolving and
filling the gap with supervised approaches. This fact occurs for depth
estimation based on either monocular or stereo, with the latter often providing
a valid source of self-supervision for the former. In contrast, to soften
typical stereo artefacts, we propose a novel self-supervised paradigm reversing
the link between the two. Purposely, in order to train deep stereo networks, we
distill knowledge through a monocular completion network. This architecture
exploits single-image clues and few sparse points, sourced by traditional
stereo algorithms, to estimate dense yet accurate disparity maps by means of a
consensus mechanism over multiple estimations. We thoroughly evaluate with
popular stereo datasets the impact of different supervisory signals showing how
stereo networks trained with our paradigm outperform existing self-supervised
frameworks. Finally, our proposal achieves notable generalization capabilities
dealing with domain shift issues. Code available at
https://github.com/FilippoAleotti/Reversing
