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
this https URL

本论文提出了一种新颖的自监督范式，通过单目完成网络提取单张图像线索和少量稀疏点用多个估计的共识机制来估计密集而准确的视差图，该方案在处理域偏移问题时具有显着的泛化能力。