论文标题
逆转周期：通过增强的单眼蒸馏自我监督的深度立体声
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
论文作者
论文摘要
在许多领域，自我监管的学习解决方案正在迅速发展，并通过有监督的方法来填补空白。这一事实是基于单眼或立体声的深度估计而发生的，后者通常为前者提供有效的自学能力来源。相比之下，为了软化典型的立体声伪像，我们提出了一种新颖的自我监督范式，以逆转两者之间的联系。为了培训深层立体声网络，我们是故意通过单眼完成网络来提炼知识的。该体系结构利用了传统的立体声算法来利用单图线的线索和很少的稀疏点，以通过多个估计的共识机制来估计密集而准确的差异图。我们使用流行的立体声数据集对不同的监督信号的影响进行了彻底评估，以显示使用范式训练的立体网络如何胜过现有的自我监督框架。最后，我们的建议达到了涉及域转移问题的显着概括能力。可在https://github.com/filippoaleotti/reversing上找到代码
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
