Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging such cue. Although self-supervised learning has recently spread across many computer vision tasks, it has been barely considered in the field of confidence estimation. In this paper, we propose a flexible and lightweight solution enabling self-adapting confidence estimation agnostic to the stereo algorithm or network. Our approach relies on the minimum information available in any stereo setup (i.e., the input stereo pair and the output disparity map) to learn an effective confidence measure. This strategy allows us not only a seamless integration with any stereo system, including consumer and industrial devices equipped with undisclosed stereo perception methods, but also, due to its self-adapting capability, for its out-of-the-box deployment in the field. Exhaustive experimental results with different standard datasets support our claims, showing how our solution is the first-ever enabling online learning of accurate confidence estimation for any stereo system and without any requirement for the end-user.
翻译:估计由立体算法推算的差异地图的可信度,由于利用这种提示的应用数量不断增加,多年来已成为一项非常重要的任务。虽然自监学习最近在许多计算机的视觉任务中传播,但在信任估计领域却很少考虑。在本文中,我们提出了一个灵活和轻量的解决方案,以便能够对立体算法或网络进行自我调整信心估计。我们的方法依靠任何立体设置(即输入立体配对和产出差异图)中现有的最低限度信息来学习有效的信任措施。这一战略不仅使我们能够与任何立体系统,包括配备不公开立体立体感方法的消费者和工业装置无缝结合,而且由于其自我调整能力,也能够在外地部署外置机。不同标准数据集的耗尽性实验结果支持我们的要求,表明我们的解决方案是如何首次使在线学习任何立体系统准确的信任估计,而无需最终用户的任何要求。