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.

本文提出了一种灵活且轻量化的解决方案，该方案可实现自适应置信度估计，具备与任何立体系统（包括未公开视觉感知方法的消费和工业设备）的无缝集成性，并且可以在现场进行开箱即用的部署，通过使用计算机视觉和自监督学习等技术，该方案能够在输入立体对和输出视差映射的最小信息下进行学习和测量置信度度量。实验证明，该方案是首个为任何立体系统实现在线学习准确置信度估计而不需要对最终用户提出任何要求的方案。