  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.
