  Stereo matching is one of the most popular techniques to estimate dense depth
maps by finding the disparity between matching pixels on two, synchronized and
rectified images. Alongside with the development of more accurate algorithms,
the research community focused on finding good strategies to estimate the
reliability, i.e. the confidence, of estimated disparity maps. This information
proves to be a powerful cue to naively find wrong matches as well as to improve
the overall effectiveness of a variety of stereo algorithms according to
different strategies. In this paper, we review more than ten years of
developments in the field of confidence estimation for stereo matching. We
extensively discuss and evaluate existing confidence measures and their
variants, from hand-crafted ones to the most recent, state-of-the-art learning
based methods. We study the different behaviors of each measure when applied to
a pool of different stereo algorithms and, for the first time in literature,
when paired with a state-of-the-art deep stereo network. Our experiments,
carried out on five different standard datasets, provide a comprehensive
overview of the field, highlighting in particular both strengths and
limitations of learning-based strategies.
