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.

本文回顾了十多年来用于立体匹配置信度估计的算法和策略，并评估了现有的置信度估计方法，包括手工设计的和最新的基于学习的方法，在五个标准数据集上进行实验，并首次将一个最先进的深度立体网络与置信度测量技术配对进行比较，旨在提供该领域的全面概述，并突出了基于学习的方法的优势和局限性。

深度学习时代的立体匹配置信度：定量评估