Poggi Matteo, Kim Seungryong, Tosi Fabio, Kim Sunok, Aleotti Filippo, Min Dongbo, Sohn Kwanghoon, Mattoccia Stefano
IEEE Trans Pattern Anal Mach Intell. 2022 Sep;44(9):5293-5313. doi: 10.1109/TPAMI.2021.3069706. Epub 2022 Aug 4.
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.
立体匹配是通过找到两幅同步且校正后的图像上匹配像素之间的视差来估计密集深度图的最流行技术之一。随着更精确算法的发展，研究界专注于寻找好的策略来估计估计视差图的可靠性，即置信度。这些信息被证明是一种强大的线索，可用于直观地找出错误匹配，并根据不同策略提高各种立体算法的整体有效性。在本文中，我们回顾了立体匹配置信度估计领域十多年的发展。我们广泛讨论并评估了现有的置信度度量及其变体，从手工制作的到最新的基于学习的最先进方法。我们研究了每种度量应用于不同立体算法集合时的不同行为，并且在文献中首次研究了与最先进的深度立体网络配对时的行为。我们在五个不同的标准数据集上进行的实验提供了该领域的全面概述，特别突出了基于学习的策略的优点和局限性。
