Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As highlighted by recent works concerned with stereo, uncertainty estimation can be a powerful cue to improve accuracy in stereo. Most confidence measures rely on features, mainly extracted from the cost volume, fed to a random forest or a convolutional neural network trained to estimate match uncertainty. In contrast, we propose a novel strategy for confidence estimation based on features computed in the disparity domain, making our proposal suited for any stereo system including COTS devices, and in constant time. We exhaustively assess the performance of our proposals, referred to as O1 and O2, on KITTI and Middlebury datasets with three popular and different stereo algorithms (CENSUS, MC-CNN and SGM), as well as a deep stereo network (PSM-Net). We also evaluate how well confidence measures generalize to different environments/datasets.

深度感测对于无数的应用至关重要，而立体声是为此目的的一种流行，有效且廉价的解决方案。正如有关立体声的最新工作所强调的那样，不确定性估计可能是提高立体声准确性的有力提示。大多数置信度度量取决于主要从成本量中提取的特征，这些特征被馈送到随机森林或经过训练以估计匹配不确定性的卷积神经网络。相比之下，我们提出了一种基于视差域中计算出的特征的置信度估计的新策略，从而使我们的建议适用于恒定时间内包括COTS设备在内的任何立体声系统。我们使用三种流行且不同的立体声算法（CENSUS，MC-CNN和SGM），以及深度立体声网络（PSM-Net）。我们还评估了置信度测度如何推广到不同的环境/数据集。