We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods.

翻译：摘要：我们提出了一种新颖的立体置信度方法，该方法可在各种立体匹配网络外部进行测量，为基于学习的方法（尤其在安全关键系统中）提供了成本体积的替代输入模态选择。基于视差定义与视差平面扫描的基本概念，所提出的立体置信度方法建立在以下核心理念上：立体图像对中的任何位移，都应体现在视差图的对应位移量更新中。基于此，该方法可归纳为三个要点：1）通过视差平面扫描，可获取多幅视差图并将其视为三维体积（预测视差体积），类似于成本体积的构建方式；2）以其中一幅视差图为锚点，可在每个空间点定义期望（或理想）视差轮廓；3）通过比较期望视差轮廓与预测视差轮廓，可量化左右图像间的匹配歧义程度，从而实现置信度测量。在多种立体匹配网络与数据集上的大量实验结果表明，所提出的立体置信度方法不仅自身展现出竞争性能，而且在将其作为基于学习的立体置信度方法的输入模态时，能带来持续的性能提升。