  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.
