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We propose a novel stereo-confidence that can be measured externally tovarious stereo-matching networks, offering an alternative input modality choiceof the cost volume for learning-based approaches, especially in safety-criticalsystems. Grounded in the foundational concepts of disparity definition and thedisparity plane sweep, the proposed stereo-confidence method is built upon theidea that any shift in a stereo-image pair should be updated in a correspondingamount shift in the disparity map. Based on this idea, the proposedstereo-confidence method can be summarized in three folds. 1) Using thedisparity plane sweep, multiple disparity maps can be obtained and treated as a3-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 adesirable (or ideal) disparity profile at every spatial point. 3) By comparingthe desirable and predicted disparity profiles, we can quantify the level ofmatching ambiguity between left and right images for confidence measurement.Extensive experimental results using various stereo-matching networks anddatasets demonstrate that the proposed stereo-confidence method not only showscompetitive performance on its own but also consistent performance improvementswhen it is used as an input modality for learning-based stereo-confidencemethods.

Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep