A stereo confidence metric using single view imagery with comparison to five alternative approaches

Although stereo vision research has progressed remarkably, stereo systems still need a fast, accurate way to estimate confidence in their output. In the current paper, we explore using stereo performance on two different images from a single view as a confidence measure for a binocular stereo system incorporating that single view. Although it seems counterintuitive to search for correspondence in two different images from the same view, such a search gives us precise quantitative performance data. Correspondences significantly far from the same location are erroneous because there is little to no motion between the two images. Using hand-generated ground truth, we quantitatively compare this new confidence metric with five commonly used confidence metrics. We explore the performance characteristics of each metric under a variety of conditions.