Evaluation of stereo confidence measures on synthetic and recorded image data

We comparatively discuss a set of confidence measures for stereo analysis by testing them on semi-global matching (SGM) cost functions. The aim is a prediction of (potentially) erroneous areas in calculated disparity maps. The evaluation is done by using the sparsification technique which provides more information than commonly used RMS or NCC measures. We also present an approach for combining different confidence measures. This allows us to perform a quantisation of confidence estimates in terms of disparity errors.