Meta-confidence estimation for stereo matching

We propose a novel framework to estimate the confidence of a disparity map taking into account, for the first time, the uncertainty affecting the confidence estimation process itself. Conversely to other tasks such as disparity estimation, the uncertainty of confidence directly hints that the confidence should be increased if initially low, but with high uncertainty, decreased otherwise. By modelling such a cue in the form of a second-level confidence, or meta-confidence, our solution allows for finding incorrect predictions inferred by confidence estimator and for learning a correction for them. Our strategy is suited for any state-of-the-art method known in literature, either implemented using random forest classifiers or deep neural networks. Especially, for deep neural networksbased models, we present a multi-headed confidence estimator followed by an uncertainty network, so as to predict mean confidence and meta-confidence within a single network without the cost of lower accuracy, a known limitation in literature for uncertainty estimation. Experimental results on a variety of stereo algorithms and confidence estimation models prove that the modeled meta-confidence is meaningful of the reliability of the estimated confidence and allows for refining it.