  Motivated by the need to identify erroneous disparity assignments, various
approaches for uncertainty and confidence estimation of dense stereo matching
have been presented in recent years. As in many other fields, especially deep
learning based methods have shown convincing results. However, most of these
methods only model the uncertainty contained in the data, while ignoring the
uncertainty of the employed dense stereo matching procedure. Additionally
modelling the latter, however, is particularly beneficial if the domain of the
training data varies from that of the data to be processed. For this purpose,
in the present work the idea of probabilistic deep learning is applied to the
task of dense stereo matching for the first time. Based on the well-known and
commonly employed GC-Net architecture, a novel probabilistic neural network is
presented, for the task of joint depth and uncertainty estimation from epipolar
rectified stereo image pairs. Instead of learning the network parameters
directly, the proposed probabilistic neural network learns a probability
distribution from which parameters are sampled for every prediction. The
variations between multiple such predictions on the same image pair allow to
approximate the model uncertainty. The quality of the estimated depth and
uncertainty information is assessed in an extensive evaluation on three
different datasets.
