  The focus in deep learning research has been mostly to push the limits of
prediction accuracy. However, this was often achieved at the cost of increased
complexity, raising concerns about the interpretability and the reliability of
deep networks. Recently, an increasing attention has been given to untangling
the complexity of deep networks and quantifying their uncertainty for different
computer vision tasks. Differently, the task of depth completion has not
received enough attention despite the inherent noisy nature of depth sensors.
In this work, we thus focus on modeling the uncertainty of depth data in depth
completion starting from the sparse noisy input all the way to the final
prediction.
  We propose a novel approach to identify disturbed measurements in the input
by learning an input confidence estimator in a self-supervised manner based on
the normalized convolutional neural networks (NCNNs). Further, we propose a
probabilistic version of NCNNs that produces a statistically meaningful
uncertainty measure for the final prediction. When we evaluate our approach on
the KITTI dataset for depth completion, we outperform all the existing Bayesian
Deep Learning approaches in terms of prediction accuracy, quality of the
uncertainty measure, and the computational efficiency. Moreover, our small
network with 670k parameters performs on-par with conventional approaches with
millions of parameters. These results give strong evidence that separating the
network into parallel uncertainty and prediction streams leads to
state-of-the-art performance with accurate uncertainty estimates.
