  Convolutional Neural Networks (CNNs) need large amounts of data with ground
truth annotation, which is a challenging problem that has limited the
development and fast deployment of CNNs for many computer vision tasks. We
propose a novel framework for depth estimation from monocular images with
corresponding confidence in a self-supervised manner. A fully differential
patch-based cost function is proposed by using the Zero-Mean Normalized Cross
Correlation (ZNCC) that takes multi-scale patches as a matching strategy. This
approach greatly increases the accuracy and robustness of the depth learning.
In addition, the proposed patch-based cost function can provide a 0 to 1
confidence, which is then used to supervise the training of a parallel network
for confidence map learning and estimation. Evaluation on KITTI dataset shows
that our method outperforms the state-of-the-art results.
