  State-of-the-art approaches to infer dense depth measurements from images
rely on CNNs trained end-to-end on a vast amount of data. However, these
approaches suffer a drastic drop in accuracy when dealing with environments
much different in appearance and/or context from those observed at training
time. This domain shift issue is usually addressed by fine-tuning on smaller
sets of images from the target domain annotated with depth labels.
Unfortunately, relying on such supervised labeling is seldom feasible in most
practical settings. Therefore, we propose an unsupervised domain adaptation
technique which does not require groundtruth labels. Our method relies only on
image pairs and leverages on classical stereo algorithms to produce disparity
measurements alongside with confidence estimators to assess upon their
reliability. We propose to fine-tune both depth-from-stereo as well as
depth-from-mono architectures by a novel confidence-guided loss function that
handles the measured disparities as noisy labels weighted according to the
estimated confidence. Extensive experimental results based on standard datasets
and evaluation protocols prove that our technique can address effectively the
domain shift issue with both stereo and monocular depth prediction
architectures and outperforms other state-of-the-art unsupervised loss
functions that may be alternatively deployed to pursue domain adaptation.
