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.

本文介绍了一种无监督领域适应技术，它仅依赖于图像对，并利用经典的立体算法生成视差测量，旨在 fine-tune 立体和单眼深度架构，通过一种新颖的置信度引导损失函数来处理以测量出视差值，并且该方法不需要基础真实标签，实验证明其效果优于其他同类无监督损失函数。