  Depth estimation from monocular images is an important task in localization
and 3D reconstruction pipelines for bronchoscopic navigation. Various
supervised and self-supervised deep learning-based approaches have proven
themselves on this task for natural images. However, the lack of labeled data
and the bronchial tissue's feature-scarce texture make the utilization of these
methods ineffective on bronchoscopic scenes. In this work, we propose an
alternative domain-adaptive approach. Our novel two-step structure first trains
a depth estimation network with labeled synthetic images in a supervised
manner; then adopts an unsupervised adversarial domain feature adaptation
scheme to improve the performance on real images. The results of our
experiments show that the proposed method improves the network's performance on
real images by a considerable margin and can be employed in 3D reconstruction
pipelines.
