# Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation Source: [Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation]() Collection index: [llms.txt]() Data updated: 2026-01-02T13:04:27.514Z Authors: Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga, Wolfgang Wein, Nassir Navab, Federico Tombari, Alexander Ladikos Published: 2021-09-24T08:11:34.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Depth Estimation on pulmonary phantom dataset (test)](): Depth Estimation; pulmonary phantom dataset (test); Mean Abs Rel Diff: 0.379. ## Original sources and implementation [Original paper]() [PDF]()