  Real-time estimation of actual environment depth is an essential module for
various autonomous system tasks such as localization, obstacle detection and
pose estimation. During the last decade of machine learning, extensive
deployment of deep learning methods to computer vision tasks yielded successful
approaches for realistic depth synthesis out of a simple RGB modality. While
most of these models rest on paired depth data or availability of video
sequences and stereo images, there is a lack of methods facing single-image
depth synthesis in an unsupervised manner. Therefore, in this study, latest
advancements in the field of generative neural networks are leveraged to fully
unsupervised single-image depth synthesis. To be more exact, two
cycle-consistent generators for RGB-to-depth and depth-to-RGB transfer are
implemented and simultaneously optimized using the Wasserstein-1 distance. To
ensure plausibility of the proposed method, we apply the models to a self
acquised industrial data set as well as to the renown NYU Depth v2 data set,
which allows comparison with existing approaches. The observed success in this
study suggests high potential for unpaired single-image depth estimation in
real world applications.
