  Real-time estimation of actual object depth is an essential module for
various autonomous system tasks such as 3D reconstruction, scene understanding
and condition assessment. During the last decade of machine learning, extensive
deployment of deep learning methods to computer vision tasks has yielded
approaches that succeed in achieving realistic depth synthesis out of a simple
RGB modality. Most of these models are based on paired RGB-depth data and/or
the availability of video sequences and stereo images. The lack of sequences,
stereo data and RGB-depth pairs makes depth estimation a fully unsupervised
single-image transfer problem that has barely been explored so far. This study
builds on recent advances in the field of generative neural networks in order
to establish fully unsupervised single-shot depth estimation. Two generators
for RGB-to-depth and depth-to-RGB transfer are implemented and simultaneously
optimized using the Wasserstein-1 distance, a novel perceptual reconstruction
term and hand-crafted image filters. We comprehensively evaluate the models
using industrial surface depth data as well as the Texas 3D Face Recognition
Database, the CelebAMask-HQ database of human portraits and the SURREAL dataset
that records body depth. For each evaluation dataset the proposed method shows
a significant increase in depth accuracy compared to state-of-the-art
single-image transfer methods.
