  Relative monocular depth, inferring depth up to shift and scale from a single
image, is an active research topic. Recent deep learning models, trained on
large and varied meta-datasets, now provide excellent performance in the domain
of natural images. However, few datasets exist which provide ground truth depth
for endoscopic images, making training such models from scratch unfeasible.
This work investigates the transfer of these models into the surgical domain,
and presents an effective and simple way to improve on standard supervision
through the use of temporal consistency self-supervision. We show temporal
consistency significantly improves supervised training alone when transferring
to the low-data regime of endoscopy, and outperforms the prevalent
self-supervision technique for this task. In addition we show our method
drastically outperforms the state-of-the-art method from within the domain of
endoscopy. We also release our code, model and ensembled meta-dataset,
Meta-MED, establishing a strong benchmark for future work.
