  Single-view depth estimation can be remarkably effective if there is enough
ground-truth depth data for supervised training. However, there are scenarios,
especially in medicine in the case of endoscopies, where such data cannot be
obtained. In such cases, multi-view self-supervision and synthetic-to-real
transfer serve as alternative approaches, however, with a considerable
performance reduction in comparison to supervised case. Instead, we propose a
single-view self-supervised method that achieves a performance similar to the
supervised case. In some medical devices, such as endoscopes, the camera and
light sources are co-located at a small distance from the target surfaces.
Thus, we can exploit that, for any given albedo and surface orientation, pixel
brightness is inversely proportional to the square of the distance to the
surface, providing a strong single-view self-supervisory signal. In our
experiments, our self-supervised models deliver accuracies comparable to those
of fully supervised ones, while being applicable without depth ground-truth
data.
