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

LightDepth: Single-View Depth Self-Supervision from Illumination Decline