  We present a self-supervised approach to training convolutional neural
networks for dense depth estimation from monocular endoscopy data without a
priori modeling of anatomy or shading. Our method only requires sequential data
from monocular endoscopic videos and a multi-view stereo reconstruction method,
e.g. structure from motion, that supervises learning in a sparse but accurate
manner. Consequently, our method requires neither manual interaction, such as
scaling or labeling, nor patient CT in the training and application phases. We
demonstrate the performance of our method on sinus endoscopy data from two
patients and validate depth prediction quantitatively using corresponding
patient CT scans where we found submillimeter residual errors.
