  We present a self-supervised learning-based pipeline for dense 3D
reconstruction from full-length monocular endoscopic videos without a priori
modeling of anatomy or shading. Our method only relies on unlabeled monocular
endoscopic videos and conventional multi-view stereo algorithms, and requires
neither manual interaction nor patient CT in both training and application
phases. In a cross-patient study using CT scans as groundtruth, we show that
our method is able to produce photo-realistic dense 3D reconstructions with
submillimeter mean residual errors from endoscopic videos from unseen patients
and scopes.
