  Estimating a scene reconstruction and the camera motion from in-body videos
is challenging due to several factors, e.g. the deformation of in-body cavities
or the lack of texture. In this paper we present Endo-Depth-and-Motion, a
pipeline that estimates the 6-degrees-of-freedom camera pose and dense 3D scene
models from monocular endoscopic videos. Our approach leverages recent advances
in self-supervised depth networks to generate pseudo-RGBD frames, then tracks
the camera pose using photometric residuals and fuses the registered depth maps
in a volumetric representation. We present an extensive experimental evaluation
in the public dataset Hamlyn, showing high-quality results and comparisons
against relevant baselines. We also release all models and code for future
comparisons.
