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

Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints