Deep Triple-Supervision Learning Unannotated Surgical Endoscopic Video Data for Monocular Dense Depth Estimation

Surface reconstruction is an essential way to expand surgical field of view during endoscopic surgery, but it certainly requires dense depth estimation of endoscopic video sequences. Unfortunately, such a dense depth recovery suffers from illumination variation, weak texture, and occlusion. To address these problems, this work proposes a new triple-supervision self-learning strategy that uses unannotated endoscopic video data to predict monocular endoscopic dense depth information. This strategy first employs an effective conventional method to estimate camera poses and sparse depth maps to establishing a sparse data self-supervision. Furthermore, our strategy still combines two consistency measures to supervise dense depth and photometric information. We evaluated our method on collected colonoscopic videos, with the experimental results showing that our triple-supervision learning framework works more effective and accurate than some current self-supervised and unsupervised learning methods.