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Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy
[chapter]
2018
Lecture Notes in Computer Science
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
doi:10.1007/978-3-030-01201-4_15
fatcat:ce4fy2lr25f3fgdogsiftvqm2i