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arXiv · 1909.03101

Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video

Xingtong Liu·Ayushi Sinha·Masaru Ishii·Gregory D. Hager·Russell H. Taylor·Mathias Unberath
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Abstract

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.

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Xingtong Liu, Ayushi Sinha, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath. 2019-09-06. Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video. https://arxiv.org/abs/1909.03101

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