Paper ID: 2410.04041 • Published Oct 5, 2024
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion
Pengcheng Chen, Wenhao Li, Nicole Gunderson, Jeremy Ruthberg, Randall Bly, Zhenglong Sun, Waleed M. Abuzeid, Eric J. Seibel
TL;DR
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In endoscopic sinus surgery (ESS), intraoperative CT (iCT) offers valuable
intraoperative assessment but is constrained by slow deployment and radiation
exposure, limiting its clinical utility. Endoscope-based monocular 3D
reconstruction is a promising alternative; however, existing techniques often
struggle to achieve the submillimeter precision required for dense
reconstruction. In this work, we propose an iterative online learning approach
that leverages Neural Radiance Fields (NeRF) as an intermediate representation,
enabling monocular depth estimation and 3D reconstruction without relying on
prior medical data. Our method attains a point-to-point accuracy below 0.5 mm,
with a demonstrated theoretical depth accuracy of 0.125 \pm 0.443 mm. We
validate our approach across synthetic, phantom, and real endoscopic scenarios,
confirming its accuracy and reliability. These results underscore the potential
of our pipeline as an iCT alternative, meeting the demanding submillimeter
accuracy standards required in ESS.