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 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.
View on arXiv@article{chen2025_2410.04041,
title={ EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion },
author={ Pengcheng Chen and Wenhao Li and Nicole Gunderson and Jeremy Ruthberg and Randall Bly and Zhenglong Sun and Waleed M. Abuzeid and Eric J. Seibel },
journal={arXiv preprint arXiv:2410.04041},
year={ 2025 }
}