用NeRF融合技术实现内镜手术毫米级3D重建，精度超0.5毫米。
EndoPerfect: High-Accuracy Monocular Depth Estimation and 3D Reconstruction for Endoscopic Surgery via NeRF-Stereo Fusion
- 用NeRF作为中间表示，迭代在线学习实现单目深度估计
- 点对点误差低于0.5毫米，理论深度精度达0.125±0.443毫米
- 无需医疗先验数据，适用于仿真、模型和真实手术场景
在鼻窦内镜手术（ESS）中，术中CT（iCT）虽具重要评估价值，但受限于部署缓慢和辐射暴露，临床应用受限。基于内镜的单目3D重建是可行替代方案，但现有方法难以达到密集重建所需的亚毫米级精度。本文提出一种迭代在线学习方法，利用神经辐射场（NeRF）作为中间表示，实现不依赖医疗先验数据的单目深度估计与3D重建。该方法点对点精度低于0.5毫米，理论深度精度为0.125±0.443毫米。我们在合成数据、模型及真实内镜场景中验证了该方法的准确性和可靠性。结果表明，本方法可作为iCT的有效替代，满足ESS中对亚毫米级精度的严苛要求。
原文摘要 · Abstract (English)
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.
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