NeRFscopy：基于神经辐射场的内窥镜时间变化组织的3D重建
NeRFscopy: Neural Radiance Fields for in-vivo Time-Varying Tissues from Endoscopy
AI总结：
NeRFscopy通过神经辐射场技术，从单目内窥镜视频中实现可变形组织的3D重建和新视角合成，无需模板或预训练模型。
摘要：
内窥镜在医学影像中至关重要，用于诊断、预后和治疗。开发一个稳健的动态3D重建管道来处理内窥镜视频可以增强可视化，提高诊断准确性，协助治疗计划，并指导手术程序。然而，由于组织的可变形性、单目相机的使用、光照变化、遮挡和未知的相机轨迹，存在挑战。受神经渲染启发，我们引入了NeRFscopy，一种自监督的管道，用于从单目视频中合成新视角并重建可变形内窥镜组织的3D结构。NeRFscopy包括一个具有标准辐射场的可变形模型和一个由SE(3)变换参数化的时依赖变形场。此外，颜色图像通过引入复杂的术语来利用，以学习一个3D隐式模型，不假设任何模板或预训练模型，仅从数据中学习。NeRFscopy在新视角合成方面实现了准确的结果，在各种具有挑战性的内窥镜场景中优于竞争方法。
英文摘要：
Endoscopy is essential in medical imaging, used for diagnosis, prognosis and treatment. Developing a robust dynamic 3D reconstruction pipeline for endoscopic videos could enhance visualization, improve diagnostic accuracy, aid in treatment planning, and guide surgery procedures. However, challenges arise due to the deformable nature of the tissues, the use of monocular cameras, illumination changes, occlusions and unknown camera trajectories. Inspired by neural rendering, we introduce NeRFscopy, a self-supervised pipeline for novel view synthesis and 3D reconstruction of deformable endoscopic tissues from a monocular video. NeRFscopy includes a deformable model with a canonical radiance field and a time-dependent deformation field parameterized by SE(3) transformations. In addition, the color images are efficiently exploited by introducing sophisticated terms to learn a 3D implicit model without assuming any template or pre-trained model, solely from data. NeRFscopy achieves accurate results in terms of novel view synthesis, outperforming competing methods across various challenging endoscopy scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
