技术报告：面向改进型图像引导手术的三维实时重建分而治之方法
Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved IGS
AI总结：
本文提出一种模块化三维实时重建流水线，通过集成帧选择、Depth-Anything V2、EndoDAC 深度估计和改进 ICP 对齐，分而治之解决内窥镜手术追踪难题，并在 Hamlyn 数据集上验证有效性。
摘要：
基于内窥镜视频追踪手术修改在技术上可行且具有显著的临床优势，但仍然充满挑战。本报告提出了一种模块化流水线，以分而治之的方式应对该过程中的临床挑战。该流水线集成了帧选择、深度估计和三维重建组件，从而在纳入新方法时具备灵活性和适应性。报告详细介绍了最新进展，包括集成 Depth-Anything V2 和 EndoDAC 用于深度估计，以及对 Iterative Closest Point (ICP) 对齐过程的改进。在 Hamlyn 数据集上进行的实验证明了所集成方法的有效性。报告同时讨论了系统的能力与局限性。
英文摘要：
Tracking surgical modifications based on endoscopic videos is technically feasible and of great clinical advantages; however, it still remains challenging. This report presents a modular pipeline to divide and conquer the clinical challenges in the process. The pipeline integrates frame selection, depth estimation, and 3D reconstruction components, allowing for flexibility and adaptability in incorporating new methods. Recent advancements, including the integration of Depth-Anything V2 and EndoDAC for depth estimation, as well as improvements in the Iterative Closest Point (ICP) alignment process, are detailed. Experiments conducted on the Hamlyn dataset demonstrate the effectiveness of the integrated methods. System capability and limitations are both discussed.
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。
