基于内镜视频实现3D实时重建，提升手术导航精度
Tech Report: Divide and Conquer 3D Real-Time Reconstruction for Improved IGS
- 分模块设计流程，支持新方法灵活接入
- 结合Depth-Anything V2与EndoDAC提升深度估计性能
- 适用于需高精度术中3D重建的外科导航场景
基于内镜视频追踪手术变化在技术上可行且具有重要临床价值，但仍面临挑战。本报告提出一种模块化流水线，通过分解任务应对临床难题。该流程整合帧选择、深度估计与3D重建组件，具备良好灵活性与可扩展性。近期进展包括引入Depth-Anything V2和EndoDAC进行深度估计，以及优化迭代最近点（ICP）对齐过程。在Hamlyn数据集上的实验验证了集成方法的有效性。同时讨论了系统能力与局限。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。