单目手术工具姿态估计——在真实环境中
Monocular pose estimation of articulated open surgery tools -- in the wild
AI总结：
本文提出了一种基于合成数据和真实数据的单目手术工具姿态估计框架，通过域适应和伪标签提升实际应用性能。
摘要：
本文提出了一种用于开放手术中手术器械单目6D姿态估计的框架，针对物体关节运动、镜面反射、遮挡以及合成到真实域适应等挑战。所提出的方法包含三个主要组成部分：(1) 一个合成数据生成流程，该流程结合了手术工具的3D扫描，带有关节 rigging 和基于物理的渲染；(2) 一个定制的姿态估计框架，结合了工具检测与姿态和关节估计；(3) 在合成和未标注的真实视频数据上进行训练的策略，采用域适应与自动产生的伪标签。在真实数据上的评估显示了所提框架的良好性能和实际应用价值，突显了其在医学增强现实和机器人系统中的潜在应用。该方法消除了对大量真实手术数据手动标注的需求。
英文摘要：
This work presents a framework for monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, specularity, occlusions, and synthetic-to-real domain adaptation. The proposed approach consists of three main components: $(1)$ synthetic data generation pipeline that incorporates 3D scanning of surgical tools with articulation rigging and physically-based rendering; $(2)$ a tailored pose estimation framework combining tool detection with pose and articulation estimation; and $(3)$ a training strategy on synthetic and real unannotated video data, employing domain adaptation with automatically generated pseudo-labels. Evaluations conducted on real data of open surgery demonstrate the good performance and real-world applicability of the proposed framework, highlighting its potential for integration into medical augmented reality and robotic systems. The approach eliminates the need for extensive manual annotation of real surgical data.
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。
