This work presents a novel approach to monocular 6D pose estimation of surgical instruments in open surgery, addressing challenges such as object articulations, symmetries, occlusions, and lack of annotated real-world data. The method leverages synthetic data generation and domain adaptation techniques to overcome these obstacles. The proposed approach consists of three main components: (1) synthetic data generation using 3D modeling of surgical tools with articulation rigging and physically-based rendering; (2) a tailored pose estimation framework combining object detection with pose estimation and a hybrid geometric fusion strategy; and (3) a training strategy that utilizes both synthetic and real unannotated data, employing domain adaptation on real video data using automatically generated pseudo-labels. Evaluations conducted on videos of open surgery demonstrate the good performance and real-world applicability of the proposed method, 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.

通过使用合成数据生成和域适应技术克服手术工具物体关节、对称性、遮挡和缺乏实际标注数据等挑战，本研究提出了一种新的开放式手术中单目6D姿态估计方法。通过使用三维建模、物理渲染和艺术品设计来生成合成数据，并结合目标检测、姿态估计和混合几何融合策略，采用合成和实际无标注数据的域适应训练策略，该方法展示了良好的性能和实际应用潜力，突出其整合到医疗增强现实和机器人系统中的潜力，并消除了对实际手术数据的广泛手动标注需求。

开放手术中关节手术器械单眼位姿估计