State-of-the-art research of traditional computer vision is increasingly
leveraged in the surgical domain. A particular focus in computer-assisted
surgery is to replace marker-based tracking systems for instrument localization
with pure image-based 6DoF pose estimation. However, the state of the art has
not yet met the accuracy required for surgical navigation. In this context, we
propose a high-fidelity marker-less optical tracking system for surgical
instrument localization. We developed a multi-view camera setup consisting of
static and mobile cameras and collected a large-scale RGB-D video dataset with
dedicated synchronization and data fusions methods. Different state-of-the-art
pose estimation methods were integrated into a deep learning pipeline and
evaluated on multiple camera configurations. Furthermore, the performance
impacts of different input modalities and camera positions, as well as training
on purely synthetic data, were compared. The best model achieved an average
position and orientation error of 1.3 mm and 1.0{\deg} for a surgical drill as
well as 3.8 mm and 5.2{\deg} for a screwdriver. These results significantly
outperform related methods in the literature and are close to clinical-grade
accuracy, demonstrating that marker-less tracking of surgical instruments is
becoming a feasible alternative to existing marker-based systems.

提出了一种高保真度的非标记光学跟踪系统，用于手术器械的本地化，采用多视角相机布置，利用深度学习方法和数据融合技术，对不同输入模型和相机位置进行评估和比较，并且取得了比文献中相关方法更好的平均误差结果，从而展示了手术器械无标记跟踪已经成为了现实。