用可微渲染和几何体提升手术机器人位姿估计精度，无需标记点初始化。
Differentiable Rendering-based Pose Estimation for Surgical Robotic Instruments
- 利用可微渲染与圆柱体建模，超越传统关键点方法
- 单次校准即达顶尖性能，实测任务中一致性优秀
- 适合需鲁棒初始化的手术器械实时追踪场景
基于视觉的手术机器人自动化中，位姿估计是一项挑战性且关键的任务。传统校准方法因缆索驱动导致关节角度测量误差，且运动链部分遮挡，难以适用于如 da Vinci Research Kit (dVRK) 等手术机器人。此前工作依赖关键点与 SolvePnP 进行初始化，但该步骤易受噪声影响。本文充分利用可微渲染技术，引入圆柱体等几何体，构建新颖的位姿假设空间下的通用位姿匹配流程。所提单次校准方法在标定一致性和真实手术任务中均达到当前最优表现，验证了无标记初始化在手术器械跟踪中的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Robot pose estimation is a challenging and crucial task for vision-based surgical robotic automation. Typical robotic calibration approaches, however, are not applicable to surgical robots, such as the da Vinci Research Kit (dVRK), due to joint angle measurement errors from cable-drives and the partially visible kinematic chain. Hence, previous works in surgical robotic automation used tracking algorithms to estimate the pose of the surgical tool in real-time and compensate for the joint angle errors. However, a big limitation of these previous tracking works is the initialization step which relied on only keypoints and SolvePnP. In this work, we fully explore the potential of geometric primitives beyond just keypoints with differentiable rendering, cylinders, and construct a versatile pose matching pipeline in a novel pose hypothesis space. We demonstrate the state-of-the-art performance of our single-shot calibration method with both calibration consistency and real surgical tasks. As a result, this marker-less calibration approach proves to be a robust and generalizable initialization step for surgical tool tracking.
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