Reliable estimation of surgical needle 3D position and orientation is essential for autonomous robotic suturing, yet existing methods operate almost exclusively under stereoscopic vision. In monocular endoscopic settings, common in transendoscopic and intraluminal procedures, depth ambiguity and rotational symmetry render needle pose estimation inherently ill-posed, producing a multimodal distribution over feasible configurations, rather than a single, well-grounded estimate. We present PinPoint, a probabilistic variational inference framework that treats this ambiguity directly, maintaining a distribution of pose hypotheses rather than suppressing it. PinPoint combines monocular image observations with robot-grasp constraints through analytical geometric likelihoods with closed-form Jacobians. This framework enables efficient Gauss-Newton preconditioning in a Stein Variational Newton inference, where second-order particle transport deterministically moves particles toward high-probability regions while kernel-based repulsion preserves diversity in the multimodal structure. On real needle-tracking sequences, PinPoint reduces mean translational error by 80% (down to 1.00 mm) and rotational error by 78% (down to 13.80°) relative to a particle-filter baseline, with substantially better-calibrated uncertainty. On induced-rotation sequences, where monocular ambiguity is most severe, PinPoint maintains a bimodal posterior 84% of the time, almost three times the rate of the particle filter baseline, correctly preserving the alternative hypothesis rather than committing prematurely to one mode. Suturing experiments in ex vivo tissue demonstrate stable tracking through intermittent occlusion, with average errors during occlusion of 1.34 mm in translation and 19.18° in rotation, even when the needle is fully embedded.

翻译：手术针三维位置与方向的可靠估计对自主机器人缝合至关重要，但现有方法几乎仅适用于立体视觉环境。在经内镜及管腔内手术常见的单目内窥镜场景中，深度模糊性与旋转对称性使得针位姿估计本质上是病态问题——其输出并非单一可靠估计，而是关于可行构型的多模态分布。本文提出PinPoint，一种直接处理该模糊性的概率变分推理框架，其保留位姿假设的分布而非将其压制。PinPoint通过具有闭合形式雅可比矩阵的解析几何似然，将单目图像观测与机器人抓取约束相结合。该框架支持在Stein变分牛顿推理中实现高效高斯-牛顿预条件，其中二阶粒子输运机制确保粒子确定性向高概率区域迁移，而基于核函数的排斥机制则维持多模态结构中的多样性。在真实针跟踪序列上，相比粒子滤波基线，PinPoint将平均平移误差降低80%（降至1.00毫米）、旋转误差降低78%（降至13.80°），且不确定性校准显著更优。在单目模糊性最严重的诱导旋转序列中，PinPoint在84%的时间保持后验双峰分布，几乎为粒子滤波基线的三倍，正确保留了替代假设而未过早收敛至单一模态。离体组织缝合实验表明，当针完全嵌入时，该方法仍能在间歇性遮挡条件下实现稳定跟踪，遮挡期间的平均平移误差为1.34毫米、旋转误差为19.18°。