AI 中文总结
研究针对手术机器人基于标记跟踪易遮挡问题，通过扩展roboreg框架，采用顺序优化和CUDA流并行化等方法，实现无标记实时跟踪，性能优于FoundationPose，推理更快，达到实时高分辨率跟踪，与基于标记方法相当且超越基础模型基线。
AI 中文摘要
目的：基于标记的手术机器人跟踪在杂乱的手术室中容易被遮挡。我们评估立体可微渲染用于无标记、实时机器人姿态跟踪，这可能提高安全性、减少设置时间并实现多机器人交互。方法：我们通过（i）顺序优化将无标记姿态估计框架roboreg扩展到在线动态跟踪，该优化通过运动自适应超参数调整在帧间传播姿态估计，以及（ii）分割和优化的CUDA流并行化，结合CUDA图加速分割。我们在38个无遮挡和5个有遮挡的位移序列上进行评估，具有静态起始/结束地面真值校准和基于动态标记的参考跟踪。结果：我们实现了30fps的实时1080p跟踪（从普通roboreg的14fps提高），与相机帧率匹配。相对于静态地面真值，精度达到1.7厘米/0.6度；相对于基于标记的参考，在27460帧上平均3D误差为1.2厘米（在1242个遮挡帧上为1.53厘米）。我们的方法在动态估计中比FoundationPose性能优11%（在遮挡下优63%），在静态估计中优250%，推理速度快6倍。结论：立体可微渲染实现了实时、高分辨率的无标记手术机器人跟踪，与基于标记的方法相当且超越基础模型基线。
英文摘要
Purpose: Marker-based tracking of surgical robots is occlusion-prone in cluttered operating rooms. We evaluate stereo differentiable rendering for marker-free, real-time robot pose tracking, potentially improving safety, reducing setup time, and enabling multi-robot interaction. Methods: We extend the markerless pose estimation framework roboreg to online dynamic tracking via (i) sequential optimisation that propagates pose estimates across frames with motion-adaptive hyperparameter tuning, and (ii) CUDA stream parallelisation of segmentation and optimisation, combined with CUDA-graph accelerated segmentation. We evaluate on 38 unobstructed and 5 occluded displacement sequences with static start/end ground-truth calibrations and dynamic marker-based reference tracking. Results: We achieve real-time 1080p tracking at 30 fps (up from 14 fps for vanilla roboreg), matching the camera frame rate. Accuracy reaches 1.7 cm / 0.6 deg against static ground truth and 1.2 cm mean 3D error over 27,460 frames against the marker-based reference (1.53 cm over 1,242 occluded frames). Our method outperforms FoundationPose by 11% in dynamic estimation (63% under occlusion) and 250% in static estimation, with 6x faster inference. Conclusions: Stereo differentiable rendering enables real-time, high-resolution marker-free surgical robot tracking, on par with marker-based approaches and surpassing foundation-model baselines.
Journal refInt J CARS (2026)
DOI：10.1007/s11548-026-03730-z
