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SurgCalib, a novel markerless hand-eye calibration framework, leverages Gaussian Splatting and differentiable rendering to address the challenges of inaccurate proprioceptive measurements in cable-driven surgical robots like the da Vinci. It initializes the surgical instrument pose using raw kinematics and refines it through a two-phase optimization under the Remote Center of Motion (RCM) constraint. Experiments on the dVRK benchmark demonstrate that SurgCalib achieves average 2D tool-tip reprojection errors of ~2mm and 3D tool-tip Euclidean distance errors of ~5mm, showcasing its potential for accurate, markerless calibration in operating rooms.
Ditch the fiducial markers: SurgCalib achieves sub-5mm 3D tool-tip error in surgical robot hand-eye calibration using Gaussian Splatting, opening the door to sterile, markerless OR workflows.
We present a Gaussian Splatting-based framework for hand-eye calibration of the da Vinci surgical robot. In a vision-guided robotic system, accurate estimation of the rigid transformation between the robot base and the camera frame is essential for reliable closed-loop control. For cable-driven surgical robots, this task faces unique challenges. The encoders of surgical instruments often produce inaccurate proprioceptive measurements due to cable stretch and backlash. Conventional hand-eye calibration approaches typically rely on known fiducial patterns and solve the AX = XB formulation. While effective, introducing additional markers into the operating room (OR) environment can violate sterility protocols and disrupt surgical workflows. In this study, we propose SurgCalib, an automatic, markerless framework that has the potential to be used in the OR. SurgCalib first initializes the pose of the surgical instrument using raw kinematic measurements and subsequently refines this pose through a two-phase optimization procedure under the RCM constraint within a Gaussian Splatting-based differentiable rendering pipeline. We evaluate the proposed method on the public dVRK benchmark, SurgPose. The results demonstrate average 2D tool-tip reprojection errors of 12.24 px (2.06 mm) and 11.33 px (1.9 mm), and 3D tool-tip Euclidean distance errors of 5.98 mm and 4.75 mm, for the left and right instruments, respectively.
