Live Thread Reconstruction for Autonomous Manipulation

Suture-needle pickup is necessary for autonomous suturing, as a needle can be unexpectedly dropped or strategically released to adjust the grasping configuration. Current methods for autonomous needle pickup typically guide a robot to move straight toward the needle and grasp it, limited to conditions where the needle is observable and directly approachable. In addition, grasping the needle lying on tissue can lead to the robot pinching nearby tissue or the needle jumping around due to its slippery surface, posing potential safety issues. This work proposes an autonomous framework that uses a suture thread as an assistive tool for indirect needle pickup, avoiding unnecessary tool-tissue contact and enabling pickup even when the needle is occluded or inaccessible. The framework spans the entire workflow, including thread and tissue reconstruction, safe grasp-point selection, stable thread lifting, and bimanual thread-following until securing needle grasping. The robot policies account for visual uncertainty to maximize robustness in real-world environments. We evaluate the proposed framework on a da Vinci Research Kit under various real-world conditions. The results demonstrate robust performance even with a challenging thread configuration or a non-approachable needle, closing the gap in applying autonomous robot policies to unstructured suturing environments.

This paper presents a video-to-model framework for automatically modeling the motion of a deformable suture thread from an input video. We utilize a recently developed CBF--CLF--QP numerical model that simplifies the characterization of deformable string motion through the selection of a small number of parameters. A perception module first localizes and tracks the thread in video, producing an ordered sequence of thread nodes. The observed thread motion is then processed by a spatio-temporal CNN network that estimates the effective parameters of a structured CBF--CLF--QP model. These parameters are used to simulate the thread under a user-defined needle velocity input. Experiments using unseen thread configurations and motion demonstrate that the framework can reliably reconstruct the thread behavior from video, automatically configure the structured model, and reproduce the expected thread motion with low tracking error. The proposed approach reduces the need for manual parameter tuning and provides a step toward automatic video-based modeling of deformable linear objects.

Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are offline and depend on accurate camera trajectory priors (often from robotic kinematics), limiting applicability when priors are missing or noisy. To address these limitations, we propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D deformable scene representation directly from surgical video. Track2Map is therefore capable of robust 3D reconstructions when camera trajectory priors are either absent or noisy, and due to its online nature it effectively works as a Simultaneous Localisation and Mapping (SLAM) method. To stabilize optimization in the presence of tissue motion and ambiguous visual cues, we introduce a track-anchored deformation initialization using dense 2D point tracks. Track statistics are further utilized to disentangle camera motion from scene deformation by detecting static camera periods and reducing drift during incremental mapping. Experiments on StereoMIS show improved reconstruction quality and camera trajectory against competing SLAM methods, as well as compared to non-SLAM methods that utilize camera trajectory priors. The code is available at https://track2map.github.io/.