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SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking

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arxiv 2502.11534 v1 pith:3H56B3EN submitted 2025-02-17 cs.RO cs.CV

Zijian Wu , Adam Schmidt , Randy Moore , Haoying Zhou , Alexandre Banks , Peter Kazanzides , Septimiu E. Salcudean This is my paper
classification cs.ROcs.CV
keywords surgicalposesurgposedatasetestimationinstrumentkeypointstool
verification ladder T0 review T1 audit T2 compute T3 formal
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Accurate and efficient surgical robotic tool pose estimation is of fundamental significance to downstream applications such as augmented reality (AR) in surgical training and learning-based autonomous manipulation. While significant advancements have been made in pose estimation for humans and animals, it is still a challenge in surgical robotics due to the scarcity of published data. The relatively large absolute error of the da Vinci end effector kinematics and arduous calibration procedure make calibrated kinematics data collection expensive. Driven by this limitation, we collected a dataset, dubbed SurgPose, providing instance-aware semantic keypoints and skeletons for visual surgical tool pose estimation and tracking. By marking keypoints using ultraviolet (UV) reactive paint, which is invisible under white light and fluorescent under UV light, we execute the same trajectory under different lighting conditions to collect raw videos and keypoint annotations, respectively. The SurgPose dataset consists of approximately 120k surgical instrument instances (80k for training and 40k for validation) of 6 categories. Each instrument instance is labeled with 7 semantic keypoints. Since the videos are collected in stereo pairs, the 2D pose can be lifted to 3D based on stereo-matching depth. In addition to releasing the dataset, we test a few baseline approaches to surgical instrument tracking to demonstrate the utility of SurgPose. More details can be found at surgpose.github.io.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training

    cs.CV 2026-01 conditional novelty 6.0 of 10

    RGB-D pre-training with explicit cross-modal objectives (MultiMAE) improves surgical detection, segmentation, pose, and depth estimation over RGB-only pre-training, with gains persisting when fine-tuned on 25% of labe...

  2. On-the-fly hand-eye calibration for the da Vinci surgical robot

    cs.RO 2026-01 conditional novelty 6.0 of 10

    A training-free on-the-fly hand-eye calibration framework using JCBB keypoint association and filter-based estimation reduces tool localization errors for cable-driven surgical robots.

  3. SurfSurg6D: Geometry Consistent Dense Correspondence for Textureless Surgical Instrument Pose Estimation

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    A new synthetic dataset and geometry-consistent dense correspondence framework improve RGB-only pose estimation accuracy for surgical instruments on three evaluation datasets.

  4. Estimating 2D Keypoints of Surgical Tools Using Vision-Language Models with Low-Rank Adaptation

    cs.CV 2025-08 conditional novelty 4.0 of 10

    Giving surgical-tool keypoint detection to Qwen2.5-VL via LoRA fine-tuning reaches MPJPE 0.0627 on SurgeoNet, comparable with or better than dedicated YOLOv8-Pose and SurgeoNet baselines.

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