A Multi-View Pipeline and Benchmark Dataset for 3D Hand Pose Estimation in Surgery

• URL: http://arxiv.org/abs/2601.15918v1
• Date: Thu, 22 Jan 2026 12:48:24 GMT
• Title: A Multi-View Pipeline and Benchmark Dataset for 3D Hand Pose Estimation in Surgery
• Authors: Valery Fischer, Alan Magdaleno, Anna-Katharina Calek, Nicola Cavalcanti, Nathan Hoffman, Christoph Germann, Joschua Wüthrich, Max Krähenmann, Mazda Farshad, Philipp Fürnstahl, Lilian Calvet,
• Abstract summary: We propose a robust pipeline for 3D hand pose estimation in surgical contexts.<n>The pipeline integrates reliable person detection, whole-body pose estimation, and state-of-the-art 2D hand keypoint prediction.<n>We introduce a novel surgical benchmark dataset comprising over 68,000 frames and 3,000 manually annotated 2D hand poses.
• Score: 1.120882117110929
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: Accurate 3D hand pose estimation supports surgical applications such as skill assessment, robot-assisted interventions, and geometry-aware workflow analysis. However, surgical environments pose severe challenges, including intense and localized lighting, frequent occlusions by instruments or staff, and uniform hand appearance due to gloves, combined with a scarcity of annotated datasets for reliable model training. Method: We propose a robust multi-view pipeline for 3D hand pose estimation in surgical contexts that requires no domain-specific fine-tuning and relies solely on off-the-shelf pretrained models. The pipeline integrates reliable person detection, whole-body pose estimation, and state-of-the-art 2D hand keypoint prediction on tracked hand crops, followed by a constrained 3D optimization. In addition, we introduce a novel surgical benchmark dataset comprising over 68,000 frames and 3,000 manually annotated 2D hand poses with triangulated 3D ground truth, recorded in a replica operating room under varying levels of scene complexity. Results: Quantitative experiments demonstrate that our method consistently outperforms baselines, achieving a 31% reduction in 2D mean joint error and a 76% reduction in 3D mean per-joint position error. Conclusion: Our work establishes a strong baseline for 3D hand pose estimation in surgery, providing both a training-free pipeline and a comprehensive annotated dataset to facilitate future research in surgical computer vision.

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