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About this paper
Valery Fischer, Alan Magdaleno, Anna-Katharina Calek, Nicola Cavalcanti, Nathan Hoffman, Christoph Germann, Joschua Wüthrich, Max Krähenmann and 3 more
A robust multi-view pipeline for 3D hand pose estimation in surgical environments was introduced, demonstrating an 8.5 mm Mean Per-Joint Position Error on a novel 68,000-frame benchmark dataset. The method addresses issues like intense lighting, frequent occlusions, and uniform gloved hand appearance prevalent in operating rooms.
Key insights
- Combining robust, object-agnostic tracking with a specialized 2D hand pose estimator dramatically improves hand localization and keypoint accuracy in visually challenging surgical environments.
- Constrained 3D optimization, particularly with temporal smoothness and shape consistency terms, is effective in achieving highly accurate and temporally coherent 3D hand trajectories from noisy 2D inputs.
- Biomechanical loss terms, while intuitively useful, did not improve performance in this specific multi-view pipeline, possibly due to conflicts with optimizing for reprojection fidelity.
Summary by alphaXiv.
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