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arXiv Computer Vision By Lucy Fothergill, Pietro Valdastri, Dominic Jones, Duygu Sarikaya

PICO: Projection-Informed Consistency Optimisation for 6DoF Surgical Tool Pose Estimation

Read the original on arXiv Computer Vision →

The paper introduces PICO, an end-to-end trainable model for 6DoF surgical tool pose estimation that uses multi-task learning to predict segmentation, depth, and pose parameters. It incorporates two geometry-aware proxy tasks—a projection loss and a point-to-point loss—to enforce consistency in 2D and 3D spaces, improving accuracy and robustness. Evaluated on the SurgRIPE dataset, PICO achieves strong performance, ranking second in rotation accuracy and maintaining competitive translation results, especially under occlusion.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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