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arXiv Computer Vision By Chenyan Jing, Hao Ding, Lalithkumar Seenivasan, Jacob M. Delgado L\'opez, Mathias Unberath

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos

Read the original on arXiv Computer Vision →

The paper presents a method for localizing functional surgical landmarks—specifically instrument tips and anchors—in surgical videos without requiring manual pixel-level mask annotations. It leverages vision foundation models, such as SAM 3, to generate dense structural priors through zero‑shot, point‑prompted masks, and refines landmark predictions with a lightweight, coarse‑to‑fine multi‑frame network. Experiments on 7,867 clips from 60 videos show that the approach achieves F1 scores of 72.4% for tip and 58.0% for anchor localization, with ablations confirming the benefits of structural priors and refinement stages.

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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