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arXiv · 2512.05529

See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors

Kunyi Yang·Qingyu Wang·Cheng Yuan·Yutong Ban
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Abstract

Pixel-wise segmentation of laparoscopic scenes is essential for computer-assisted surgery but difficult to scale due to the high cost of dense annotations. We propose depth-guided surgical scene segmentation (DepSeg), a training-free framework that utilizes monocular depth as a geometric prior together with pretrained vision foundation models. DepSeg first estimates a relative depth map with a pretrained monocular depth estimation network and proposes depth-guided point prompts, which SAM2 converts into class-agnostic masks. Each mask is then described by a pooled pretrained visual feature and classified via template matching against a template bank built from annotated frames. On the CholecSeg8k dataset, DepSeg improves over a direct SAM2 auto segmentation baseline (35.9% vs. 14.7% mIoU) and maintains competitive performance even when using only 10--20% of the object templates. These results show that depth-guided prompting and template-based classification offer an annotation-efficient segmentation approach.

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Kunyi Yang, Qingyu Wang, Cheng Yuan, Yutong Ban. 2025-12-05. See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors. https://arxiv.org/abs/2512.05529

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