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English(EN) EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images

EndoUFM框架利用基础模型改进内窥镜深度估计

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了EndoUFM，一个新颖的无监督框架，旨在改进内窥镜图像中的深度估计。该方法利用双基础模型来弥合自然图像与手术环境之间的领域差距。该框架采用RVLoRA的自适应微调策略和基于深度可分离卷积的残差块（Res-DSC）来增强局部特征捕获。此外，还实现了掩码引导的平滑度损失，以确保解剖结构内的深度一致性，最终目标是提高手术精度和安全性。 AI

影响 通过改善微创手术中的空间感知来提高手术精度和安全性。

排序理由 该集群描述了一篇关于特定AI任务新颖框架的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

EndoUFM框架利用基础模型改进内窥镜深度估计

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xinning Yao, Bo Liu, Bojian Li, Jingjing Wang, Jinghua Yue, Fugen Zhou ·

    EndoUFM：利用基础模型进行内窥镜图像的单目深度估计

    arXiv:2508.17916v2 Announce Type: replace Abstract: Depth estimation is a foundational component for 3D reconstruction in minimally invasive endoscopic surgeries. However, existing monocular depth estimation techniques often exhibit limited performance to the varying illumination…

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