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EndoUFM framework uses foundation models for improved endoscopic depth estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EndoUFM, a novel unsupervised framework designed to improve depth estimation in endoscopic images. This approach leverages dual foundation models to overcome the domain gap between natural images and surgical environments. The framework incorporates an adaptive fine-tuning strategy using RVLoRA and a Residual block based on Depthwise Separable Convolution (Res-DSC) to enhance local feature capture. Additionally, a mask-guided smoothness loss is implemented to ensure depth consistency within anatomical structures, ultimately aiming to enhance surgical precision and safety. AI

IMPACT Enhances surgical precision and safety by improving spatial perception during minimally invasive procedures.

RANK_REASON The cluster describes a new research paper detailing a novel framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EndoUFM framework uses foundation models for improved endoscopic depth estimation

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The cluster describes a new research paper detailing a novel framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images

    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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