EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth
Estimation from Any Endoscopic Camera

EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera

14 May 2024

Mobarakol Islam

Hongliang Ren

Papers citing "EndoDAC: Efficient Adapting Foundation Model for Self-Supervised Depth Estimation from Any Endoscopic Camera"

7 / 7 papers shown

Title
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion Mona Sheikh Zeinoddin Mobarakol Islam Zafer Tandogdu Greg Shaw Mathew J. Clarkson E. Mazomenos Danail Stoyanov 61 0 0 10 Mar 2025
NFL-BA: Improving Endoscopic SLAM with Near-Field Light Bundle Adjustment Andrea Dunn Beltran Daniel Rho Marc Niethammer Roni Sengupta Roni Sengupta 86 2 0 17 Dec 2024
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy Bojian Li Bo Liu Jinghua Yue F. Zhou Fugen Zhou MedIm MDE 42 2 0 12 Sep 2024
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels Qingyao Tian Zhen Chen Huai Liao Xinyan Huang Lujie Li Sebastien Ourselin Hongbin Liu 53 1 0 09 Sep 2024
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data Lihe Yang Bingyi Kang Zilong Huang Xiaogang Xu Jiashi Feng Hengshuang Zhao VLM 139 681 0 19 Jan 2024
SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective An-Chi Wang Mobarakol Islam Mengya Xu Yang Zhang Hongliang Ren AAML VLM 74 36 0 28 Apr 2023
Customized Segment Anything Model for Medical Image Segmentation Kaiwen Zhang Dong Liu MedIm VLM 95 276 0 26 Apr 2023