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[pdf]
[arXiv]
[bibtex]@InProceedings{Obukhov_2025_CVPR, author = {Obukhov, Anton and Poggi, Matteo and Tosi, Fabio and Arora, Ripudaman Singh and Spencer, Jaime and Russel, Chris and Hadfield, Simon and Bowden, Richard and Wang, Shuaihang and Ma, Zhenxin and Chen, Weijie and Xu, Baobei and Sun, Fengyu and Xie, Di and Zhu, Jiang and Lavreniuk, Mykola and Guan, Haining and Wu, Qun and Zeng, Yupei and Lu, Chao and Wang, Huanran and Zhou, Guangyuan and Zhang, Haotian and Wang, Jianxiong and Rao, Qiang and Wang, Chunjie and Liu, Xiao and Lou, Zhiqiang and Jiang, Hualie and Chen, Yihao and Xu, Rui and Tan, Minglang and Qin, Zihan and Mao, Yifan and Liu, Jiayang and Xu, Jialei and Yang, Yifan and Zhao, Wenbo and Jiang, Junjun and Liu, Xianming and Zhao, Mingshuai and Ming, Anlong and Chen, Wu and Xue, Feng and Yu, Mengying and Gao, Shida and Wang, Xiangfeng and Omotara, Gbenga and Farag, Ramy and Demby's, Jacket and Tousi, Seyed Mohamad Ali and DeSouza, Guilherme N. and Yang, Tuấn Anh and Nguyễn, Minh Quang and Trần, Thi\^en Ph\'uc and Luginov, Albert and Shahzad, Muhammad}, title = {The Fourth Monocular Depth Estimation Challenge}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops}, month = {June}, year = {2025}, pages = {6182-6195} }
The Fourth Monocular Depth Estimation Challenge
Abstract
This paper discusses the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), a challenge focusing on zero-shot generalization to the SYNS-Patches benchmark, a dataset featuring challenging environments in both natural and indoor settings. In this edition, we switched the evaluation protocol to perform least-squares rather than median alignment. The challenge received a total of 24 submissions outperforming the baselines on the test set: 10 among them submitted a report describing their approach, with a diffuse use of Depth Anything v2 at the core of most methods. The challenge winners improved 3D F-Score performance over the previous edition winners from 22.58% to 23.05%.
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