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| Format: | Preprint |
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2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.17787 |
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| _version_ | 1866916706428911616 |
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| author | Obukhov, Anton Poggi, Matteo Tosi, Fabio Arora, Ripudaman Singh Spencer, Jaime Russell, Chris Hadfield, Simon Bowden, Richard Wang, Shuaihang Ma, Zhenxin Chen, Weijie Xu, Baobei Sun, Fengyu Xie, Di Zhu, Jiang Lavreniuk, Mykola Guan, Haining Wu, Qun Zeng, Yupei Lu, Chao Wang, Huanran Zhou, Guangyuan Zhang, Haotian Wang, Jianxiong Rao, Qiang Wang, Chunjie Liu, Xiao Lou, Zhiqiang Jiang, Hualie Chen, Yihao Xu, Rui Tan, Minglang Qin, Zihan Mao, Yifan Liu, Jiayang Xu, Jialei Yang, Yifan Zhao, Wenbo Jiang, Junjun Liu, Xianming Zhao, Mingshuai Ming, Anlong Chen, Wu Xue, Feng Yu, Mengying Gao, Shida Wang, Xiangfeng Omotara, Gbenga Farag, Ramy Demby, Jacket Tousi, Seyed Mohamad Ali DeSouza, Guilherme N Yang, Tuan-Anh Nguyen, Minh-Quang Tran, Thien-Phuc Luginov, Albert Shahzad, Muhammad |
| author_facet | Obukhov, Anton Poggi, Matteo Tosi, Fabio Arora, Ripudaman Singh Spencer, Jaime Russell, Chris Hadfield, Simon Bowden, Richard Wang, Shuaihang Ma, Zhenxin Chen, Weijie Xu, Baobei Sun, Fengyu Xie, Di Zhu, Jiang Lavreniuk, Mykola Guan, Haining Wu, Qun Zeng, Yupei Lu, Chao Wang, Huanran Zhou, Guangyuan Zhang, Haotian Wang, Jianxiong Rao, Qiang Wang, Chunjie Liu, Xiao Lou, Zhiqiang Jiang, Hualie Chen, Yihao Xu, Rui Tan, Minglang Qin, Zihan Mao, Yifan Liu, Jiayang Xu, Jialei Yang, Yifan Zhao, Wenbo Jiang, Junjun Liu, Xianming Zhao, Mingshuai Ming, Anlong Chen, Wu Xue, Feng Yu, Mengying Gao, Shida Wang, Xiangfeng Omotara, Gbenga Farag, Ramy Demby, Jacket Tousi, Seyed Mohamad Ali DeSouza, Guilherme N Yang, Tuan-Anh Nguyen, Minh-Quang Tran, Thien-Phuc Luginov, Albert Shahzad, Muhammad |
| contents | This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a dataset featuring challenging environments in both natural and indoor settings. In this edition, we revised the evaluation protocol to use least-squares alignment with two degrees of freedom to support disparity and affine-invariant predictions. We also revised the baselines and included popular off-the-shelf methods: Depth Anything v2 and Marigold. The challenge received a total of 24 submissions that outperformed the baselines on the test set; 10 of these included a report describing their approach, with most leading methods relying on affine-invariant predictions. The challenge winners improved the 3D F-Score over the previous edition's best result, raising it from 22.58% to 23.05%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Fourth Monocular Depth Estimation Challenge Obukhov, Anton Poggi, Matteo Tosi, Fabio Arora, Ripudaman Singh Spencer, Jaime Russell, Chris Hadfield, Simon Bowden, Richard Wang, Shuaihang Ma, Zhenxin Chen, Weijie Xu, Baobei Sun, Fengyu Xie, Di Zhu, Jiang Lavreniuk, Mykola Guan, Haining Wu, Qun Zeng, Yupei Lu, Chao Wang, Huanran Zhou, Guangyuan Zhang, Haotian Wang, Jianxiong Rao, Qiang Wang, Chunjie Liu, Xiao Lou, Zhiqiang Jiang, Hualie Chen, Yihao Xu, Rui Tan, Minglang Qin, Zihan Mao, Yifan Liu, Jiayang Xu, Jialei Yang, Yifan Zhao, Wenbo Jiang, Junjun Liu, Xianming Zhao, Mingshuai Ming, Anlong Chen, Wu Xue, Feng Yu, Mengying Gao, Shida Wang, Xiangfeng Omotara, Gbenga Farag, Ramy Demby, Jacket Tousi, Seyed Mohamad Ali DeSouza, Guilherme N Yang, Tuan-Anh Nguyen, Minh-Quang Tran, Thien-Phuc Luginov, Albert Shahzad, Muhammad Computer Vision and Pattern Recognition This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a dataset featuring challenging environments in both natural and indoor settings. In this edition, we revised the evaluation protocol to use least-squares alignment with two degrees of freedom to support disparity and affine-invariant predictions. We also revised the baselines and included popular off-the-shelf methods: Depth Anything v2 and Marigold. The challenge received a total of 24 submissions that outperformed the baselines on the test set; 10 of these included a report describing their approach, with most leading methods relying on affine-invariant predictions. The challenge winners improved the 3D F-Score over the previous edition's best result, raising it from 22.58% to 23.05%. |
| title | The Fourth Monocular Depth Estimation Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.17787 |