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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2404.16831 |
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| _version_ | 1866916226423324672 |
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| author | Spencer, Jaime Tosi, Fabio Poggi, Matteo Arora, Ripudaman Singh Russell, Chris Hadfield, Simon Bowden, Richard Zhou, GuangYuan Li, ZhengXin Rao, Qiang Bao, YiPing Liu, Xiao Kim, Dohyeong Kim, Jinseong Kim, Myunghyun Lavreniuk, Mykola Li, Rui Mao, Qing Wu, Jiang Zhu, Yu Sun, Jinqiu Zhang, Yanning Patni, Suraj Agarwal, Aradhye Arora, Chetan Sun, Pihai Jiang, Kui Wu, Gang Liu, Jian Liu, Xianming Jiang, Junjun Zhang, Xidan Wei, Jianing Wang, Fangjun Tan, Zhiming Wang, Jiabao Luginov, Albert Shahzad, Muhammad Hosseini, Seyed Trajcevski, Aleksander Elder, James H. |
| author_facet | Spencer, Jaime Tosi, Fabio Poggi, Matteo Arora, Ripudaman Singh Russell, Chris Hadfield, Simon Bowden, Richard Zhou, GuangYuan Li, ZhengXin Rao, Qiang Bao, YiPing Liu, Xiao Kim, Dohyeong Kim, Jinseong Kim, Myunghyun Lavreniuk, Mykola Li, Rui Mao, Qing Wu, Jiang Zhu, Yu Sun, Jinqiu Zhang, Yanning Patni, Suraj Agarwal, Aradhye Arora, Chetan Sun, Pihai Jiang, Kui Wu, Gang Liu, Jian Liu, Xianming Jiang, Junjun Zhang, Xidan Wei, Jianing Wang, Fangjun Tan, Zhiming Wang, Jiabao Luginov, Albert Shahzad, Muhammad Hosseini, Seyed Trajcevski, Aleksander Elder, James H. |
| contents | This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Third Monocular Depth Estimation Challenge Spencer, Jaime Tosi, Fabio Poggi, Matteo Arora, Ripudaman Singh Russell, Chris Hadfield, Simon Bowden, Richard Zhou, GuangYuan Li, ZhengXin Rao, Qiang Bao, YiPing Liu, Xiao Kim, Dohyeong Kim, Jinseong Kim, Myunghyun Lavreniuk, Mykola Li, Rui Mao, Qing Wu, Jiang Zhu, Yu Sun, Jinqiu Zhang, Yanning Patni, Suraj Agarwal, Aradhye Arora, Chetan Sun, Pihai Jiang, Kui Wu, Gang Liu, Jian Liu, Xianming Jiang, Junjun Zhang, Xidan Wei, Jianing Wang, Fangjun Tan, Zhiming Wang, Jiabao Luginov, Albert Shahzad, Muhammad Hosseini, Seyed Trajcevski, Aleksander Elder, James H. Computer Vision and Pattern Recognition This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%. |
| title | The Third Monocular Depth Estimation Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.16831 |