The Third Monocular Depth Estimation Challenge

• DOI: 10.48550/arxiv.2404.16831
• Publication Date: 2024-06-17
• Authors: Spencer, Jaime; Tosi, Fabio; Poggi, Matteo; Arora, Ripudaman ...; 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, Aleks...; Elder, James H.
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition

To appear in CVPRW2024<br/>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%.<br/>