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arxiv: 2404.16831 · v2 · pith:DCTCT22Fnew · submitted 2024-04-25 · 💻 cs.CV

The Third Monocular Depth Estimation Challenge

Jaime Spencer , Fabio Tosi , Matteo Poggi , Ripudaman Singh Arora , Chris Russell , Simon Hadfield , Richard Bowden , GuangYuan Zhou
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ZhengXin Li Qiang Rao YiPing Bao Xiao Liu Dohyeong Kim Jinseong Kim Myunghyun Kim Mykola Lavreniuk Rui Li Qing Mao Jiang Wu Yu Zhu Jinqiu Sun Yanning Zhang Suraj Patni Aradhye Agarwal Chetan Arora Pihai Sun Kui Jiang Gang Wu Jian Liu Xianming Liu Junjun Jiang Xidan Zhang Jianing Wei Fangjun Wang Zhiming Tan Jiabao Wang Albert Luginov Muhammad Shahzad Seyed Hosseini Aleksander Trajcevski James H. Elder
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classification 💻 cs.CV
keywords challengedeptheditionestimationmonocularthirdanythingapproach
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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%.

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  1. Depth Anything V2

    cs.CV 2024-06 unverdicted novelty 6.0

    Depth Anything V2 delivers finer, more robust monocular depth predictions by replacing real labeled images with synthetic data, scaling the teacher model, and using large-scale pseudo-labeled real images for student training.