Paper Title:
The Third Monocular Depth Estimation Challenge
Published on:
25 April 2024
Primary Category:
Computer Vision and Pattern Recognition
Paper Authors:
Jaime Spencer,
Fabio Tosi,
Matteo Poggi,
Ripudaman Singh Arora,
Chris Russell,
Simon Hadfield,
Richard Bowden,
GuangYuan Zhou,
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
Challenge tests depth estimation generalization
19 submissions beat baseline performance
Most use Depth Anything model
Winning method gets 23.72% 3D F-Score
Monocular depth estimation challenge tests generalization
The paper summarizes the third Monocular Depth Estimation Challenge, which tested algorithms on their ability to generalize to complex natural and indoor scenes. 19 submissions outperformed the baseline method. 10 teams submitted reports, showing widespread use of Depth Anything model. The top method increased the 3D F-Score from 17.51% to 23.72%.
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