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Monocular depth estimation is “impossible” because one image can’t measure depth geometrically. Our iDisc #CVPR2023 can group pixels w/o supervision and learn depth inductive bias on groups. We get LiDAR-like (but denser) depth from single images! More:

Fisher Yu

2,889 subscribers

52,234 次观看 • 3 年前 •via X (Twitter)

新闻政治科学技术教育 #CVPR2023

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10 条评论

AntiFisherYu001 的头像

AntiFisherYu0012 年前

You are a murderer

Adrian 的头像

Adrian3 年前

@ValueAnalyst1 @a_meta4 check it out

MinotaurOnLucy 的头像

MinotaurOnLucy3 年前

As an outsider, I have the following questions: What is the current state-of-the-art performance for out of distribution cases? If it is not good, will we see a foundational model that achieves good out of distribution performance in the near term, or would it be more mid term?

julien michot 的头像

julien michot3 年前

Why "impossible"? Here you get the most plausible depths.

Nikolaos Sarafianos 的头像

Nikolaos Sarafianos3 年前

This is great work! Did you test it on humans at various distances from the camera by any chance?

Fisher Yu 的头像

Fisher Yu3 年前

We test the method on street scenes that have people. However, it is indeed interesting to see whether we can use the method to estimate depth in human-centric scenes.

ζ Pedram ζ 的头像

ζ Pedram ζ3 年前

Neat idea, github?

Fisher Yu 的头像

Fisher Yu3 年前

github link: The full code will be released before CVPR 2023.

test bot 的头像

test bot3 年前

@Scobleizer 69

𝘃𝗿𝗹𝗹𝗿𝘃 的头像

𝘃𝗿𝗹𝗹𝗿𝘃3 年前

@Scobleizer Great work!

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