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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 görüntüleme • 3 yıl önce •via X (Twitter)

Haberler & Politika Bilim & Teknoloji Eğitim #CVPR2023

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AntiFisherYu001 profil fotoğrafı

AntiFisherYu0012 yıl önce

You are a murderer

Adrian profil fotoğrafı

Adrian3 yıl önce

@ValueAnalyst1 @a_meta4 check it out

MinotaurOnLucy profil fotoğrafı

MinotaurOnLucy3 yıl önce

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 profil fotoğrafı

julien michot3 yıl önce

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

Nikolaos Sarafianos profil fotoğrafı

Nikolaos Sarafianos3 yıl önce

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

Fisher Yu profil fotoğrafı

Fisher Yu3 yıl önce

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 profil fotoğrafı

ζ Pedram ζ3 yıl önce

Neat idea, github?

Fisher Yu profil fotoğrafı

Fisher Yu3 yıl önce

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

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@Scobleizer 69

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𝘃𝗿𝗹𝗹𝗿𝘃3 yıl önce

@Scobleizer Great work!

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After a year of team work, we're thrilled to introduce Depth Anything 3 (DA3)! 🚀 Aiming for human-like spatial perception, DA3 extends monocular depth estimation to any-view scenarios, including single images, multi-view images, and video. In pursuit of minimal modeling, DA3 reveals two key insights: 💎 A plain transformer (e.g., vanilla DINO) is enough. No specialized architecture. ✨ A single depth-ray representation is enough. No complex 3D tasks. Three series of models have been released: the main DA3 series, a monocular metric estimation series, and a monocular depth estimation series. The core team members, aside from me: Haotong Lin, Sili Chen, Jun Hao Liew, Donny Y. Chen. 👇(1/n) #DepthAnything3

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The iPhone LiDAR depth is pretty stable but low-res and low detail. Monodepth is highly detailed but unstable ("depth flickering"). For more stable and detailed metric depth, I solve for the per-frame affine transform that optimally "anchors" the monodepth to the LiDAR.

The iPhone LiDAR depth is pretty stable but low-res and low detail. Monodepth is highly detailed but unstable ("depth flickering"). For more stable and detailed metric depth, I solve for the per-frame affine transform that optimally "anchors" the monodepth to the LiDAR.

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Image to depth using Marigold and ConfyUI Then depth to 3D geometry in Blender Super interesting stuff 🤯

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working on more depth w lower weights after a couple weeks off from back squatting.

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Checkout our YouTube Channel for the entire video! We go more into depth on our #2024lifestylewrap

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