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Supervised learning has held 3D Vision back for too long. Meet RayZer — a self-supervised 3D model trained with zero 3D labels: ❌ No supervision of camera & geometry ✅ Just RGB images And the wild part? RayZer outperforms supervised methods (as 3D labels from COLMAP is noisy) 🌐...

71,634 次观看 • 1 年前 •via X (Twitter)

9 条评论

Hanwen Jiang 的头像
Hanwen Jiang1 年前

🔍 How does RayZer work? It performs 3D-aware image auto-encoding, which first disentangles images into scene + camera (reconstruction), then re-entangles them back into images (rendering) and learn via RGB loss. The key is splitting the images into two sets — one set to reconstruct scene, and the other to provide supervision, which avoids trivial non-3D solutions.

Hanwen Jiang 的头像
Hanwen Jiang1 年前

🤯 RayZer outperforms supervised methods — why? Turns out, 3D labels from COLMAP are noisy. GS-LRM and LVSM consistently fail on scenes of glasses, high luminance intensity, and white walls. These are cases where COLMAP usually fail. This highlights the need for self-supervised learning — and shows just how powerful it can be.

Hanwen Jiang 的头像
Hanwen Jiang1 年前

RayZer is similar to video generation models philosophically: ❌ No 3D-aware architecture ❌ No 3D representation & rendering equation ❌ No 3D supervision ✅ But 3D awareness emerges. (We show more inference results)

Hanwen Jiang 的头像
Hanwen Jiang1 年前

Joint work with @HaoTan5 @totoro97_ @Haian_Jin @__yuezhao__ @Sai__Bi @KaiZhang9546 @fujun_luan Kalyan Sunkavalli @qixing_huang @geopavlakos

Dmytro Mishkin 🇺🇦 的头像
Dmytro Mishkin 🇺🇦1 年前

Amazing! Dare to try it in Image Matching Challenge? :)

Hanwen Jiang 的头像
Hanwen Jiang1 年前

haha, I don't think it works on images with different lighting conditions now

relu 的头像
relu1 年前

Super cool. I’ve been looking for pose estimation without any supervision from SfM and couldn’t find any papers! Was super surprised. I’m glad someone finally got this working

Jeffrey Ouyang-Zhang 的头像
Jeffrey Ouyang-Zhang1 年前

cool work!

Jang Hyun (Vincent) Cho 的头像
Jang Hyun (Vincent) Cho1 年前

amazing

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