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Hanwen Jiang

@hanwenjiang1 • 2,375 subscribers

Research Scientist @ Adobe Research | PhD @ UT Austin | Exploring what could unlock human productivity: currently visual intelligence, previous 3D (failed)

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(1/N) Will this be the BERT/GPT moment for 3D vision？ Finally, unsupervised pre-training for 3D works. Led by Qitao Zhao , we present E-RayZer — a fully self-supervised 3D reconstruction model that: 🔥Matches or surpasses supervised methods like VGGT 👀Learns transferable 3D representations, outperforming CroCo, VideoMAE, and DINO 📈Scales with more unlabeled data A new recipe for scalable 3D foundation models.
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(1/N) Will this be the BERT/GPT moment for 3D vision？ Finally, unsupervised pre-training for 3D works. Led by Qitao Zhao , we present E-RayZer — a fully self-supervised 3D reconstruction model that: 🔥Matches or surpasses supervised methods like VGGT 👀Learns transferable 3D representations, outperforming CroCo, VideoMAE, and DINO 📈Scales with more unlabeled data A new recipe for scalable 3D foundation models.

Hanwen Jiang

58,737 views • 10 months ago

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) 🌐 Project: (1/4)
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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) 🌐 Project: (1/4)

Hanwen Jiang

71,634 views • 1 year ago

💥 Think more real data is needed for scene reconstruction? Think again! Meet MegaSynth: scaling up feed-forward 3D scene reconstruction with synthesized scenes. In 3 days, it generates 700K scenes for training—70x larger than real data! ✨ The secret? Reconstruction is mostly non-semantic! No need to rely heavily on real or highly realistic synthetic data. 🌐 Project: (1/4)
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💥 Think more real data is needed for scene reconstruction? Think again! Meet MegaSynth: scaling up feed-forward 3D scene reconstruction with synthesized scenes. In 3 days, it generates 700K scenes for training—70x larger than real data! ✨ The secret? Reconstruction is mostly non-semantic! No need to rely heavily on real or highly realistic synthetic data. 🌐 Project: (1/4)

Hanwen Jiang

27,343 views • 1 year ago

Dense observations with perfect camera poses are hard to collect. What is a more practical setting for large-scale general object reconstruction? Presenting FORGE - jointly recovering object shape and camera poses from few-view observations, and it generalizes to novel categories in zero-shot. Project page & code: Joint work with Zhenyu Jiang, Kristen Grauman, Yuke Zhu
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Dense observations with perfect camera poses are hard to collect. What is a more practical setting for large-scale general object reconstruction? Presenting FORGE - jointly recovering object shape and camera poses from few-view observations, and it generalizes to novel categories in zero-shot. Project page & code: Joint work with Zhenyu Jiang, Kristen Grauman, Yuke Zhu

Hanwen Jiang

18,132 views • 3 years ago

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