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<div align="center">
<h1>VGGT: Visual Geometry Grounded Transformer</h1>
<!-- <a href=""><img src='https://img.shields.io/badge/arXiv-VGGT' alt='Paper PDF'></a> -->
<!-- <a href=''><img src='https://img.shields.io/badge/Project_Page-green' alt='Project Page'></a> -->
<a href="https://jytime.github.io/data/VGGT_CVPR25.pdf" target="_blank" rel="noopener noreferrer">
<img src="https://img.shields.io/badge/Paper-VGGT" alt="Paper PDF">
</a>
<a href="https://arxiv.org/abs/2503.11651"><img src="https://img.shields.io/badge/arXiv-2503.11651-b31b1b" alt="arXiv"></a>
<a href="https://vgg-t.github.io/"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a>
<a href='https://huggingface.co/spaces/facebook/vggt'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
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year={2025}
}
```
## Overview
Visual Geometry Grounded Transformer (VGGT, CVPR 2025) is a feed-forward neural network that directly infers all key 3D attributes of a scene, including extrinsic and intrinsic camera parameters, point maps, depth maps, and 3D point tracks, **from one, a few, or hundreds of its views, within seconds**.