VGGT-1B-Commercial

<div align="center"> <h1>VGGT: Visual Geometry Grounded Transformer</h1> <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>

Meta AI Research; University of Oxford, VGG

Jianyuan Wang, Minghao Chen, Nikita Karaev,
Andrea Vedaldi, Christian Rupprecht, David Novotny

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This Hugging Face repository provides a model checkpoint licensed for commercial use, with the exception of military applications. Refer to the LICENSE file for full terms.

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.

Quick Start

Please refer to our Github Repo

Citation

If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:

@inproceedings{wang2025vggt,
  title={VGGT: Visual Geometry Grounded Transformer},
  author={Wang, Jianyuan and Chen, Minghao and Karaev, Nikita and Vedaldi, Andrea and Rupprecht, Christian and Novotny, David},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2025}
}