VGGT: Visual Geometry Grounded Transformer
Authors: name: Jianyuan Wang, name: Minghao Chen, name: Nikita Karaev, name: Andrea Vedaldi, name: Christian Rupprecht, name: David Novotny
Organization: AI at Meta

We present VGGT, a feed-forward neural network that directly infers all key
3D attributes of a scene, including camera parameters, point maps, depth maps,
and 3D point tracks, from one, a few, or hundreds of its views. This approach
is a step forward in 3D computer vision, where models have typically been
constrained to and specialized for single tasks. It is also simple and
efficient, reconstructing images in under one second, and still outperforming
alternatives that require post-processing with visual geometry optimization
techniques. The network achieves state-of-the-art results in multiple 3D tasks,
including camera parameter estimation, multi-view depth estimation, dense point
cloud reconstruction, and 3D point tracking. We also show that using pretrained
VGGT as a feature backbone significantly enhances downstream tasks, such as
non-rigid point tracking and feed-forward novel view synthesis. Code and models
are publicly available at https://github.com/facebookresearch/vggt.