VGGT: Visual Geometry Grounded Transformer
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.
67d7f63e8c022f989bb5ecfe
Jianyuan Wang
admin_assigned
2025-03-17T14:46:11.045Z
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Jianyuan Wang
user
JianyuanWang
67d7f63e8c022f989bb5ecff
Minghao Chen
admin_assigned
2025-03-17T14:46:57.518Z
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MINGHAO CHEN
user
silentchen
67d7f63e8c022f989bb5ed00
Nikita Karaev
admin_assigned
2025-03-17T14:47:04.483Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6393a73584c565d2c3416cb9/OGtX-i-_MLg5--qA054ti.jpeg
Nikita Karaev
user
nikkar
67d7f63e8c022f989bb5ed01
Andrea Vedaldi
67d7f63e8c022f989bb5ed02
Christian Rupprecht
67d7f63e8c022f989bb5ed03
David Novotny
VGGT: Visual Geometry Grounded Transformer
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.
VGGT, a feed-forward neural network, efficiently infers multiple 3D attributes from single or multiple views, outperforming alternatives and enhancing downstream tasks without post-processing.
feed-forward neural network camera parameters point maps depth maps 3D point tracks 3D computer vision multi-view depth estimation dense point cloud reconstruction 3D point tracking pretrained VGGT non-rigid point tracking feed-forward novel view synthesis