title: VGGT: Visual Geometry Grounded Transformer
summary: 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.
paper_authors._id: 67d7f63e8c022f989bb5ecfe
paper_authors.name: Jianyuan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:46:11.045Z
paper_authors._id: 649bf403fd9cea8366d669ad
paper_authors.avatarUrl: /avatars/27bd8ca9a948ec38fee950b64f669ce3.svg
paper_authors.fullname: Jianyuan Wang
paper_authors.type: user
paper_authors.user: JianyuanWang
paper_authors._id: 67d7f63e8c022f989bb5ecff
paper_authors.name: Minghao Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:46:57.518Z
paper_authors._id: 636a3d8bf8d9af4aea18553f
paper_authors.avatarUrl: /avatars/028a86d088764fd66c36a2ddebf09f9a.svg
paper_authors.fullname: MINGHAO CHEN
paper_authors.type: user
paper_authors.user: silentchen
paper_authors._id: 67d7f63e8c022f989bb5ed00
paper_authors.name: Nikita Karaev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T14:47:04.483Z
paper_authors._id: 6393a73584c565d2c3416cb9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6393a73584c565d2c3416cb9/OGtX-i-_MLg5--qA054ti.jpeg
paper_authors.fullname: Nikita Karaev
paper_authors.type: user
paper_authors.user: nikkar
paper_authors._id: 67d7f63e8c022f989bb5ed01
paper_authors.name: Andrea Vedaldi
paper_authors._id: 67d7f63e8c022f989bb5ed02
paper_authors.name: Christian Rupprecht
paper_authors._id: 67d7f63e8c022f989bb5ed03
paper_authors.name: David Novotny
paper_title: VGGT: Visual Geometry Grounded Transformer
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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