# facebookresearch/vggt

[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer

Repository: https://github.com/facebookresearch/vggt
Canonical: https://ross.abutalabs.com/products/vggt
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-05-19T03:39:39+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 83, release rhythm 35, longevity 40
- inputs: {"age_days": 561, "days_push": 106, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14292, forks 1537 (observed 2026-08-28T04:11:06.688133+00:00)

## What it is
VGGT (Visual Geometry Grounded Transformer) is a feed-forward transformer model from Meta AI and Oxford VGG that infers 3D geometry—camera parameters, depth maps, and point maps—directly from one or more images. It ships as a Python library with pretrained checkpoints, a Hugging Face demo, and training/finetuning code.

## Use cases
- estimate camera poses from multiple images
- reconstruct 3D point clouds from photos
- predict depth maps from a single or few images
- run structure-from-motion with a neural network instead of COLMAP
- finetune a 3D vision transformer on a custom dataset
- feed 3D geometry features into downstream vision or robotics models

## When to choose
- you need fast feed-forward 3D reconstruction or camera estimation from images
- you want a learned alternative to classical SfM pipelines like COLMAP
- you need a strong pretrained 3D vision backbone for downstream tasks
- you want a CVPR 2025 award-winning, actively maintained research model with commercial-use checkpoint options

## When to avoid
- you need a lightweight CPU-only solution—the model requires a GPU with substantial memory
- you need permissively licensed weights without an approval workflow—the original checkpoint is non-commercial and the commercial one requires an application form
- you need real-time on-device inference on edge hardware
- you need classical, highly precise bundle adjustment SfM with full control over the pipeline

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, transformers, gpu-computing
- domain: computer-vision, machine-learning, deep-learning, artificial-intelligence, robotics
- platform: python, cross-platform
- tags: 3d-reconstruction, visual-geometry, transformer, camera-estimation, depth-estimation, point-cloud, cvpr-2025, research-model, gpu, linux

## Member repositories
- facebookresearch/vggt (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:06.688133+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:12:40.100735+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/vggt (fetched 2026-08-28T04:11:06.688133+00:00, sha 4d322cbf9384)
- Data as of 2026-08-30T08:39:29.467469+00:00.