# facebookresearch/fast3r

[CVPR 2025] Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass

Repository: https://github.com/facebookresearch/fast3r
Canonical: https://ross.abutalabs.com/products/fast3r
Homepage: https://fast3r-3d.github.io/
Language: Python
License: NOASSERTION
License Family: other
Archived: true
Last push: 2025-05-07T04:15:25+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 20, release rhythm 35, longevity 49
- inputs: {"age_days": 687, "days_push": 483, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1593, forks 95 (observed 2026-08-28T04:05:08.928322+00:00)

## What it is
Fast3R is the official PyTorch implementation of a CVPR 2025 model from Meta FAIR that reconstructs 3D scenes and estimates camera poses from up to 1500 images in a single Transformer forward pass. It generalizes the pairwise DUSt3R approach to many views in parallel, with a Gradio demo for uploading images or videos and visualizing reconstructions.

## Use cases
- reconstruct a 3D scene from many unordered photos
- estimate camera poses from a video or image set in one forward pass
- generate point clouds from multi-view images without global alignment
- research baseline for multi-view 3D reconstruction
- visualize 3D reconstruction and confidence maps from uploaded video

## When to choose
- you need fast multi-view 3D reconstruction from dozens to 1000+ images
- you want to avoid slow pairwise methods like DUSt3R with global alignment
- you need camera pose estimation as part of a vision pipeline
- you have a GPU and want a research-grade pretrained model

## When to avoid
- you need a production-ready, commercially licensed 3D reconstruction tool (license is custom/non-standard)
- you have no GPU available
- you need real-time reconstruction on edge or mobile devices
- you need precise metric-scale reconstruction for surveying or CAD

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, graphics, simulation
- domain: computer-vision, machine-learning, deep-learning, graphics, artificial-intelligence
- platform: python, windows
- tags: 3d-reconstruction, multi-view-stereo, camera-pose-estimation, transformer, cvpr-2025, point-clouds, gradio-demo, research-code, gpu, linux, macos

## Member repositories
- facebookresearch/fast3r (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:08.928322+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 2026-08-30T03:54:14.859238+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/fast3r (fetched 2026-08-28T04:05:08.928322+00:00, sha 524d30fa66b8)
  - homepage: https://fast3r-3d.github.io/ (fetched 2026-08-29T11:25:05.554902+00:00, sha 44a9c54ecfd5)
- Data as of 2026-08-30T08:39:29.467469+00:00.