# naver/dust3r

DUSt3R: Geometric 3D Vision Made Easy

Repository: https://github.com/naver/dust3r
Canonical: https://ross.abutalabs.com/products/dust3r
Homepage: https://dust3r.europe.naverlabs.com/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-09-24T16:32:31+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 43, release rhythm 35, longevity 66
- inputs: {"age_days": 924, "days_push": 343, "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 7288, forks 765 (observed 2026-08-28T04:09:58.037332+00:00)

## What it is
DUSt3R is the official PyTorch implementation of a CVPR 2024 model that performs dense, unconstrained stereo and multi-view 3D reconstruction from arbitrary image collections without requiring camera calibration or pose priors. It regresses pointmaps with a Transformer architecture and includes a global alignment strategy, training code, and an interactive demo.

## Use cases
- reconstruct a 3D model from a pair of photos
- estimate depth maps without camera calibration
- recover camera poses from unordered image collections
- run multi-view stereo reconstruction in the wild
- match pixels between images via 3D pointmaps
- train a 3D vision model on custom datasets

## When to choose
- you need 3D reconstruction from images without known camera intrinsics or poses
- you want a research-grade model for depth, pose, and pointmap estimation
- you are doing robotics, SLAM, or SfM research and want a strong pretrained baseline

## When to avoid
- you need a commercially licensed solution (code is CC BY-NC-SA 4.0, non-commercial only)
- you need real-time reconstruction on CPU or edge hardware
- you need a production-ready end-user application rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, image-processing, machine-learning, deep-learning, gpu-computing
- domain: computer-vision, machine-learning, artificial-intelligence, robotics, graphics
- platform: python
- tags: 3d-reconstruction, pointmaps, multi-view-stereo, depth-estimation, camera-pose-estimation, transformer, non-commercial-license, research-code, linux, gpu, docker

## Member repositories
- naver/dust3r (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:58.037332+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 2026-08-29T17:38:51.722337+00:00, confidence not recorded.
  - readme: https://github.com/naver/dust3r (fetched 2026-08-28T04:09:58.037332+00:00, sha c17e400e747d)
  - homepage: https://dust3r.europe.naverlabs.com/ (fetched 2026-08-29T08:36:29.780682+00:00, sha 767e37afbe14)
  - site_page: https://europe.naverlabs.com/about (fetched 2026-08-29T08:36:29.807820+00:00, sha 9b6b73f491dc)
  - site_page: https://europe.naverlabs.com/about/collaboration (fetched 2026-08-29T08:36:29.870662+00:00, sha bda22dfced38)
- Data as of 2026-08-30T08:39:29.467469+00:00.