# rmurai0610/MASt3R-SLAM

[CVPR 2025] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors

Repository: https://github.com/rmurai0610/MASt3R-SLAM
Canonical: https://ross.abutalabs.com/products/mast3r-slam
Homepage: https://edexheim.github.io/mast3r-slam/
Language: Python
License: NOASSERTION
License Family: other
Topics: computer-vision, robotics, slam, cvpr2025
Last push: 2025-11-09T07:36:29+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 51, release rhythm 35, longevity 39
- inputs: {"age_days": 555, "days_push": 297, "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 3167, forks 374 (observed 2026-08-28T04:07:47.301916+00:00)

## What it is
MASt3R-SLAM is a real-time monocular dense SLAM system built on the MASt3R two-view 3D reconstruction prior, producing globally consistent camera poses and dense geometry at 15 FPS. It supports uncalibrated RGB video with a generic central camera model, plus camera tracking, pointmap fusion, loop closure, and global optimisation.

## Use cases
- run real-time dense SLAM from a monocular RGB video
- reconstruct 3D geometry from uncalibrated camera footage
- track camera pose and build dense maps without known calibration
- evaluate SLAM on TUM and other benchmark datasets
- handle zooming or time-varying camera models in SLAM
- research dense SLAM with learned 3D reconstruction priors

## When to choose
- you need real-time dense SLAM from a single RGB camera without calibration
- you want state-of-the-art monocular SLAM benchmark performance with known calibration
- your videos are in-the-wild with no fixed parametric camera model

## When to avoid
- you need a production system with a permissive license (license is non-standard)
- you lack a CUDA-capable GPU
- you need stereo, LiDAR, or multi-camera SLAM
- you need a lightweight CPU-only solution

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, simulation
- domain: computer-vision, robotics, autonomous-vehicles
- platform: python
- tags: slam, 3d-reconstruction, monocular-camera, real-time, pointmap, loop-closure, cvpr2025, research-code, linux, gpu

## Member repositories
- rmurai0610/MASt3R-SLAM (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.301916+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 2026-08-29T18:45:31.883390+00:00, confidence not recorded.
  - readme: https://github.com/rmurai0610/MASt3R-SLAM (fetched 2026-08-28T04:07:47.301916+00:00, sha 047b13542996)
  - homepage: https://edexheim.github.io/mast3r-slam/ (fetched 2026-08-29T09:39:45.686716+00:00, sha abf0e72c8e64)
- Data as of 2026-08-30T08:39:29.467469+00:00.