# facebookresearch/map-anything

MapAnything: Universal Feed-Forward Metric 3D Reconstruction

Repository: https://github.com/facebookresearch/map-anything
Canonical: https://ross.abutalabs.com/products/map-anything
Language: Python
License: Apache-2.0
License Family: permissive
Topics: 3d-reconstruction, ai, calibration, depth-completion, depth-estimation, image-to-3d, multi-view-stereo, robotics, sfm
Last push: 2026-08-07T19:34:34+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 81, longevity 25
- inputs: {"age_days": 363, "days_push": 26, "days_rel": 47, "gap_med": 56.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3682, forks 283 (observed 2026-08-28T04:08:13.757640+00:00)

## What it is
MapAnything is an open-source research framework from Meta and CMU for universal feed-forward metric 3D reconstruction using an end-to-end trained transformer. It supports over 12 reconstruction tasks including multi-view stereo, structure-from-motion, monocular depth estimation, registration, and depth completion, with a complete stack for data processing, training, inference, and profiling.

## Use cases
- reconstruct 3D scenes from multiple images
- estimate metric depth from a single image
- run structure-from-motion without classical pipelines
- complete missing depth in partial depth maps
- register point clouds or images using poses and calibration
- build 3D perception for robotics from camera feeds
- convert images to 3D geometry

## When to choose
- you need a single feed-forward model covering many 3D vision tasks
- you want metric-scale 3D reconstruction from images with optional calibration or pose inputs
- you need a research framework with training and inference code included
- you are working on robotics or 3D perception research

## When to avoid
- you need a lightweight production-ready 3D pipeline with minimal dependencies
- you require real-time reconstruction on CPU-only hardware
- you need classical photogrammetry with fine-grained bundle adjustment control
- your project is not in Python or lacks GPU support

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, simulation
- domain: computer-vision, machine-learning, robotics, artificial-intelligence
- platform: python, windows
- tags: 3d-reconstruction, multi-view-stereo, depth-estimation, sfm, transformer, metric-3d, depth-completion, calibration, gpu, linux, macos

## Member repositories
- facebookresearch/map-anything (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.757640+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 2026-08-29T18:31:12.903656+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/map-anything (fetched 2026-08-28T04:08:13.757640+00:00, sha a84a0a32e267)
- Data as of 2026-08-30T08:39:29.467469+00:00.