# microsoft/MoGe

[CVPR'25 Oral] MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

Repository: https://github.com/microsoft/MoGe
Canonical: https://ross.abutalabs.com/products/moge
Homepage: https://wangrc.site/MoGePage/
Language: JavaScript
License: NOASSERTION
License Family: other
Topics: 3d-reconstruction, 3d-vision, monocular-depth-estimation, monocular-geometry-estimation
Last push: 2026-08-19T06:38:54+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 98, release rhythm 35, longevity 50
- inputs: {"age_days": 709, "days_push": 14, "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 2807, forks 215 (observed 2026-08-28T04:07:22.772423+00:00)

## What it is
MoGe is a deep learning model from Microsoft Research that recovers 3D geometry from a single open-domain image, predicting metric point maps, depth maps, normal maps, and camera field of view in one forward pass. It ships with inference code, pretrained weights, and a Hugging Face demo, achieving ~60ms latency per image on modern GPUs.

## Use cases
- estimate depth maps from single photos
- recover 3D point clouds from one image
- predict surface normal maps from images
- reconstruct 3D geometry for single-view 3D reconstruction
- estimate camera field of view from an image
- build 3D assets from photos for graphics pipelines

## When to choose
- you need fast, accurate monocular geometry or depth estimation from arbitrary images
- you want point maps, depth, and normals from a single unified model
- you need a research-grade model with pretrained weights and a demo

## When to avoid
- you need multi-view or video-based 3D reconstruction with true global scale
- you cannot run GPU inference
- you need a lightweight CPU-only depth estimator

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, computer-vision, image-processing, deep-learning
- domain: computer-vision, machine-learning, graphics, artificial-intelligence
- platform: python, cross-platform
- tags: monocular-depth-estimation, 3d-reconstruction, point-maps, depth-maps, normal-maps, cvpr-2025, research-model, inference, gpu

## Member repositories
- microsoft/MoGe (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.772423+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 2026-08-30T08:14:27.714880+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/MoGe (fetched 2026-08-28T04:07:22.772423+00:00, sha d22d190c99d0)
  - homepage: https://wangrc.site/MoGePage/ (fetched 2026-08-29T09:54:33.428222+00:00, sha 0a45df5fc9da)
- Data as of 2026-08-30T08:39:29.467469+00:00.