# facebookresearch/meshrcnn

code for Mesh R-CNN, ICCV 2019

Repository: https://github.com/facebookresearch/meshrcnn
Canonical: https://ross.abutalabs.com/products/meshrcnn
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-01-12T11:00:49+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 62, release rhythm 35, longevity 100
- inputs: {"age_days": 2430, "days_push": 233, "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 1161, forks 175 (observed 2026-08-28T04:03:49.251286+00:00)

## What it is
Mesh R-CNN is Facebook AI Research's official implementation of the ICCV 2019 paper, a model that detects objects in images and predicts their 3D shape as triangle meshes. It is built on top of Detectron2 and PyTorch3D and includes demos, training, and evaluation code for the Pix3D and ShapeNet benchmarks.

## Use cases
- predict 3D mesh models of objects from a single image
- run 3D object detection and instance segmentation on photos
- reproduce Mesh R-CNN results on the Pix3D benchmark
- train and evaluate 3D shape prediction on ShapeNet
- experiment with mesh prediction heads in a Detectron2 pipeline

## When to choose
- you need research-grade code for image-to-3D mesh prediction
- your project already uses Detectron2 and PyTorch3D
- you want to reproduce or extend the Mesh R-CNN paper

## When to avoid
- you need a production-ready, actively supported 3D vision product
- you want a simple pretrained API without installing Detectron2 and PyTorch3D
- you need real-time 3D reconstruction on edge devices

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, machine-learning, deep-learning
- platform: python
- tags: 3d-reconstruction, mesh-prediction, instance-segmentation, detectron2, pytorch3d, iccv-2019, research-code, research, linux, gpu

## Member repositories
- facebookresearch/meshrcnn (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:49.251286+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 2026-08-30T06:31:42.361604+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/meshrcnn (fetched 2026-08-28T04:03:49.251286+00:00, sha 29e2a68fb213)
- Data as of 2026-08-30T08:39:29.467469+00:00.