# ranahanocka/MeshCNN

Convolutional Neural Network for 3D meshes in PyTorch

Repository: https://github.com/ranahanocka/MeshCNN
Canonical: https://ross.abutalabs.com/products/meshcnn
Language: Python
License: MIT
License Family: permissive
Topics: pytorch, geometric-deep-learning, convolutional-neural-networks, machine-learning, mesh, computer-graphics, segmentation, 3d
Last push: 2024-02-07T16:41:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2676, "days_push": 938, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1731, forks 342 (observed 2026-08-28T04:05:28.698142+00:00)

## What it is
MeshCNN is a PyTorch library implementing a convolutional neural network that operates directly on 3D triangular meshes, with mesh-specific convolution, pooling, and unpooling layers. It supports tasks like 3D shape classification and segmentation and was published at SIGGRAPH 2019.

## Use cases
- classify 3D shapes with a neural network
- segment parts of a 3D mesh
- run deep learning on triangular meshes
- visualize learned mesh edge collapses
- train a CNN on SHREC or human segmentation datasets

## When to choose
- you need deep learning directly on 3D mesh geometry rather than voxel or point-cloud representations
- you want a research-grade reference implementation of mesh convolutions in PyTorch
- you need pretrained models for 3D classification or segmentation benchmarks

## When to avoid
- you work with point clouds or voxels instead of triangular meshes
- you need a maintained library with recent PyTorch support, since it targets PyTorch 1.2
- you need production-scale training pipelines rather than research code

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing
- domain: deep-learning, computer-vision, graphics, machine-learning
- platform: python
- tags: pytorch, 3d-meshes, geometric-deep-learning, mesh-segmentation, shape-classification, siggraph, linux, macos, gpu

## Member repositories
- ranahanocka/MeshCNN (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:28.698142+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 2026-08-30T03:31:43.874837+00:00, confidence not recorded.
  - readme: https://github.com/ranahanocka/MeshCNN (fetched 2026-08-28T04:05:28.698142+00:00, sha 6612e4a295ea)
- Data as of 2026-08-30T08:39:29.467469+00:00.