WangYueFt/dgcnn

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1846 stars · 445 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Author's reference implementation of Dynamic Graph CNN (DGCNN), a neural network module called EdgeConv for learning on 3D point clouds. It provides TensorFlow and PyTorch code for point cloud classification, semantic segmentation, and part segmentation.

Use cases

• classify 3d point clouds with deep learning
• semantic segmentation of point clouds
• part segmentation of 3d shapes
• implement edgeconv in my model
• benchmark point cloud models on modelnet
• reproduce dgcnn paper results

When to choose

• you need a proven baseline for point cloud classification or segmentation
• you want to use or extend the EdgeConv module
• you are reproducing the DGCNN paper or comparing against it

When to avoid

• you need actively maintained code with recent dependency support
• you want a high-level API rather than research scripts
• your task is not point cloud based

Facets

library · maturity maintenance

deep-learning machine-learning computer-vision deep-learning machine-learning python point-clouds edgeconv pytorch tensorflow classification segmentation research-code linux gpu

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