guochengqian/PointNeXt

Health v2 · maintenance only

Adoption not part of the score

1067 stars · 163 forks observed · 2026-08-28

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

PointNeXt is the official PyTorch implementation of the NeurIPS'22 paper that improves PointNet++ via better training and model scaling strategies for point cloud understanding. It includes the OpenPoints library, a flexible codebase supporting many point-based networks with reproducible, fair benchmarking.

Use cases

• train point cloud classification models
• benchmark point-based networks like PointNet++ and DGCNN fairly
• run semantic segmentation on 3D point clouds
• reproduce PointNeXt paper results with pretrained models
• build custom point cloud networks from config files
• apply improved training strategies to boost PointNet++ accuracy

When to choose

• you need state-of-the-art point cloud classification or segmentation in PyTorch
• you want a reproducible benchmarking framework for point-based methods
• you want pretrained models and training logs for 3D vision research

When to avoid

• you work with images or text rather than 3D point clouds
• you need a production-ready inference service rather than a research codebase
• you don't have GPU resources for training deep learning models

Facets

library · maturity stable

machine-learning deep-learning computer-vision deep-learning machine-learning python point-cloud pointnet pytorch 3d-vision research-code benchmarking neurips gpu linux

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markdown · JSON · MCP: product_card(name="guochengqian/PointNeXt")