torch-points3d/torch-points3d

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2711 stars · 404 forks observed · 2026-08-28

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

A PyTorch-based framework for deep learning on 3D point clouds, supporting models like PointNet, KPConv, and MinkowskiEngine sparse convolutions. It provides a high-level API and Hydra-based configuration for reproducible training and benchmarking on datasets like S3DIS and ScanNet.

Use cases

• train point cloud segmentation models
• benchmark deep learning models on 3D datasets
• run semantic segmentation on point clouds
• experiment with KPConv or PointNet architectures
• classify 3D point cloud data
• reproduce state-of-the-art point cloud benchmarks

When to choose

• you need a high-level, configurable framework for point cloud deep learning in PyTorch
• you want reproducible benchmarking against standard 3D datasets like S3DIS or ScanNet
• you want to compare multiple point cloud architectures under one API

When to avoid

• you work with 2D images or standard vision tasks rather than 3D point clouds
• you need a framework under active development with recent feature updates
• you cannot use CUDA or a sparse convolution backend

Facets

framework · maturity maintenance

deep-learning machine-learning computer-vision benchmarking deep-learning computer-vision machine-learning python point-cloud pytorch pointnet kpconv minkowskiengine segmentation 3d-vision pytorch-geometric gpu docker linux

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markdown · JSON · MCP: product_card(name="torch-points3d/torch-points3d")