Pointcept

Health v2 · maintenance only

Adoption not part of the score

3196 stars · 408 forks observed · 2026-08-28

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

Pointcept is a PyTorch-based research codebase for point cloud perception, providing implementations of state-of-the-art 3D scene understanding models such as Point Transformer V3, Sonata, Concerto, and SAM3D. It offers training, evaluation, and pretrained weights for tasks like semantic and instance segmentation of 3D point clouds.

Use cases

• train point cloud semantic segmentation models
• run 3D scene segmentation with Point Transformer V3
• apply Segment Anything to 3D point clouds
• benchmark point cloud perception models on ScanNet
• use pretrained 3D encoders for downstream tasks
• research self-supervised learning for point clouds

When to choose

• you need state-of-the-art point cloud segmentation or backbone models
• you are doing 3D perception research in PyTorch
• you want pretrained 3D scene encoders like Sonata or PTv3

When to avoid

• you need production-ready 3D inference services rather than research code
• your data is meshes or voxels rather than sparse point clouds
• you need a no-GPU or lightweight deployment

Facets

library · maturity active

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning python point-cloud 3d-vision pytorch semantic-segmentation scene-understanding point-transformer self-supervised-learning research-codebase research gpu linux

Member repositories

For agents

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