drprojects/superpoint_transformer

Health v2 · maintenance only

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1049 stars · 133 forks observed · 2026-08-28

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

Official PyTorch implementation of Superpoint Transformer (ICCV'23), SuperCluster (3DV'24), and EZ-SP (ICRA'26) for efficient semantic and panoptic segmentation of large-scale 3D point clouds. It partitions point clouds into hierarchical superpoints and applies lightweight transformer-based segmentation on top.

Use cases

• segment large 3d point clouds semantically
• run panoptic segmentation on lidar scans
• partition point clouds into superpoints
• train a fast lightweight 3d segmentation model
• cluster superpoints into object instances
• reproduce iccv 2023 superpoint transformer results

When to choose

• you need efficient semantic or panoptic segmentation of large-scale 3D scenes
• you want a lightweight, fast transformer model for point clouds
• you need hierarchical superpoint partitioning of point clouds
• you want reproducible research code from published papers

When to avoid

• you need 2D image segmentation rather than 3D point clouds
• you need a production-ready plug-and-play inference service rather than a research codebase
• you work outside PyTorch or lack GPU resources

Facets

library · maturity active

deep-learning machine-learning image-processing computer-vision deep-learning machine-learning python windows point-cloud semantic-segmentation panoptic-segmentation superpoint transformer pytorch 3d-scene-understanding graph-clustering research-code gpu linux macos

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