sshaoshuai/PointRCNN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1817 stars · 422 forks observed · 2026-08-28

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

PyTorch implementation of PointRCNN, a two-stage 3D object detector that generates 3D box proposals directly from raw LiDAR point clouds. It is the official research code release for the CVPR 2019 paper, evaluated on the KITTI dataset.

Use cases

• detect 3D objects from LiDAR point clouds
• train a 3D object detection model on KITTI
• reproduce PointRCNN CVPR 2019 results
• run 3D box proposal generation from raw point cloud
• benchmark two-stage point cloud detectors
• research bin-based 3D box regression losses

When to choose

• you need the original PointRCNN implementation for research or paper reproduction
• you work with KITTI-format LiDAR data and PyTorch
• you want a reference two-stage point-cloud-only 3D detector

When to avoid

• you need multi-class joint training or a maintained toolbox - use OpenPCDet instead
• you need Windows or production-ready deployment support
• you want actively updated code - the repo is a frozen research release

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision computer-vision autonomous-vehicles deep-learning python 3d-object-detection point-cloud lidar pytorch kitti cvpr-2019 research-code linux gpu

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For agents

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