graspnet/graspnet-baseline

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1008 stars · 228 forks observed · 2026-08-28

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

The official baseline deep learning model for the GraspNet-1Billion benchmark, detecting dense 6-DoF grasp poses from point clouds of cluttered scenes. It includes training, inference, and evaluation code built on PyTorch with custom CUDA operators.

Use cases

• detect grasp poses for objects from point clouds
• train a robot grasping detection model
• evaluate grasp detection on the GraspNet-1Billion benchmark
• run grasp inference on RGBD camera data
• reproduce CVPR 2020 grasping baseline results
• generate tolerance labels for grasp training

When to choose

• you need a strong baseline for 6-DoF grasp detection research
• you want to benchmark against GraspNet-1Billion
• you're doing robotics manipulation research with point clouds

When to avoid

• you need a production-ready grasping system for commercial use (license is CC BY-NC-SA)
• you want a plug-and-play robot control stack rather than a research model
• you lack a GPU or the ability to compile custom CUDA operators

Facets

library · maturity stable

machine-learning deep-learning computer-vision robotics robotics computer-vision deep-learning autonomous-vehicles python grasp-detection point-cloud pytorch benchmark 6d-pose rgb-d research-code non-commercial-license linux gpu

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