GraspNet

GraspNet is an open project for general object grasping that is continuously enriched. Currently we release GraspNet-1Billion. It contains 190 cluttered and complex scenes captured by two popular RGBD cameras (Kinect A4Z and RealSense D435), bringing 97,280 images in total. For each image, we annotate the accurate 6D pose and the dense grasp poses for each object. In total, our dataset contains over 1.1 billion grasp poses. We hope our benchmark can facilitate related research in general object grasping as well as other related areas (e.g. 6D pose estimation, unseen object segmentation, etc.).
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ABUNDANT DATA

190 scenes, 97,280 images, 88 objects, 2 cameras

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DENSE ANNOTATIONS

6DoF grasp labels, planar grasp labels, 6D poses, instance masks, etc.

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UNIFIED EVALUATION

Standard evaluation metric for different grasp pose representation

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Please cite our paper if it helps your research:

@inproceedings{fang2020graspnet,
  title={GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping},
  author={Fang, Hao-Shu and Wang, Chenxi and Gou, Minghao and Lu, Cewu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11444--11453},
  year={2020}
}

ABUNDANT DATA

DENSE ANNOTATIONS

UNIFIED EVALUATION

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GraspNet