GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping

Object grasping is critical for many applications, which is also a challenging computer vision problem. However, for cluttered scene, current researches suffer from the problems of insufficient training data and the lacking of evaluation benchmarks. In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system. Our dataset contains 97,280 RGB-D image with over one billion grasp poses. Meanwhile, our evaluation system directly reports whether a grasping is successful by analytic computation, which is able to evaluate any kind of grasp poses without exhaustively labeling ground-truth. In addition, we propose an end-to-end grasp pose prediction network given point cloud inputs, where we learn approaching direction and operation parameters in a decoupled manner. A novel grasp affinity field is also designed to improve the grasping robustness. We conduct extensive experiments to show that our dataset and evaluation system can align well with real-world experiments and our proposed network achieves the state-of-the-art performance. Our dataset, source code and models are publicly available at

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Robotic Grasping GraspNet-1Billion graspnet-baseline-repo AP_similar 42.27 # 5
AP_novel 16.61 # 5
AP_seen 47.47 # 5
Robotic Grasping GraspNet-1Billion graspnet-baseline AP_similar 26.11 # 6
AP_novel 10.55 # 6
AP_seen 27.56 # 6


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