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 www.graspnet.net.
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Datasets
Introduced in the Paper:
GraspNet-1BillionTask | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Robotic Grasping | GraspNet-1Billion | graspnet-baseline-CD | AP_similar | 42.27 | # 6 | |
AP_novel | 16.61 | # 6 | ||||
AP_seen | 47.47 | # 6 | ||||
mAP | 35.45 | # 6 | ||||
Robotic Grasping | GraspNet-1Billion | graspnet-baseline | AP_similar | 26.11 | # 7 | |
AP_novel | 10.55 | # 7 | ||||
AP_seen | 27.56 | # 7 | ||||
mAP | 21.41 | # 7 |