Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPR42600.2020.01146 | CVPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
27 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haoshu Fang | 1 | 57 | 6.86 |
| Chenxi Wang | 2 | 0 | 1.01 |
| Minghao Gou | 3 | 2 | 1.39 |
| Cewu Lu | 4 | 993 | 62.08 |