论文标题
GDN：用于端到端6-DOF GRASP检测的粗到五（C2F）表示
GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection
论文作者
论文摘要
我们提出了一个端到端的GRASP检测网络，GRASP检测网络（GDN），该网络与新颖的粗到细节（C2F）抓握表示设计合作，以基于点云检测多种和准确的6-DOF GRASP。与以前的两阶段方法相比，这些方法采样和评估了多个抓手的候选者，我们的体系结构的速度至少要快20倍。就单个对象场景的成功率和杂物场景中的完整速度而言，它的准确率也高8％和40％。我们的方法显示了具有不同视图和输入点的设置之间的卓越结果。此外，我们提出了一个新的基于AP的指标，该指标同时考虑旋转和过渡误差，使其成为GRASP检测模型的更全面的评估工具。
We proposed an end-to-end grasp detection network, Grasp Detection Network (GDN), cooperated with a novel coarse-to-fine (C2F) grasp representation design to detect diverse and accurate 6-DoF grasps based on point clouds. Compared to previous two-stage approaches which sample and evaluate multiple grasp candidates, our architecture is at least 20 times faster. It is also 8% and 40% more accurate in terms of the success rate in single object scenes and the complete rate in clutter scenes, respectively. Our method shows superior results among settings with different number of views and input points. Moreover, we propose a new AP-based metric which considers both rotation and transition errors, making it a more comprehensive evaluation tool for grasp detection models.
