  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.
