  Reliable robotic grasping in unstructured environments is a crucial but
challenging task. The main problem is to generate the optimal grasp of novel
objects from partial noisy observations. This paper presents an end-to-end
grasp detection network taking one single-view point cloud as input to tackle
the problem. Our network includes three stages: Score Network (SN), Grasp
Region Network (GRN), and Refine Network (RN). Specifically, SN regresses point
grasp confidence and selects positive points with high confidence. Then GRN
conducts grasp proposal prediction on the selected positive points. RN
generates more accurate grasps by refining proposals predicted by GRN. To
further improve the performance, we propose a grasp anchor mechanism, in which
grasp anchors with assigned gripper orientations are introduced to generate
grasp proposals. Experiments demonstrate that REGNet achieves a success rate of
79.34% and a completion rate of 96% in real-world clutter, which significantly
outperforms several state-of-the-art point-cloud based methods, including GPD,
PointNetGPD, and S4G. The code is available at
https://github.com/zhaobinglei/REGNet_for_3D_Grasping.
