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Reliable robotic grasping in unstructured environments is a crucial butchallenging task. The main problem is to generate the optimal grasp of novelobjects from partial noisy observations. This paper presents an end-to-endgrasp detection network taking one single-view point cloud as input to tacklethe problem. Our network includes three stages: Score Network (SN), GraspRegion Network (GRN), and Refine Network (RN). Specifically, SN regresses pointgrasp confidence and selects positive points with high confidence. Then GRNconducts grasp proposal prediction on the selected positive points. RNgenerates more accurate grasps by refining proposals predicted by GRN. Tofurther improve the performance, we propose a grasp anchor mechanism, in whichgrasp anchors with assigned gripper orientations are introduced to generategrasp proposals. Experiments demonstrate that REGNet achieves a success rate of79.34% and a completion rate of 96% in real-world clutter, which significantlyoutperforms several state-of-the-art point-cloud based methods, including GPD,PointNetGPD, and S4G. The code is available athttps://github.com/zhaobinglei/REGNet_for_3D_Grasping.

REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds