  Regional geometric information is crucial for determining grasp poses. A
series of region-based methods succeed in extracting regional features and
enhancing grasp detection quality. However, faced with a cluttered scene with
multiple objects and potential collision, the definition of the grasp-relevant
region remains inconsistent among methods, and the relationship between grasps
and regional spaces remains incompletely investigated. In this paper, from a
novel region-aware and grasp-centric viewpoint, we propose Normalized Grasp
Space (NGS), unifying the grasp representation within a normalized regional
space. The relationship among the grasp widths, region scales, and gripper
sizes is considered and empowers our method to generalize to grippers and
scenes with different scales. Leveraging the characteristics of the NGS, we
find that 2D CNNs are surprisingly underestimated for complicated 6-DoF grasp
detection tasks in clutter scenes and build a highly efficient Region-aware
Normalized Grasp Network (RNGNet). Experiments conducted on the public
benchmark show that our method achieves the best grasp detection results
compared to the previous state-of-the-arts while attaining a real-time
inference speed of approximately 50 FPS. Real-world cluttered scene clearance
experiments underscore the effectiveness of our method with a higher success
rate than other methods. Further human-to-robot handover and moving object
grasping experiments demonstrate the potential of our proposed method for
closed-loop grasping in dynamic scenarios.
