  Great success has been achieved in the 6-DoF grasp learning from the point
cloud input, yet the computational cost due to the point set orderlessness
remains a concern. Alternatively, we explore the grasp generation from the
RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects
the projection of the gripper keypoints in the image space and then recover the
SE(3) poses with a PnP algorithm. A synthetic dataset based on the primitive
shape and the grasp family is constructed to examine our idea. Metric-based
evaluation reveals that our method outperforms the baselines in terms of the
grasp proposal accuracy, diversity, and the time cost. Finally, robot
experiments show high success rate, demonstrating the potential of the idea in
the real-world applications.
