  Grasp detection of novel objects in unstructured environments is a key
capability in robotic manipulation. For 2D grasp detection problems where
grasps are assumed to lie in the plane, it is common to design a fully
convolutional neural network that predicts grasps over an entire image in one
step. However, this is not possible for grasp pose detection where grasp poses
are assumed to exist in SE(3). In this case, it is common to approach the
problem in two steps: grasp candidate generation and candidate classification.
Since grasp candidate classification is typically expensive, the problem
becomes one of efficiently identifying high quality candidate grasps. This
paper proposes a new grasp candidate generation method that significantly
outperforms major 3D grasp detection baselines. Supplementary material is
available at https://atenpas.github.io/psn/.
