  3D grasp synthesis generates grasping poses given an input object. Existing
works tackle the problem by learning a direct mapping from objects to the
distributions of grasping poses. However, because the physical contact is
sensitive to small changes in pose, the high-nonlinear mapping between 3D
object representation to valid poses is considerably non-smooth, leading to
poor generation efficiency and restricted generality. To tackle the challenge,
we introduce an intermediate variable for grasp contact areas to constrain the
grasp generation; in other words, we factorize the mapping into two sequential
stages by assuming that grasping poses are fully constrained given contact
maps: 1) we first learn contact map distributions to generate the potential
contact maps for grasps; 2) then learn a mapping from the contact maps to the
grasping poses. Further, we propose a penetration-aware optimization with the
generated contacts as a consistency constraint for grasp refinement. Extensive
validations on two public datasets show that our method outperforms
state-of-the-art methods regarding grasp generation on various metrics.
