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.

本篇研究通过介绍一种新的中间变量来提高三维抓取合成的效率，以解决现有方法想要直接从物体映射到抓取姿势的非光滑和低效问题，并提出了一种基于接触区域的因子图形式来描述抓取姿态生成过程，并通过实验验证了优化方法的有效性。