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。

Contact2Grasp: 基于手物接触限制的三维握取合成

Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint

Grasping and manipulating objects is an important human skill. Since
hand-object contact is fundamental to grasping, capturing it can lead to
important insights. However, observing contact through external sensors is
challenging because of occlusion and the complexity of the human hand. We
present ContactDB, a novel dataset of contact maps for household objects that
captures the rich hand-object contact that occurs during grasping, enabled by
use of a thermal camera. Participants in our study grasped 3D printed objects
with a post-grasp functional intent. ContactDB includes 3750 3D meshes of 50
household objects textured with contact maps and 375K frames of synchronized
RGB-D+thermal images. To the best of our knowledge, this is the first
large-scale dataset that records detailed contact maps for human grasps.
Analysis of this data shows the influence of functional intent and object size
on grasping, the tendency to touch/avoid 'active areas', and the high frequency
of palm and proximal finger contact. Finally, we train state-of-the-art image
translation and 3D convolution algorithms to predict diverse contact patterns
from object shape. Data, code and models are available at
this https URL

使用红外传感器捕捉和记录了 50 种日常物品的手 - 物体接触点图，研究表明功能意图和物品大小对握取有影响，多种算法用于预测手 - 物体接触点图。