ContactDB

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 https://contactdb.cc.gatech.edu.抓取与操作物体是人类的一项核心技能。手-物体接触是抓取行为的本质基础，对其进行捕捉能够获得极具价值的研究洞察。然而，借助外部传感器观测手部接触状态极具挑战——这源于遮挡现象以及人类手部结构的复杂性。我们提出ContactDB，一款面向家居物品的新型接触图谱数据集，通过热成像相机成功捕捉了抓取过程中丰富的手-物体交互接触信息。本研究中的参与者在抓取3D打印物体时，均带有明确的抓取后功能使用意图。ContactDB包含50类家居物品的3750个搭载接触图谱纹理的3D网格模型，以及37.5万帧同步RGB-D+热成像图像。据我们所知，这是首个记录人类抓取动作详细接触图谱的大规模数据集。对该数据集的分析揭示了功能意图与物体尺寸对抓取策略的影响、触摸/规避“主动区域”的行为倾向，以及手掌与近端手指接触的高频率特征。最后，我们基于当前顶尖的图像转换与3D卷积算法，实现了从物体形状预测多样化接触模式的任务。数据集、代码与模型均可通过https://contactdb.cc.gatech.edu获取。

搜集汇总

背景与挑战

背景概述

ContactDB是一个专注于抓握接触分析的数据集，通过热成像技术记录人手与家庭物品接触的详细地图，包含50个物品的3750个3D网格和大量同步图像数据。该数据集旨在研究抓握行为的影响因素，并用于训练先进算法预测接触模式，适用于计算机视觉和机器人抓取研究。