  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.
