  Image editing and compositing have become ubiquitous in entertainment, from
digital art to AR and VR experiences. To produce beautiful composites, the
camera needs to be geometrically calibrated, which can be tedious and requires
a physical calibration target. In place of the traditional multi-image
calibration process, we propose to infer the camera calibration parameters such
as pitch, roll, field of view, and lens distortion directly from a single image
using a deep convolutional neural network. We train this network using
automatically generated samples from a large-scale panorama dataset, yielding
competitive accuracy in terms of standard `2 error. However, we argue that
minimizing such standard error metrics might not be optimal for many
applications. In this work, we investigate human sensitivity to inaccuracies in
geometric camera calibration. To this end, we conduct a large-scale human
perception study where we ask participants to judge the realism of 3D objects
composited with correct and biased camera calibration parameters. Based on this
study, we develop a new perceptual measure for camera calibration and
demonstrate that our deep calibration network outperforms previous single-image
based calibration methods both on standard metrics as well as on this novel
perceptual measure. Finally, we demonstrate the use of our calibration network
for several applications, including virtual object insertion, image retrieval,
and compositing. A demonstration of our approach is available at
https://lvsn.github.io/deepcalib .
