  We present a CNN-based technique to estimate high-dynamic range outdoor
illumination from a single low dynamic range image. To train the CNN, we
leverage a large dataset of outdoor panoramas. We fit a low-dimensional
physically-based outdoor illumination model to the skies in these panoramas
giving us a compact set of parameters (including sun position, atmospheric
conditions, and camera parameters). We extract limited field-of-view images
from the panoramas, and train a CNN with this large set of input image--output
lighting parameter pairs. Given a test image, this network can be used to infer
illumination parameters that can, in turn, be used to reconstruct an outdoor
illumination environment map. We demonstrate that our approach allows the
recovery of plausible illumination conditions and enables photorealistic
virtual object insertion from a single image. An extensive evaluation on both
the panorama dataset and captured HDR environment maps shows that our technique
significantly outperforms previous solutions to this problem.
