  We present a neural network that predicts HDR outdoor illumination from a
single LDR image. At the heart of our work is a method to accurately learn HDR
lighting from LDR panoramas under any weather condition. We achieve this by
training another CNN (on a combination of synthetic and real images) to take as
input an LDR panorama, and regress the parameters of the Lalonde-Matthews
outdoor illumination model. This model is trained such that it a) reconstructs
the appearance of the sky, and b) renders the appearance of objects lit by this
illumination. We use this network to label a large-scale dataset of LDR
panoramas with lighting parameters and use them to train our single image
outdoor lighting estimation network. We demonstrate, via extensive experiments,
that both our panorama and single image networks outperform the state of the
art, and unlike prior work, are able to handle weather conditions ranging from
fully sunny to overcast skies.
