  We present a method to estimate lighting from a single image of an indoor
scene. Previous work has used an environment map representation that does not
account for the localized nature of indoor lighting. Instead, we represent
lighting as a set of discrete 3D lights with geometric and photometric
parameters. We train a deep neural network to regress these parameters from a
single image, on a dataset of environment maps annotated with depth. We propose
a differentiable layer to convert these parameters to an environment map to
compute our loss; this bypasses the challenge of establishing correspondences
between estimated and ground truth lights. We demonstrate, via quantitative and
qualitative evaluations, that our representation and training scheme lead to
more accurate results compared to previous work, while allowing for more
realistic 3D object compositing with spatially-varying lighting.
