  We propose a real-time method to estimate spatiallyvarying indoor lighting
from a single RGB image. Given an image and a 2D location in that image, our
CNN estimates a 5th order spherical harmonic representation of the lighting at
the given location in less than 20ms on a laptop mobile graphics card. While
existing approaches estimate a single, global lighting representation or
require depth as input, our method reasons about local lighting without
requiring any geometry information. We demonstrate, through quantitative
experiments including a user study, that our results achieve lower lighting
estimation errors and are preferred by users over the state-of-the-art. Our
approach can be used directly for augmented reality applications, where a
virtual object is relit realistically at any position in the scene in
real-time.
