  Illumination estimation from a single image is critical in 3D rendering and
it has been investigated extensively in the computer vision and computer
graphic research community. On the other hand, existing works estimate
illumination by either regressing light parameters or generating illumination
maps that are often hard to optimize or tend to produce inaccurate predictions.
We propose Earth Mover Light (EMLight), an illumination estimation framework
that leverages a regression network and a neural projector for accurate
illumination estimation. We decompose the illumination map into spherical light
distribution, light intensity and the ambient term, and define the illumination
estimation as a parameter regression task for the three illumination
components. Motivated by the Earth Mover distance, we design a novel spherical
mover's loss that guides to regress light distribution parameters accurately by
taking advantage of the subtleties of spherical distribution. Under the
guidance of the predicted spherical distribution, light intensity and ambient
term, the neural projector synthesizes panoramic illumination maps with
realistic light frequency. Extensive experiments show that EMLight achieves
accurate illumination estimation and the generated relighting in 3D object
embedding exhibits superior plausibility and fidelity as compared with
state-of-the-art methods.
