  Lighting estimation from a single image is an essential yet challenging task
in computer vision and computer graphics. Existing works estimate lighting by
regressing representative illumination parameters or generating illumination
maps directly. However, these methods often suffer from poor accuracy and
generalization. This paper presents Geometric Mover's Light (GMLight), a
lighting estimation framework that employs a regression network and a
generative projector for effective illumination estimation. We parameterize
illumination scenes in terms of the geometric light distribution, light
intensity, ambient term, and auxiliary depth, and estimate them as a pure
regression task. Inspired by the earth mover's distance, we design a novel
geometric mover's loss to guide the accurate regression of light distribution
parameters. With the estimated lighting parameters, the generative projector
synthesizes panoramic illumination maps with realistic appearance and
frequency. Extensive experiments show that GMLight achieves accurate
illumination estimation and superior fidelity in relighting for 3D object
insertion.
