  Accurately estimating scene lighting is critical for applications such as
mixed reality. Existing works estimate illumination by generating illumination
maps or regressing illumination parameters. However, the method of generating
illumination maps has poor generalization performance and parametric models
such as Spherical Harmonic (SH) and Spherical Gaussian (SG) fall short in
capturing high-frequency or low-frequency components. This paper presents
MixLight, a joint model that utilizes the complementary characteristics of SH
and SG to achieve a more complete illumination representation, which uses SH
and SG to capture low-frequency ambient and high-frequency light sources
respectively. In addition, a special spherical light source sparsemax
(SLSparsemax) module that refers to the position and brightness relationship
between spherical light sources is designed to improve their sparsity, which is
significant but omitted by prior works. Extensive experiments demonstrate that
MixLight surpasses state-of-the-art (SOTA) methods on multiple metrics. In
addition, experiments on Web Dataset also show that MixLight as a parametric
method has better generalization performance than non-parametric methods.
