MixLight: Borrowing the Best of both Spherical Harmonics and Gaussian Models

arXiv:2404.12768v1 Announce Type: new
Abstract: 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 …

abstract applications arxiv best of cs.ai cs.cv cs.gr however lighting maps mixed mixed reality parameters parametric performance reality type