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

作     者：Ji, Xinlong Zhan, Fangneng Lu, Shijian Huang, Shi-Sheng Huang, Hua 

作者机构：The School of Computing Science & Technology Beijing Institute of Technology Beijing100081 China The Max Planck Institute for Informatics Saarbrücken66123 Germany The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The School of ArtificialIntelligence Beijing Normal University Beijing100875 China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Light sources 

摘      要：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. Copyright © 2024, The Authors. All rights reserved.