FHLight: A novel method of indoor scene illumination estimation using improved loss function

In augmented reality tasks, especially in indoor scenes, achieving illumination consistency between virtual objects and real environments is a critical challenge. Currently, mainstream methods are illumination parameters regression and illumination map generation. Among these two categories of methods, few works can effectively recover both high-frequency and low-frequency illumination information within indoor scenes. In this work, we argue that effective restoration of low-frequency illumination information forms the foundation for capturing high-frequency illumination details. In this way, we propose a novel illumination estimation method called FHLight. Technically, we use a low-frequency spherical harmonic irradiance map (LFSHIM) restored by the low-frequency illumination regression network (LFIRN) as prior information to guide the high-frequency illumination generator (HFIG) to restore the illumination map. Furthermore, we suggest an improved loss function to optimize the network training procedure, ensuring that the model accurately restores both low-frequency and high-frequency illumination information within the scene. We compare FHLight with several competitive methods, and the results demonstrate significant improvements in metrics such as RMSE, si-RMSE, and Angular error. In addition, visual experiments further confirm that FHLight is capable of generating scene illumination maps with genuine frequencies, effectively resolving the illumination consistency issue between virtual objects and real scenes. The code is available at https://github.com/WA-tyro/FHLight.git.