Learning HDR illumination from LDR panorama images

A Comprehensive Review of Deep Learning based Illumination Estimation

“…Specifically, multiple papers have been published recently which report the deep learning models can accurately estimate the illumination parameters from digital images, and the extracted illumination information can benefit many downstream applications. For instance, (Jin et al, 2021) demonstrated that in augmented reality applications, harmonic coefficients can be inferred in real-time utilizing deep learning models, and the coefficients can estimate the light settings (multiple light sources with distinct light intensities) of the real-world environment (Marques et al, 2018). The illumination hint can also be extracted from the depth information, and vice versa.…”

“…Illumination estimation has become a critical task for building robust computer vision understanding models in real world applications and combining virtual objects with the real scenes in the simulation environments (Jin et al, 2021). The appearance of the scenes like brightness, color and shadows are determined by the illumination settings.…”

“…The calculation of the spherical harmonic coefficient is based on the idea of probability theory, that is, the use of "finite" to estimate "infinite'. (Jin et al, 2021) proposes a real-time rendering technology, belonging to the category of precomputed radiative transfer (PRT). PRT is one type of computer graphics technique used to render a scene in real time with complex light interactions being precomputed to save time widely used in the field of game graphics rendering to simulate complex scene lighting quickly and in real time.…”