Lighting Estimation

Lighting estimation aims to reconstruct a scene's illumination from images, enabling realistic virtual object insertion and improved image editing. Current research focuses on developing robust methods using neural networks, including neural radiance fields (NeRFs), diffusion models, and graph convolutional networks, often incorporating physically-based rendering principles and addressing challenges like limited field-of-view and handling high dynamic range (HDR) data. Improved perceptual evaluation frameworks are also being developed to better align algorithmic performance with human perception. These advancements have significant implications for augmented reality, computer graphics, and other vision applications requiring accurate and realistic lighting representation.