Illumination Estimation

Illumination estimation focuses on accurately determining the lighting conditions in images and scenes, aiming to separate illumination effects from object properties like reflectance. Current research emphasizes robust feature extraction under varying illumination, employing techniques like intrinsic image decomposition, photometric bundle adjustment, and neural networks (including diffusion models and convolutional neural networks) to achieve this. These advancements are crucial for improving the accuracy and reliability of computer vision tasks such as object recognition, 3D reconstruction, and image editing, particularly in challenging real-world scenarios with diverse and dynamic lighting.