Reflectance Estimation

Reflectance estimation aims to determine how surfaces reflect light, a crucial step in various computer vision and graphics tasks. Current research focuses on improving the accuracy and robustness of reflectance estimation from images, often employing neural networks, including diffusion models and mixtures-of-experts, to address challenges like shadows, specularities, and the ill-posed nature of the inverse rendering problem. These advancements are driving progress in applications such as 3D facial reconstruction, robotic grasping, and image harmonization, where accurate material properties are essential for realistic rendering and effective interaction with the environment. The development of more efficient and accurate methods is continuously improving the quality and realism of computer-generated imagery and robotic perception.