Illumination learning from a single image with unknown shape and texture

In this paper, we develop a method for learning illumination from a single image, which can benefit illumination-invariant algorithms in computer vision and image-based rendering in graphics. Illumination learning has been widely studied, yet still has some shortcomings such as the restriction of Lambertian surfaces and the prerequisite of known shape or texture. Our method can adaptively learn illumination from images of vehicles with unknown shape and texture. We formulate the illumination model with both diffusion and specularity components using a frequency-space representation, and adopt an iterative strategy to estimate lighting, shape, and texture under a joint energy function. Using our method, we can perform de-lighting and re-lighting on input images, and render other 3D models with learned illumination. Experimental results show that our method can work in a wide range of real-world environments with both indoor and outdoor illumination conditions.