Shape, Illumination, and Reflectance from Shading
This addresses a fundamental challenge in computer vision for applications like robotics and graphics, though it builds on existing statistical methods.
The paper tackles the problem of inferring 3D scene properties like shape, reflectance, and illumination from a single 2D image by formulating it as statistical inference to find the most likely explanation, and it outperforms previous solutions to related classic computer vision problems.
A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world. Traditional methods for recovering scene properties such as shape, reflectance, or illumination rely on multiple observations of the same scene to overconstrain the problem. Recovering these same properties from a single image seems almost impossible in comparison -- there are an infinite number of shapes, paint, and lights that exactly reproduce a single image. However, certain explanations are more likely than others: surfaces tend to be smooth, paint tends to be uniform, and illumination tends to be natural. We therefore pose this problem as one of statistical inference, and define an optimization problem that searches for the *most likely* explanation of a single image. Our technique can be viewed as a superset of several classic computer vision problems (shape-from-shading, intrinsic images, color constancy, illumination estimation, etc) and outperforms all previous solutions to those constituent problems.