One Shot Photometric Stereo from Reflectance Classification

3D reconstruction of object shape is one of the most important problem in the field of computer vision. Especially, estimation of normal orientation of object surface is useful for photo-realistic image rendering. For this estimation, the photometric stereo is often used. However, it requires multiple images taken under different lighting conditions in the same pose, and thus, we cannot apply it to moving objects in general. In this paper, we propose a one-shot photometric stereo for estimating surface normal of moving objects with arbitrary textures. In our method, we estimate surface orientation and reflectance property simultaneously. For this objective, reflectance data set is used for decreasing DoF (Degree of Freedom) of estimation. In addition, we classify reflectance property of an input image into limited number of classes. By using the prior knowledge, our method can estimate surface orientation and reflectance property, even if input information is not sufficient for the estimation.