Surface reflectance and normal estimation from photometric stereo

In this paper, we propose a new photometric stereo method for estimating diffuse reflection and surface normal from color images. Using dichromatic reflection model, we introduce surface chromaticity as a matching invariant for photometric stereo, which serves as the foundation of the theory of this paper. An extremely simple and robust reflection components separation method is proposed based on the invariant. Our separation method differs from most previous methods which either assume dependencies among pixels or require segmentation. We also show that a linear relationship between the image color and the surface normal can be obtained based on this invariant. The linear relationship turns the surface normal estimation problem into a linear system that can be solved exactly or via least-squares optimization. We present experiments on both synthetic and real images, which demonstrate the effectiveness of our method.

► We separate reflection components based on the surface chromaticity invariant.
► We estimate surface normal for non-Lambertian surfaces.
► We derive a linear relationship between the image color and the surface normal.
► We demonstrate accurate surface reconstruction of specular objects.