Multispectral photometric stereo(MPS) aims at recovering the surface normal
of a scene from a single-shot multispectral image captured under multispectral
illuminations. Existing MPS methods adopt the Lambertian reflectance model to
make the problem tractable, but it greatly limits their application to
real-world surfaces. In this paper, we propose a deep neural network named
NeuralMPS to solve the MPS problem under general non-Lambertian spectral
reflectances. Specifically, we present a spectral reflectance
decomposition(SRD) model to disentangle the spectral reflectance into geometric
components and spectral components. With this decomposition, we show that the
MPS problem for surfaces with a uniform material is equivalent to the
conventional photometric stereo(CPS) with unknown light intensities. In this
way, NeuralMPS reduces the difficulty of the non-Lambertian MPS problem by
leveraging the well-studied non-Lambertian CPS methods. Experiments on both
synthetic and real-world scenes demonstrate the effectiveness of our method