@conference {lv2023nonlambertian,
	title = {Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition},
	booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI)},
	year = {2023},
	month = {aug},
	pages = {1249{\textendash}1257},
	address = {Macau, SAR China},
	abstract = {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 non-Lambertian spectral reflectances. Specifically, we present a spectral reflectance decomposition model to disentangle the spectral reflectance into a geometric component and a spectral component. 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.},
	isbn = {978-1-956792-03-4},
	author = {Lv, Jipeng and Guo, Heng and Chen, Guanying and Liang, Jinxiu and Shi, Boxin}
}
