<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lv, Jipeng</style></author><author><style face="normal" font="default" size="100%">Guo, Heng</style></author><author><style face="normal" font="default" size="100%">Chen, Guanying</style></author><author><style face="normal" font="default" size="100%">Liang, Jinxiu</style></author><author><style face="normal" font="default" size="100%">Shi, Boxin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">aug</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Macau, SAR China</style></pub-location><pages><style face="normal" font="default" size="100%">1249–1257</style></pages><isbn><style face="normal" font="default" size="100%">978-1-956792-03-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>