论文标题
NeuralMPS：非lambertian多光谱光度立体声通过光谱反射分解
NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition
论文作者
论文摘要
多光谱光度立体声（MPS）旨在从在多光谱照明下捕获的单光谱图像中恢复场景的表面正常。现有的MPS方法采用了兰伯特反射模型，以使问题可解决，但它极大地限制了其在现实世界中的应用。在本文中，我们提出了一个名为NeuralMP的深神经网络，以解决一般非lambertian光谱反射率下的MPS问题。具体而言，我们提出了一个光谱反射分解（SRD）模型，以将光谱反射率分解为几何成分和光谱成分。通过这种分解，我们表明具有均匀材料的表面的MPS问题相当于未知光强度的常规光度立体声（CPS）。通过这种方式，神经mps通过利用精心研究的非lambertian CPS方法来减少非陆层MPS问题的难度。合成场景和现实场景的实验证明了我们方法的有效性。
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.
