Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes.

翻译：对场景中光线空间辐射度分布的建模已在诸多应用（包括视角合成）中得到广泛探索。然而，由于光谱与偏振这两类光的波动特性常被整合到三个RGB光谱波段中，且人类视觉对其不可感知，因此往往被忽视。尽管如此，这些特性蕴含着场景中丰富的材料与几何信息。本文提出对光谱偏振场——即任意光线在任意波长下的空间斯托克斯矢量分布——进行建模。我们提出神经光谱偏振场（NeSpoF），一种对给定位置、方向和波长的连续变量下物理有效斯托克斯矢量进行建模的神经表示方法。NeSpoF能够处理天然带噪声的原始测量数据，具备内存高效性，并保留物理关键信号——这些因素对于表征光谱偏振场的高维信号至关重要。为验证NeSpoF，我们首次提出包含合成场景与真实场景的多视角高光谱偏振图像数据集。该数据集采用我们自主研发的紧凑型高光谱偏振成像系统采集，该系统已针对系统不完美性进行了鲁棒性标定。我们在多样化的场景中展示了NeSpoF的能力。