Image datasets are essential not only in validating existing methods in
computer vision but also in developing new methods. Most existing image
datasets focus on trichromatic intensity images to mimic human vision. However,
polarization and spectrum, the wave properties of light that animals in harsh
environments and with limited brain capacity often rely on, remain
underrepresented in existing datasets. Although spectro-polarimetric datasets
exist, these datasets have insufficient object diversity, limited illumination
conditions, linear-only polarization data, and inadequate image count. Here, we
introduce two spectro-polarimetric datasets: trichromatic Stokes images and
hyperspectral Stokes images. These novel datasets encompass both linear and
circular polarization; they introduce multiple spectral channels; and they
feature a broad selection of real-world scenes. With our dataset in hand, we
analyze the spectro-polarimetric image statistics, develop efficient
representations of such high-dimensional data, and evaluate spectral dependency
of shape-from-polarization methods. As such, the proposed dataset promises a
foundation for data-driven spectro-polarimetric imaging and vision research.
Dataset and code will be publicly available.

提出了两个新的光谱极化数据集：三色 Stokes 图像和高光谱 Stokes 图像。这些数据集包含线性和圆偏振；引入了多个光谱通道；并且具有广泛选择的真实世界场景。通过这些数据集，分析了光谱极化图像的统计特性，发展了高维数据的有效表示方式，并评估了形态 - 来自 - 极化方法的光谱依赖性。该数据集为数据驱动的光谱极化成像和视觉研究奠定了基础。数据集和代码将公开提供。