Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset
This provides a foundational dataset for data-driven spectro-polarimetric imaging and vision research, addressing a gap for researchers in computer vision and imaging.
The authors tackled the lack of diverse spectro-polarimetric datasets by introducing two new datasets with trichromatic and hyperspectral Stokes images, featuring linear and circular polarization across multiple spectral channels and real-world scenes, enabling analysis of image statistics and evaluation of shape-from-polarization methods.
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.