Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset

Yujin Jeon, Eunsue Choi, Youngchan Kim, Yunseong Moon, Khalid Omer, Felix Heide, Seung-Hwan Baek; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22098-22108

Image datasets are essential not only in validating existing methods in computer vision but also in developing new methods. Many image datasets exist consisting of trichromatic intensity images taken with RGB cameras which are designed to replicate 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 there are previous spectro-polarimetric datasets they have insufficient object diversity limited illumination conditions linear-only polarization data and inadequate image count. Here we introduce two spectro-polarimetric datasets consisting of trichromatic Stokes images and hyperspectral Stokes images. These 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.