PRISM: Polarimetric Road-surface Intelligent Sensing and Measurement Dataset

RGB cameras struggle on the road surface itself: pavement is nearly texture-less at the centimetre scale, and dry, wet, and icy asphalt can look photometrically identical. Polarization is the natural complement, since Fresnel optics ties the angle of linear polarization (AoLP) to surface-normal azimuth and the degree of linear polarization (DoLP) to refractive index, but no public dataset has made it possible to test whether this physical promise translates into measurable gains on real roads. We introduce PRISM, a polarimetric road-surface dataset of 47,098 time-synchronized frames combining trichromatic linear polarization, co-boresighted RGB, 128-channel LiDAR, and RTK-GNSS/INS, collected across proving-ground and open-road environments under clear, overcast, rainy, foggy, and snowy conditions. Dense road-surface elevation ground truth, validated against as-designed proving-ground geometry, accompanies frame-level labels over five materials and five surface states. Two benchmark tracks are defined on these data: road-surface condition classification and bird's-eye-view elevation estimation. Both share a five-variant input ablation crossing RGB, monochromatic and trichromatic polarization, and their combinations. Reference baselines turn the physical promise into a measurable one. Adding trichromatic polarization to RGB reduces elevation MAE from 3.61 to 3.26cm and improves every threshold metric, while yielding consistent gains on condition classification across backbones. The improvement is task-conditional: trichromatic resolution drives the elevation gain, where AoLP encodes geometric information that intensity cannot, and stratified evaluation localizes where polarimetric channels matter most. PRISM, together with reference baselines, evaluation and stratification scripts, and a datasheet, is publicly available at \url{https://huggingface.co/datasets/NeurIPS-2026-PRISM/PRISM-Dataset}.