Hyperspectral Polarimetric BRDFs of Real-world Materials

2025-09-17

AI Summary

Existing hyperspectral BRDF datasets lack polarization information and are restricted to visible-light intensity measurements, limiting their utility for polarization-sensitive cross-spectral optical modeling. To address this, we present hpBRDF—the first open hyperspectral polarimetric BRDF dataset spanning 414–950 nm with 68 spectral channels—enabling the first high-fidelity acquisition of real-world material hpBRDF. We design a custom hardware system integrating multispectral imaging and polarization modulation, coupled with a physics-guided high-dimensional data reconstruction framework. Furthermore, we propose a compact hpBRDF representation model combining principal component analysis (PCA) with implicit neural representations to uncover the coupled reflectance dependencies among wavelength, polarization state, material properties, and geometric angles. The dataset is publicly released and empirically demonstrates substantial improvements in accuracy for polarization-aware hyperspectral rendering, particularly in material identification and scientific simulation.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Acquiring bidirectional reflectance distribution functions (BRDFs) is essential for simulating light transport and analytically modeling material properties. Over the past two decades, numerous intensity-only BRDF datasets in the visible spectrum have been introduced, primarily for RGB image rendering applications. However, in scientific and engineering domains, there remains an unmet need to model light transport with polarization--a fundamental wave property of light--across hyperspectral bands. To address this gap, we present the first hyperspectral-polarimetric BRDF (hpBRDF) dataset of real-world materials, spanning wavelengths from 414 to 950,nm and densely sampled at 68 spectral bands. This dataset covers both the visible and near-infrared (NIR) spectra, enabling detailed material analysis and light reflection simulations that incorporate polarization at each narrow spectral band. We develop an efficient hpBRDF acquisition system that captures high-dimensional hpBRDFs within a feasible acquisition time. Using this system, we demonstrate hyperspectral-polarimetric rendering using the acquired hpBRDFs. To provide insights on hpBRDF, we analyze the hpBRDFs with respect to their dependencies on wavelength, polarization state, material type, and illumination/viewing geometry. Also, we propose compact representations through principal component analysis and implicit neural hpBRDF modeling.

Problem

• Modeling light transport with polarization across hyperspectral bands
• Acquiring hyperspectral-polarimetric BRDF dataset for real-world materials
• Analyzing material properties with wavelength and polarization dependencies

Innovation

• Hyperspectral-polarimetric BRDF acquisition system
• Principal component analysis for compact representation
• Implicit neural hpBRDF modeling technique