ICTPolarReal: A Polarized Reflection and Material Dataset of Real World Objects

TL;DR

This paper introduces a comprehensive polarized reflection dataset of real-world objects to improve inverse rendering models, enabling better material understanding and generalization to real-world scenarios.

Contribution

The creation of a large-scale, multi-dimensional polarized reflection dataset of real objects, facilitating advancements in inverse rendering and material modeling.

Findings

Enhanced material separation accuracy

Improved illumination fidelity in models

Better geometric consistency in reconstructions

Abstract

Accurately modeling how real-world materials reflect light remains a core challenge in inverse rendering, largely due to the scarcity of real measured reflectance data. Existing approaches rely heavily on synthetic datasets with simplified illumination and limited material realism, preventing models from generalizing to real-world images. We introduce a large-scale polarized reflection and material dataset of real-world objects, captured with an 8-camera, 346-light Light Stage equipped with cross/parallel polarization. Our dataset spans 218 everyday objects across five acquisition dimensions-multiview, multi-illumination, polarization, reflectance separation, and material attributes-yielding over 1.2M high-resolution images with diffuse-specular separation and analytically derived diffuse albedo, specular albedo, and surface normals. Using this dataset, we train and evaluate state-of-the-art inverse and forward rendering models on intrinsic decomposition, relighting, and sparse-view 3D reconstruction, demonstrating significant improvements in material separation, illumination fidelity, and geometric consistency. We hope that our work can establish a new foundation for physically grounded material understanding and enable real-world generalization beyond synthetic training regimes. Project page: https://jingyangcarl.github.io/ICTPolarReal/