Shape from Polarization of Thermal Emission and Reflection

TL;DR

This paper introduces a novel LWIR polarization-based shape estimation method that models emission and reflection effects, utilizing both model-based and neural network approaches, validated on a new real-world dataset.

Contribution

It presents a comprehensive polarization model for LWIR, incorporates a learning-based surface normal estimation, and introduces ThermoPol, the first real-world LWIR SfP dataset.

Findings

High accuracy in surface normal estimation across various materials

Effective modeling of emission and reflection effects in LWIR polarization

Demonstrated broad applicability of the method in real-world scenarios

Abstract

Shape estimation for transparent objects is challenging due to their complex light transport. To circumvent these difficulties, we leverage the Shape from Polarization (SfP) technique in the Long-Wave Infrared (LWIR) spectrum, where most materials are opaque and emissive. While a few prior studies have explored LWIR SfP, these attempts suffered from significant errors due to inadequate polarimetric modeling, particularly the neglect of reflection. Addressing this gap, we formulated a polarization model that explicitly accounts for the combined effects of emission and reflection. Based on this model, we estimated surface normals using not only a direct model-based method but also a learning-based approach employing a neural network trained on a physically-grounded synthetic dataset. Furthermore, we modeled the LWIR polarimetric imaging process, accounting for inherent systematic errors to ensure accurate polarimetry. We implemented a prototype system and created ThermoPol, the first real-world benchmark dataset for LWIR SfP. Through comprehensive experiments, we demonstrated the high accuracy and broad applicability of our method across various materials, including those transparent in the visible spectrum.