Polarization and Hyperspectral Imaging for Synthetic Scene Rendering

Polarization and spectral imaging technology has wide application prospects and economic value in environmental detection, target recognition, remote sensing detection and industrial detection. However, the acquisition of hyperspectral or spectro-polarimetric imaging data is difficult and expensive in general. This study aims to develop a synthetic thermal imaging dataset using computer simulation. The study seeks to explore the simulation performance of Monte-Carlo path tracing algorithm in the fields of spectroscopy and thermal imaging. The goal is to provide a novel tool for effective and accurate dataset generation for thermal imaging neural networks training.