High-Resolution 3D Thermal Mapping: From Dual-Sensor Calibration to Thermally Enriched Points Cloud

Thermal imaging is increasingly applied in remote sensing to identify material degradation, monitor structural integrity, and support energy diagnostics. However, its broader adoption is limited by the low spatial resolution of thermal sensors compared to RGB cameras. This study proposes a modular pipeline to generate thermally enriched 3D point clouds by fusing RGB and thermal imagery acquired simultaneously with a dual sensor unmanned aerial vehicle system. The methodology includes geometric calibration of both cameras, undistortion of images, cross-spectral feature matching, and projection of radiometric data onto the photogrammetric model (accepting a certain approximation) through a computed homography. Thermal values are extracted using a custom parser and assigned to 3D points based on visibility masks and interpolation strategies. Among twelve evaluated matching algorithms, LightGlue yielded the most accurate results. The system achieved subpixel reprojection errors and low temperature deviation from ground-truth data. A case study on photovoltaic panels demonstrates the method’s capability to identify surface anomalies and map thermal patterns with high spatial detail. Developed entirely in Python, the workflow can be integrated into Agisoft Metashape or adapted to other software. The proposed approach enables cost-effective, high-resolution thermal mapping and holds potential for civil engineering, cultural heritage conservation, and environmental monitoring applications.