SuperThermal: Matching Thermal as Visible Through Thermal Feature Exploration

Abstract

Infrared Radiation (IR) images that capture the reflected IR signals from the surrounding environment have been applied to pedestrian detection and rescue tasks, especially for thermal images reflecting the surface temperature of the objects at nighttime. However, feature detection, extraction and matching are particularly challenging on thermal images as there are few textures compared to normal RGB images, which brings in an enormous gap in introducing various computer vision tasks such as image matching and retrieval, visual localization and SLAM. In this letter, we propose to design a full pipeline to learn a reliable and robust feature detector and descriptor. Our model trained on cross-spectral stereo and KAIST dataset is able to realize a reliable and accurate matching without further annotations. The proposed method is demonstrated to have superior performance compared with both widely used hand-crafted features and features extracted from the most recent learning-based models on visual and quantitative experiments. It also shows a good generalization and robustness ability on FLIR thermal benchmark even without training or fine-tuning on it.