Edge-Guided Detector-Free Network for Robust and Accurate Visible-Thermal Image Matching

Recent detector-free models strive to leverage both local and global context for image matching, showcasing enhanced robustness, particularly in scenarios with weak-textured scenes. Despite these advancements, automatically establishing feature correspondences between visible and thermal images still introduces additional challenges. Differences in radiation and geometry between these modalities often result in degraded performance for the majority of existing methods. To this end, we propose edge-guided detector-free model termed EDMatcher for visible-thermal image matching. Besides local and global context in the images, EDMatcher also leverages modality-robust structural information in image edges, which demonstrates promising robustness to images with distinct modalities. Moreover, an edge-masked ground-truth matrix generation strategy is introduced during the training, which helps EDMatcher to further focus on more salient regions while leaving out texture-less regions, leading to more efficient learning. Extensive experiments show that EDMatcher has strong generalization and achieves excellent matching performances.