DGIM: Cascaded Dynamic Data Generation for Robust Cross-Modal Image Matching

To address the challenges posed by extensive modality differences in image matching tasks, this paper proposes a cascaded learning framework. It guides the optimization of an end-to-end matching model via a dynamic data engine, which can provide sufficient cross-modal training data to support the model’s full adaptation to cross-modal features. The data engine integrates a random homography transformation module and a lightweight image generation model, enabling the online synthesis of cross-modal image pairs with geometric variations and diverse styles. This provides the matching model with rich cross-modal stimulation. The matching model adopts a hybrid architecture combining a CNN backbone and Transformer attention mechanisms, which integrates multi-scale local feature extraction with global context modeling. By adopting the proposed stepwise aggregation strategy, the efficiency of feature extraction is well ensured. Subsequently, a coarse-to-fine matching strategy is employed to achieve high accuracy and robustness of feature alignment. Comprehensive experiments on both self-collected and public cross-modal image matching datasets demonstrate that the proposed DGIM outperforms existing state-of-the-art approaches in cross-modal matching performance while achieving a good balance between efficiency and effectiveness. It also exhibits broad practical potential across multiple fields and scenes. This work provides novel solutions and evaluation benchmarks for cross-modal image matching tasks. The code and testing dataset will be made publicly available at https://github.com/LotrL/DGIM.