Utilizing deep learning for feature matching

In the field of feature matching research, utilizing deep learning in this field to overcome variations lighting condition across different images has long been a challenging task. In this study, we propose III-Net, a novel training model that addresses feature matching using our proposed double-triplet loss with softmax loss. This approach effectively enhances the overall performance and stability of feature matching. Unlike previous deep learning architectures, III-Net specifically handles scenarios with significant variations in lighting conditions during feature matching. As a result of this additional processing, our approach demonstrates superior performance in real-life scenarios compared to all previous research outcomes.