RDDP: Reliable Detection and Description of Interest Points

Recent works have paid much attention on learning repeatable maps for detection and description of interest points. However, there are always some components which is repeatable but not discriminative in reality. Training on the whole images with these components will lead to poor matching performance. Thus, we propose a self-supervised network with a new branch which can eliminate the negative influence of unreliable image components. We also design a loss function including a part calculated with Average Precision (AP) to make full use of the reliability branch. Evaluations on HPatches dataset show that the proposed method achieves competitive results compared with state-of-the-art methods.