Learning to Match Ground Camera Image and UAV 3-D Model-Rendered Image Based on Siamese Network with Attention Mechanism

Different domain image sensors or imaging mechanisms provide cross-domain images when sensing the same scene. There is a domain shift between cross-domain images so that the image gap between different domains is the major challenge for measuring the similarity of the feature descriptors extracted from different domain images. Specifically, matching ground camera images and unmanned aerial vehicle (UAV) 3-D model-rendered images, which are two kinds of extremely challenging cross-domain images, is a way to establish indirectly the spatial relationship between 2-D and 3-D spaces. This provides a solution for the virtual-real registration of augmented reality (AR) in outdoor environments. However, during matching, handcrafted descriptors and existing learning-based feature descriptors limit the rendered images. In this letter, first, to learn robust and invariant 128-D local feature descriptors for ground camera and rendered images, we present a novel network structure, SiamAM-Net, which embeds the autoencoders with an attention mechanism into the Siamese network. Then, to narrow the gap between the cross-domain images during the optimizing of SiamAM-Net, we design an adaptive margin for the loss function. Finally, we match the ground camera-rendered images by using the learned local feature descriptors and explore the outdoor AR virtual-real registration. Experiments show that the local feature descriptors, learned by SiamAM-Net, are robust and achieve state-of-the-art retrieval performance on the cross-domain image data set of ground camera and rendered images. In addition, several outdoor AR applications also demonstrate the usefulness of the proposed outdoor AR virtual-real registration.