Abstract: At present,most scene matching algorithms use traditional feature point matching algorithms.The algorithm process consists of feature detection and feature matching.For weak texture scenes,both the accuracy and matching success rate are low.UFormer proposes an end-to-end solution to complete Transformer-based feature extraction and matching operations,and uses an attention mechanism to improve the algorithm’s ability to deal with weak texture scenes.Inspired by the U-Net architecture,UFormer constructs the sub-pixel-level mapping relationship of images from coarse to fine based on the encoder-decoder structure.The encoder uses the self-cross attention overlapping structure to detect and extract the relevant features of each scale of the image,establish feature connections,and perform down-sampling for coarse-grained matching to provide the initial position.The decoder upsamples,restores image resolution,fuses attentional feature maps at each scale,achieves matching at a fine-grained level,and refines the matching results to sub-pixel precision in a desired way.Introduce the ground-truth homography matrix to calculate the Euclidean distance feedback loss of coarse and fine-grained matching point-to-coordinates,and supervise the learning of the network.UFormer integrates feature detection and feature matching,with a simpler structure,which improves real-time performance while ensuring accuracy,and has the ability to deal with weak texture scenes to a certain extent.On the collected drone trajectory data set,compared with SIFT,the coordinate accuracy improves by 0.416 pixels,the matching time decreases to 0.106 s,and the matching success rate for weak texture scene images is higher.

Key words: Scene matching, Attention mechanism, Visual localization, Deep learning