基于深度学习特征的二维图像匹配算法综述

Review of 2D Image Matching Algorithms Based on Deep Learning Features

图像匹配的目标是从两个或多个图像中找到相似结构之间的对应关系,是计算机视觉技术的重要基础,在机器人、遥感、自动驾驶等领域具有广泛应用。近年来随着深度学习技术的发展,基于深度学习的二维(2D)图像匹配算法在特征提取、特征描述、特征匹配3个方面不断进行改进,其性能在匹配精度、鲁棒性等方面远超传统算法,取得了重大突破。首先,总结近10年基于深度学习特征的2D图像匹配算法,将其分为基于局部特征的双阶段图像匹配、联合特征检测和描述的图像匹配、无特征检测的图像匹配3类算法,阐述这3类算法的发展过程、分类方法、性能评价指标并归纳其优点及局限性。然后,介绍2D图像匹配算法的典型应用场景,分析2D图像匹配算法的研究进展对其应用领域的影响。最后,总结并展望2D图像匹配算法的发展趋势。

The objective of image matching is to establish correspondences between similar structures across two or more images.This task is fundamental to computer vision,with applications in robotics,remote sensing,and autonomous driving.With the advancements in deep learning in recent years,Two-Dimensional(2D)image matching algorithms based on deep learning have seen regular improvements in feature extraction,description,and matching.The performance of these algorithms in terms of matching accuracy and robustness has surpassed that of traditional algorithms,leading to significant advancements.First,this study summarizes 2D image matching algorithms based on deep learning features from the past ten years and categorizes them into three types:two-stage image matching based on local features,image matching of joint detection and description,and image matching without feature detection.Second,the study details the development processes,classification methods,and performance evaluation metrics of these three categories and summarizes their advantages and limitations.Typical application scenarios of 2D image matching algorithms are then introduced,and the effects of research progress in 2D image matching on its application domains are analyzed.Finally,the study summarizes the development trends of 2D image matching algorithms and discusses future prospects.