基于深度学习特征的二维图像匹配算法综述

doi:10.19678/j.issn.1000-3428.0068580

摘要/Abstract

摘要： 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对应关系的算法，是计算机视觉领域中的基础且重要的问题，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的发展，基于深度学习的二维匹配算法在特征提取、特征描述以及特征匹配三方面不断进行改进，其性能在匹配精度、鲁棒性等方面远超传统算法，取得了丰硕的成果。尽管目前已存在大量关于二维图像匹配算法的综述，但是仍缺少对最新图像匹配算法发展的归纳。因此，对计算机视觉领域的二维图像匹配任务近八年基于深度学习特征的二维匹配方法进行总结，从基于局部特征的检测和描述的双阶段匹配方法、基于联合特征检测和描述的匹配方法和无特征检测的匹配方法三方面详细介绍了二维图像匹配算法的发展过程、分类方法、性能评价指标，并对各类方法的优点及局限性进行归纳。然后，对二维图像匹配算法的应用场景进行介绍，阐述了二维图像匹配的进展对其应用领域的影响。最后，对二维图像匹配算法的发展趋势进行总结和展望。

Abstract: The objective of image matching is to establish correspondences between similar structures across two or more images. This task stands as a fundamental and crucial issue in computer vision and its applications span widely across robotics, remote sensing, and autonomous driving. With the advancement of deep learning in recent years, 2D image matching algorithms based on deep learning have seen continuous improvements in feature extraction, description and matching. The performance of these algorithms, in terms of matching accuracy and robustness, significantly surpasses traditional methods, yielding substantial achievements. Although there exist numerous reviews on 2D image matching algorithms, the latest developments have yet to be summarized. Therefore, a comprehensive review of deep-learning-feature-based 2D image matching methods over the past eight years is carried out. This study provides a detailed introduction to the development, classification methods, and performance evaluation metrics of 2D image matching algorithms, focusing on three aspects: local feature detection and description two-stage methods, joint feature detection and description methods and detector-free matching methods. The advantages and limitations of each method are also summarized. Subsequently, the application scenarios of 2D image matching algorithms are introduced, elucidating the impact of advancements in two-dimensional image matching on its application domains. Finally, the development trends of 2D image matching algorithms are summarized and future prospects are presented.

黄开基, 杨华. 基于深度学习特征的二维图像匹配算法综述[J]. 计算机工程, doi: 10.19678/j.issn.1000-3428.0068580.

HUANG Kaiji, YANG Hua. Review of deep-learning-feature-based methods for 2D image matching[J]. Computer Engineering, doi: 10.19678/j.issn.1000-3428.0068580.

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