Image Matching: Foundations, State of the Art, and Future Directions

作者：Ming Yang, Rui Wu, Yunxuan Yang, Tao Liang, Yifan Zhang, Yixin Xie, G. Siva Prasad Reddy · 发表于：Journal of Imaging · 年份：2025 · DOI：10.3390/jimaging11100329 · 被引用次数：3 · 研究领域：Advanced Image and Video Retrieval Techniques、Advanced Neural Network Applications、Robotics and Sensor-Based Localization

Image matching plays a critical role in a wide range of computer vision applications, including object recognition, 3D reconstruction, aiming-point and six-degree-of-freedom detection for aiming devices, and video surveillance. Over the past three decades, image-matching algorithms and techniques have evolved significantly, from handcrafted feature extraction algorithms to modern approaches powered by deep learning neural networks and attention mechanisms. This paper provides a comprehensive review of image-matching techniques, aiming to offer researchers valuable insights into the evolving landscape of this field. It traces the historical development of feature-based methods and examines the transition to neural network-based approaches that leverage large-scale data and learned representations. Additionally, this paper discusses the current state of the field, highlighting key algorithms, benchmarks, and real-world applications. Furthermore, this study introduces some recent contributions to this area and outlines promising directions for future research, including H-matrix optimization, LoFTR model speedup, and performance improvements. It also identifies persistent challenges such as robustness to viewpoint and illumination changes, scalability, and matching under extreme conditions. Finally, this paper summarizes future trends for research and development in this field.