Research on Homography Estimation Method Based on Deep Learning

With the rapid development of deep learning technology, deep learning-based homography estimation methods have received increasing attention in the field of computer vision. Homography estimation aims to derive the homography matrix describing the geometric relationship between images by analyzing feature points in the images, thereby revealing the projection transformation between one image and another. This paper first elaborates on the core concepts and principles of homography estimation, followed by an in-depth exploration of traditional feature-based methods for homography estimation. We analyze in detail the applications of supervised learning and unsupervised learning-related deep learning models in homography estimation. Furthermore, we elucidate the potential value of homography estimation in practical applications and identify current challenges in research, proposing future research directions. This paper aims to delve into and provide practical guidance to facilitate a comprehensive understanding of homography estimation methods among computer vision researchers and engineers, thereby promoting their further development and application in practical scenarios.