基于深度学习的图像拼接算法研究综述.

Image stitching is an important branch in computer vision and computer graphics, and has a wide range of applications in 3D imaging and other aspects. Compared with the traditional image stitching framework based on feature point detection, the image stitching framework based on deep learning has stronger scene generalization performance. Although there are many research results on image stitching based on deep learning, there is still a lack of comprehensive analysis and summary of the corresponding research. In order to facilitate the subsequent work in this field, this paper sorted out the representative results in this field in the past 10 years. Based on the comparison between traditional stitching methods and deep learning-based image stitching methods, it collated and analysed the learning strategy and model architecture design, classical model review, and dataset from the three sub-problems of homography estimation, image stitching, and image rectangling in the research field of image stitching. It summarized some features of deep learning-based image stitching research methods and summarized the current research status in the field, and prospected the future research prospects. [ABSTRACT FROM AUTHOR]