Advancements of Image and Video Stitching Techniques: A Review

Image and video stitching techniques represent a significant research direction in the field of computer vision. They aim to generate images or videos with a wide field of view from multiple overlapping images or videos. These techniques find widespread application in virtual reality, autonomous driving, surveillance systems, digital cultural heritage preservation, and other fields. However, image and video stitching still encounter challenges, including large parallax, occlusion, weak texture, shape distortion, video jitter, and low efficiency. In recent years, these challenges have spurred the development of various new image and video stitching techniques. This work offers a comprehensive review of the latest advancements in image and video stitching techniques. First, we analyze traditional stitching methods and deep learning-based methods, discussing key techniques such as shape correction in image stitching and video stabilization. Second, we review datasets and evaluation metrics for image and video stitching tasks and analyze the performance of mainstream algorithms on representative datasets. Finally, we discuss typical application scenarios and future research directions for image and video stitching. This work aims to provide researchers with a detailed overview of advancements in image and video stitching techniques and to inspire improved stitching methods, thereby advancing the development of image and video stitching.