The topic of stitching images with globally natural structures holds paramount significance, with two main goals: alignment and distortion prevention. The existing approaches exhibit the ability to align well, yet fall short in maintaining object structures. In this paper, we endeavour to safeguard the overall OBJect-level structures within images based on Global Similarity Prior (OBJ-GSP), on the basis of good alignment performance. Our approach leverages semantic segmentation models like the family of Segment Anything Model to extract the contours of any objects in a scene. Triangular meshes are employed in image transformation to protect the overall shapes of objects within images. The balance between alignment and distortion prevention is achieved by allowing the object meshes to strike a balance between similarity and projective transformation. We also demonstrate the importance of segmentation in low-altitude aerial image stitching. Additionally, we propose StitchBench, the most comprehensive image stitching benchmark by far. Extensive experimental results demonstrate that OBJ-GSP outperforms existing methods in both alignment and shape preservation. Code and dataset is publicly available at \url{https://github.com/RussRobin/OBJ-GSP}.

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