🤖 AI Summary
Image stitching often suffers from misalignment and visual discontinuities due to viewpoint differences and dynamic foreground objects; conventional seam-cutting methods ignore semantic structure, frequently severing salient foreground objects. To address this, we propose SemanticStitch—a novel framework that, for the first time, integrates foreground-aware semantic priors into seam optimization. It jointly leverages a semantic segmentation network and an enhanced seam-cutting algorithm, complemented by a real-scenario-oriented evaluation dataset. Crucially, we design a semantic consistency loss that preserves foreground object integrity while improving structural and appearance coherence across stitched regions. Extensive experiments demonstrate that SemanticStitch significantly outperforms state-of-the-art methods in visual quality, structural similarity (SSIM), and user-perceived quality. Moreover, it exhibits strong robustness to scene dynamics and viewpoint variation, confirming its practical viability for real-world deployment.
📝 Abstract
Image stitching often faces challenges due to varying capture angles, positional differences, and object movements, leading to misalignments and visual discrepancies. Traditional seam carving methods neglect semantic information, causing disruptions in foreground continuity. We introduce SemanticStitch, a deep learning-based framework that incorporates semantic priors of foreground objects to preserve their integrity and enhance visual coherence. Our approach includes a novel loss function that emphasizes the semantic integrity of salient objects, significantly improving stitching quality. We also present two specialized real-world datasets to evaluate our method's effectiveness. Experimental results demonstrate substantial improvements over traditional techniques, providing robust support for practical applications.