Robust Image Stitching with Optimal Plane

• URL: http://arxiv.org/abs/2508.05903v1
• Date: Thu, 07 Aug 2025 23:53:26 GMT
• Title: Robust Image Stitching with Optimal Plane
• Authors: Lang Nie, Yuan Mei, Kang Liao, Yunqiu Xu, Chunyu Lin, Bin Xiao,
• Abstract summary: textitRopStitch is an unsupervised deep image stitching framework with both robustness and naturalness.<n>textitRopStitch significantly outperforms existing methods, particularly in scene robustness and content naturalness.
• Score: 39.80133570371559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present \textit{RopStitch}, an unsupervised deep image stitching framework with both robustness and naturalness. To ensure the robustness of \textit{RopStitch}, we propose to incorporate the universal prior of content perception into the image stitching model by a dual-branch architecture. It separately captures coarse and fine features and integrates them to achieve highly generalizable performance across diverse unseen real-world scenes. Concretely, the dual-branch model consists of a pretrained branch to capture semantically invariant representations and a learnable branch to extract fine-grained discriminative features, which are then merged into a whole by a controllable factor at the correlation level. Besides, considering that content alignment and structural preservation are often contradictory to each other, we propose a concept of virtual optimal planes to relieve this conflict. To this end, we model this problem as a process of estimating homography decomposition coefficients, and design an iterative coefficient predictor and minimal semantic distortion constraint to identify the optimal plane. This scheme is finally incorporated into \textit{RopStitch} by warping both views onto the optimal plane bidirectionally. Extensive experiments across various datasets demonstrate that \textit{RopStitch} significantly outperforms existing methods, particularly in scene robustness and content naturalness. The code is available at {\color{red}https://github.com/MmelodYy/RopStitch}.

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