{"ID":2885464,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05903","arxiv_id":"2508.05903","title":"Robust Image Stitching with Optimal Plane","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}.","short_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 coa...","url_abs":"https://arxiv.org/abs/2508.05903","url_pdf":"https://arxiv.org/pdf/2508.05903v3","authors":"[\"Lang Nie\",\"Yuan Mei\",\"Kang Liao\",\"Yunqiu Xu\",\"Chunyu Lin\",\"Bin Xiao\"]","published":"2025-08-07T23:53:26Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":611196,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885464,"paper_url":"https://arxiv.org/abs/2508.05903","paper_title":"Robust Image Stitching with Optimal Plane","repo_url":"https://github.com/MmelodYy/RopStitch","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}