{"ID":2839544,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00052","arxiv_id":"2512.00052","title":"Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking","abstract":"Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex non-linear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods fail to model such complex deformations, whereas traditional non-rigid techniques tend to overfit and lack robustness in sparse-texture sonar data. To address these challenges, we propose a coarse-to-fine hierarchical non-rigid registration framework tailored for large-scale side-scan sonar images. Our method begins with a global Thin Plate Spline initialization from sparse correspondences, followed by superpixel-guided segmentation that partitions the image into structurally consistent patches preserving terrain integrity. Each patch is then refined by a pretrained SynthMorph network in an unsupervised manner, enabling dense and flexible alignment without task-specific training. Finally, a fusion strategy integrates both global and local deformations into a smooth, unified deformation field. Extensive quantitative and visual evaluations demonstrate that our approach significantly outperforms state-of-the-art rigid, classical non-rigid, and learning-based methods in accuracy, structural consistency, and deformation smoothness on the challenging sonar dataset.","short_abstract":"Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex non-linear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods fail to model such complex deformations, whereas traditional non-rigid technique...","url_abs":"https://arxiv.org/abs/2512.00052","url_pdf":"https://arxiv.org/pdf/2512.00052v1","authors":"[\"Can Lei\",\"Nuno Gracias\",\"Rafael Garcia\",\"Hayat Rajani\",\"Huigang Wang\"]","published":"2025-11-19T12:44:31Z","proceeding":"physics.geo-ph","tasks":"[\"physics.geo-ph\",\"cs.CV\"]","methods":"[]","has_code":false}