Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking

• URL: http://arxiv.org/abs/2512.00052v1
• Date: Wed, 19 Nov 2025 12:44:31 GMT
• Title: Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking
• Authors: Can Lei, Nuno Gracias, Rafael Garcia, Hayat Rajani, Huigang Wang,
• Abstract summary: Side-scan sonar mosaicking plays a crucial role in large-scale seabed mapping.<n>Existing rigid or affine registration methods fail to model complex deformations.<n>We propose a coarse-to-fine hierarchical non-rigid registration framework tailored for large-scale side-scan sonar images.
• Score: 0.1631115063641726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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