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

TL;DR

This paper introduces a hierarchical non-rigid registration framework for side-scan sonar mosaicking that effectively models complex deformations, improving accuracy and robustness over existing methods.

Contribution

It proposes a novel coarse-to-fine approach combining global initialization, superpixel segmentation, and pretrained neural networks for robust non-rigid registration of sonar images.

Findings

Outperforms state-of-the-art methods in accuracy and structural consistency.

Demonstrates robustness on large-scale, complex sonar datasets.

Achieves smooth and dense deformation fields for improved 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.