Three-dimentional reconstruction of underwater side-scan sonar images based on shape-from-shading and monocular depth fusion

Modern marine research requires high-precision three-dimensional (3D) underwater data. Underwater environments experience severe visible light attenuation, which leads to inferior imaging compared with air. In contrast, sound waves are less affected underwater; hence side-scan sonar is used for underwater 3D reconstruction. Typically, the shape-from-shading algorithm (SfS) is widely used to reconstruct surface normal or heights from side-scan sonar images. However, this approach has challenges because of global information loss and noise. To address these issues, this study introduces a surface-normal fusion method. Specifically, we propose a frequency separation SfS algorithm using a discrete cosine transform, which provides a surface-normal map with less noise. We then fuse the surface-normal map with a novel depth estimation network to achieve high-precision 3D reconstruction of underwater side-scan sonar images. We conducted experiments on synthetic, NYU-depth-v2, and real side-scan sonar datasets to demonstrate the effectiveness of the proposed method.