Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath
<|reference_start|>Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath: We propose an optimization technique for 3-D underwater object modeling from 2-D forward-scan sonar images at known poses. A key contribution, for objects imaged in the proximity of the sea surface, is to resolve the multipath artifacts due to the air-water interface. Here, the object image formed by the direct target backscatter is almost always corrupted by the ghost and sometimes by the mirror components (generated by the multipath propagation). Assuming a planar air-water interface, we model, localize, and discard the corrupted object region within each view, thus avoiding the distortion of recovered 3-D shape. Additionally, complementary visual cues from the boundary of the mirror component, distinct at suitable sonar poses, are employed to enhance the 3-D modeling accuracy.   The optimization is implemented as iterative shape adjustment by displacing the vertices of triangular patches in the 3-D surface mesh model, in order to minimize the discrepancy between the data and synthesized views of the 3-D object model. To this end, we first determine 2-D motion fields that align the object regions in the data and synthesized views, then calculate the 3-D motion of triangular patch centers, and finally the model vertices. The 3-D model is initialized with the solution of an earlier space carving method applied to the same data. The same parameters are applied in various experiments with 2 real data sets, mixed real-synthetic data set, and computer-generated data guided by general findings from a real experiment, to explore the impact of non-flat air-water interface. The results confirm the generation of a refined 3-D model in about half-dozen iterations.<|reference_end|>
@article{liu2024object,
    title={Object Modeling from Underwater Forward-Scan Sonar Imagery with
  Sea-Surface Multipath},
    author={Yuhan Liu and Shahriar Negaharipour},
    journal={arXiv preprint arXiv:2409.06815},
    year={2024},
    doi={10.1109/JOE.2024.3412268},
    archivePrefix={arXiv},
    eprint={2409.06815},
    primaryClass={cs.CV}
}