解决水面声呐成像中的多路径干扰,重建水下物体三维模型
Object Modeling from Underwater Forward-Scan Sonar Imagery with Sea-Surface Multipath
- 基于平面水面假设,建模并剔除多路径造成的图像伪影
- 通过镜像边界等视觉线索提升三维重建精度,6次迭代内完成优化
- 适用于真实与合成数据,对非平面界面也有鲁棒性
本文提出一种从已知姿态的二维前向扫描声呐图像中进行三维水下物体建模的优化方法。针对靠近海面成像的物体,关键贡献在于消除由气-水界面引起的多路径伪影。直接目标回波图像通常被鬼影和镜像成分(由多路径传播产生)污染。在假设气-水界面为平面的前提下,我们建模、定位并剔除每视图中受污染的物体区域,从而避免三维形状失真。此外,利用镜像边界在特定声呐姿态下的显著特征,进一步提升三维建模精度。优化过程通过迭代调整三角网格顶点实现,以最小化实际数据与合成视图之间的差异。首先计算数据与合成视图间物体区域的二维运动场,再推导出三角片中心的三维运动,最终更新模型顶点。三维模型初始值由同一数据集上先前空间雕刻法的结果提供。相同参数应用于两个真实数据集、一个真实-合成混合数据集以及由真实实验发现指导的计算机生成数据,探索非平面气-水界面的影响。结果表明,约六次迭代即可获得精细化三维模型。
原文摘要 · Abstract (English)
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.
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