侧扫声呐的几何驱动光声配准与视角不变反射率映射

中文摘要 AI 辅助

侧扫声呐（SSS）是大规模水下测绘的主要模态，但散斑噪声、阴影和极端视角依赖等声学复杂性严重阻碍了自动感知与跨模态对齐。传统手工设计的描述子和现代深度学习匹配器，在无3D几何约束的情况下无法弥合光学与声学图像间的物理域差距。为克服这些局限，我们提出一种用于像素级光声配准和视角不变反射率映射的新型几何驱动框架。该方法利用运动恢复结构（SfM）重建密集海底网格，作为视觉与声学域间的几何锚点；引入首次底部回波（FBR）提取算法，动态校正未校准SfM重建导致的非线性高度漂移；此外，应用逆朗伯模型和双高斯权重函数分离固有海底反射率，有效抵消斜距传播损失和几何视角依赖。通过将这些分离的声学属性与光学像素确定性关联，我们的流程生成高精度、严格配准的多模态数据集。这种自动化的物理引导方法无需人工标注，为底栖生境测绘中的高级自监督学习铺平了道路。

英文摘要

Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping, yet automated perception and cross-modal alignment are severely bottlenecked by acoustic complexities such as speckle noise, shadows, and extreme viewpoint dependencies. Traditional handcrafted descriptors and modern deep learning matchers fail to bridge the physical domain gap between optical and acoustic imagery without 3D geometric constraints. To overcome these limitations, we propose a novel geometry-driven framework for pixel-level opti-acoustic co-registration and view-invariant reflectivity mapping. Our method utilizes Structure-from-Motion (SfM) to reconstruct a dense 3D seafloor mesh, acting as a geometric anchor between the visual and acoustic domains. We introduce a First Bottom Return (FBR) extraction algorithm to dynamically correct non-linear altitude drift caused by uncalibrated SfM reconstruction. Furthermore, we apply an inverse Lambertian model and a dual-Gaussian weighting function to isolate the intrinsic seabed reflectivity, effectively neutralizing slant-range propagation loss and geometric view-dependence. By deterministically associating these isolated acoustic properties with optical pixels, our pipeline generates highly accurate, strictly co-registered multi-modal datasets. This automated, physics-guided approach eliminates the need for manual annotation and paves the way for advanced self-supervised learning in benthic habitat mapping.

发表机构

• Computer Vision and Robotics Research Institute (ViCOROB)（计算机视觉与机器人研究学院（ViCOROB））
• University of Girona（赫罗纳大学）

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