融合声呐与视觉数据，实现水下高精度3D重建
SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping
- 用平面扫描法融合声呐与视觉信息，避免传统方法的几何假设缺陷
- 在高浑浊度环境下深度图精度显著优于现有方法
- 适合水下机器人、海洋探测等复杂环境下的三维建模任务
在视觉退化的水下环境中实现精确3D重建仍是一项重大挑战。单一模态方法效果有限：基于视觉的方法因能见度差和几何约束而失效，声呐则受固有的俯仰模糊性和低分辨率限制。因此，先前的融合技术依赖启发式规则和有缺陷的几何假设，导致显著伪影且难以建模复杂场景。本文提出SonarSweep，一种新颖的端到端深度学习框架，通过将严谨的平面扫描算法适配于声呐与视觉数据的跨模态融合，克服上述局限。在高保真仿真与真实环境中的大量实验表明，SonarSweep持续生成密集且准确的深度图，在高浑浊度等严苛条件下显著优于当前最优方法。为促进后续研究，我们将公开代码及首个同步立体相机与声呐数据的新数据集。
原文摘要 · Abstract (English)
Accurate 3D reconstruction in visually-degraded underwater environments remains a formidable challenge. Single-modality approaches are insufficient: vision-based methods fail due to poor visibility and geometric constraints, while sonar is crippled by inherent elevation ambiguity and low resolution. Consequently, prior fusion technique relies on heuristics and flawed geometric assumptions, leading to significant artifacts and an inability to model complex scenes. In this paper, we introduce SonarSweep, a novel, end-to-end deep learning framework that overcomes these limitations by adapting the principled plane sweep algorithm for cross-modal fusion between sonar and visual data. Extensive experiments in both high-fidelity simulation and real-world environments demonstrate that SonarSweep consistently generates dense and accurate depth maps, significantly outperforming state-of-the-art methods across challenging conditions, particularly in high turbidity. To foster further research, we will publicly release our code and a novel dataset featuring synchronized stereo-camera and sonar data, the first of its kind.
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