Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring. Treacherous operating conditions, fragile surroundings, and limited navigation control often dictate that submersibles restrict their range of motion and, thus, the baseline over which they can capture measurements. In the context of 3D scene reconstruction, it is well-known that smaller baselines make reconstruction more challenging. Our work develops a physics-based multimodal acoustic-optical neural surface reconstruction framework (AONeuS) capable of effectively integrating high-resolution RGB measurements with low-resolution depth-resolved imaging sonar measurements. By fusing these complementary modalities, our framework can reconstruct accurate high-resolution 3D surfaces from measurements captured over heavily-restricted baselines. Through extensive simulations and in-lab experiments, we demonstrate that AONeuS dramatically outperforms recent RGB-only and sonar-only inverse-differentiable-rendering--based surface reconstruction methods. A website visualizing the results of our paper is located at this address: https://aoneus.github.io/

翻译：水下感知与三维表面重建是广泛用于建筑、安防、海洋考古及环境监测的挑战性问题。恶劣作业环境、脆弱周边环境及有限的导航控制常迫使潜水器限制自身运动范围，从而限制其采集测量的基线长度。在三维场景重建领域，已知较短的基线会使重建更具挑战性。本研究开发了一种基于物理的多模态声光神经表面重建框架（AONeuS），能够有效融合高分辨率RGB测量与低分辨率深度分辨成像声纳测量。通过融合这些互补模态，本框架可从严重受限基线下采集的测量数据重建高精度高分辨率三维表面。通过大量仿真与实验室实验，我们证明AONeuS在性能上显著优于近期基于RGB单模态和声纳单模态的可微逆渲染表面重建方法。论文结果可视化网站地址为：https://aoneus.github.io/