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: this https URL

我们的研究开发了一种基于物理学的多模态声光神经表面重建框架（AONeuS），能够有效地整合高分辨率 RGB 测量和低分辨率深度解析成像声纳测量，从而在受限的基线上捕获的测量中重建出准确的高分辨率 3D 表面。