Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to enable dense reconstruction of transparent surfaces in indoor scenes. We propose a novel model that leverages generative latent optimization to learn an implicit representation of indoor scenes consisting of transparent surfaces. We demonstrate that we can query the implicit representation to enable volumetric rendering in image space or 3D geometry reconstruction (point clouds or mesh) with transparent surface prediction. We evaluate our method's effectiveness qualitatively and quantitatively on a new dataset collected using a custom, low-cost sensing platform featuring RGB-D cameras and ultrasonic sensors. Our method exhibits significant improvement over state-of-the-art for transparent surface reconstruction.

翻译：室内移动机器人必须能够应对包含透明表面的复杂场景。本文提出一种新颖方法，通过隐式神经表征融合听觉与视觉感知模态，实现室内场景透明表面的稠密重建。我们提出一种创新模型，利用生成式潜在优化学习包含透明表面的室内场景隐式表征。实验表明，通过查询该隐式表征可实现图像空间的体素渲染或带透明表面预测的三维几何重建（点云或网格）。我们在使用定制化低成本传感平台（配备RGB-D相机与超声波传感器）采集的新数据集上，对方法进行了定性与定量评估。本方法在透明表面重建任务上较现有最优技术展现出显著提升。