论文标题
使用成像声纳的神经隐式表面重建
Neural Implicit Surface Reconstruction using Imaging Sonar
论文作者
论文摘要
我们提出了一种使用成像声纳（也称为前瞻性声纳（FLS））对物体致密的技术。与以前的方法将场景几何形状模拟为点云或体积网格相比，我们将几何形状表示为神经隐式函数。此外，鉴于这样的表示，我们使用了一个可区分的体积渲染器，该渲染器将声波传播建模以合成成像声纳测量值。我们对真实和合成数据集进行了实验，并表明我们的算法从多视图FLS图像中重建高保真表面几何形状，质量要比以前的技术高得多，并且没有其相关的内存在头顶上。
We present a technique for dense 3D reconstruction of objects using an imaging sonar, also known as forward-looking sonar (FLS). Compared to previous methods that model the scene geometry as point clouds or volumetric grids, we represent the geometry as a neural implicit function. Additionally, given such a representation, we use a differentiable volumetric renderer that models the propagation of acoustic waves to synthesize imaging sonar measurements. We perform experiments on real and synthetic datasets and show that our algorithm reconstructs high-fidelity surface geometry from multi-view FLS images at much higher quality than was possible with previous techniques and without suffering from their associated memory overhead.
