  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.
