  Recent advances in differentiable rendering, which allow calculating the
gradients of 2D pixel values with respect to 3D object models, can be applied
to estimation of the model parameters by gradient-based optimization with only
2D supervision. It is easy to incorporate deep neural networks into such an
optimization pipeline, allowing the leveraging of deep learning techniques.
This also largely reduces the requirement for collecting and annotating 3D
data, which is very difficult for applications, for example when constructing
geometry from 2D sensors. In this work, we propose a differentiable renderer
for sidescan sonar imagery. We further demonstrate its ability to solve the
inverse problem of directly reconstructing a 3D seafloor mesh from only 2D
sidescan sonar data.
