  Generative modeling of 3D scenes is a crucial topic for aiding mobile robots
to improve unreliable observations. However, despite the rapid progress in the
natural image domain, building generative models is still challenging for 3D
data, such as point clouds. Most existing studies on point clouds have focused
on small and uniform-density data. In contrast, 3D LiDAR point clouds widely
used in mobile robots are non-trivial to be handled because of the large number
of points and varying-density. To circumvent this issue, 3D-to-2D projected
representation such as a cylindrical depth map has been studied in existing
LiDAR processing tasks but susceptible to discrete lossy pixels caused by
failures of laser reflection. This paper proposes a novel framework based on
generative adversarial networks to synthesize realistic LiDAR data as an
improved 2D representation. Our generative architectures are designed to learn
a distribution of inverse depth maps and simultaneously simulate the lossy
pixels, which enables us to decompose an underlying smooth geometry and the
corresponding uncertainty of laser reflection. To simulate the lossy pixels, we
propose a differentiable framework to learn to produce sample-dependent binary
masks using the Gumbel-Sigmoid reparametrization trick. We demonstrate the
effectiveness of our approach in synthesis and reconstruction tasks on two
LiDAR datasets. We further showcase potential applications by recovering
various corruptions in LiDAR data.
