  3D LiDAR sensors are indispensable for the robust vision of autonomous mobile
robots. However, deploying LiDAR-based perception algorithms often fails due to
a domain gap from the training environment, such as inconsistent angular
resolution and missing properties. Existing studies have tackled the issue by
learning inter-domain mapping, while the transferability is constrained by the
training configuration and the training is susceptible to peculiar lossy noises
called ray-drop. To address the issue, this paper proposes a generative model
of LiDAR range images applicable to the data-level domain transfer. Motivated
by the fact that LiDAR measurement is based on point-by-point range imaging, we
train an implicit image representation-based generative adversarial networks
along with a differentiable ray-drop effect. We demonstrate the fidelity and
diversity of our model in comparison with the point-based and image-based
state-of-the-art generative models. We also showcase upsampling and restoration
applications. Furthermore, we introduce a Sim2Real application for LiDAR
semantic segmentation. We demonstrate that our method is effective as a
realistic ray-drop simulator and outperforms state-of-the-art methods.
