  Generative modeling of 3D LiDAR data is an emerging task with promising
applications for autonomous mobile robots, such as scalable simulation, scene
manipulation, and sparse-to-dense completion of LiDAR point clouds. While
existing approaches have demonstrated the feasibility of image-based LiDAR data
generation using deep generative models, they still struggle with fidelity and
training stability. In this work, we present R2DM, a novel generative model for
LiDAR data that can generate diverse and high-fidelity 3D scene point clouds
based on the image representation of range and reflectance intensity. Our
method is built upon denoising diffusion probabilistic models (DDPMs), which
have shown impressive results among generative model frameworks in recent
years. To effectively train DDPMs in the LiDAR domain, we first conduct an
in-depth analysis of data representation, loss functions, and spatial inductive
biases. Leveraging our R2DM model, we also introduce a flexible LiDAR
completion pipeline based on the powerful capabilities of DDPMs. We demonstrate
that our method surpasses existing methods in generating tasks on the KITTI-360
and KITTI-Raw datasets, as well as in the completion task on the KITTI-360
dataset. Our project page can be found at https://kazuto1011.github.io/r2dm.
