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. Existing
approaches have shown the feasibility of image-based LiDAR data generation
using deep generative models while still struggling with the fidelity of
generated data and training instability. 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 based on the denoising diffusion probabilistic models
(DDPMs), which have demonstrated impressive results among generative model
frameworks and have been significantly progressing in recent years. To
effectively train DDPMs on the LiDAR domain, we first conduct an in-depth
analysis regarding data representation, training objective, and spatial
inductive bias. Based on our designed model R2DM, we also introduce a flexible
LiDAR completion pipeline using the powerful properties of DDPMs. We
demonstrate that our method outperforms the baselines on the generation task of
KITTI-360 and KITTI-Raw datasets and the upsampling task of KITTI-360 datasets.
Our code and pre-trained weights will be available at
https://github.com/kazuto1011/r2dm