  Autonomous driving demands high-quality LiDAR data, yet the cost of physical
LiDAR sensors presents a significant scaling-up challenge. While recent efforts
have explored deep generative models to address this issue, they often consume
substantial computational resources with slow generation speeds while suffering
from a lack of realism. To address these limitations, we introduce RangeLDM, a
novel approach for rapidly generating high-quality range-view LiDAR point
clouds via latent diffusion models. We achieve this by correcting range-view
data distribution for accurate projection from point clouds to range images via
Hough voting, which has a critical impact on generative learning. We then
compress the range images into a latent space with a variational autoencoder,
and leverage a diffusion model to enhance expressivity. Additionally, we
instruct the model to preserve 3D structural fidelity by devising a
range-guided discriminator. Experimental results on KITTI-360 and nuScenes
datasets demonstrate both the robust expressiveness and fast speed of our LiDAR
point cloud generation.
