  Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the
Lidar point cloud generation task, benefiting from their stable training and
iterative refinement during sampling. However, DMs often fail to realistically
model Lidar raydrop noise due to their inherent denoising process. To retain
the strength of iterative sampling while enhancing the generation of raydrop
noise, we introduce LidarGRIT, a generative model that uses auto-regressive
transformers to iteratively sample the range images in the latent space rather
than image space. Furthermore, LidarGRIT utilises VQ-VAE to separately decode
range images and raydrop masks. Our results show that LidarGRIT achieves
superior performance compared to SOTA models on KITTI-360 and KITTI odometry
datasets. Code available at:https://github.com/hamedhaghighi/LidarGRIT.
