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.

翻译：三维激光雷达数据的生成建模是一项新兴任务，在自主移动机器人领域具有广阔的应用前景，例如可扩展仿真、场景操控以及激光雷达点云的稀疏到密集补全。现有方法已证明基于图像的深度生成模型在激光雷达数据生成中的可行性，但仍面临生成数据保真度低和训练不稳定的问题。本文提出了R2DM，一种用于激光雷达数据的新型生成模型，能够基于距离和反射率强度的图像表征生成多样且高保真的三维场景点云。我们的方法基于去噪扩散概率模型（DDPMs），该模型近年来在生成模型框架中展现出优异性能并取得显著进展。为在激光雷达领域有效训练DDPMs，我们首先对数据表征、训练目标和空间归纳偏置进行了深入分析。基于所设计的R2DM模型，我们还利用DDPMs的强大特性引入了一种灵活的激光雷达补全流程。实验表明，我们的方法在KITTI-360和KITTI-Raw数据集的生成任务以及KITTI-360数据集的上采样任务中均优于基线方法。相关代码与预训练权重将发布于https://github.com/kazuto1011/r2dm。