RangeLDM: Fast Realistic LiDAR Point Cloud Generation
<|reference_start|>RangeLDM: Fast Realistic LiDAR Point Cloud Generation: 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.<|reference_end|>
@article{hu2024rangeldm:,
    title={RangeLDM: Fast Realistic LiDAR Point Cloud Generation},
    author={Qianjiang Hu, Zhimin Zhang, and Wei Hu},
    journal={arXiv preprint arXiv:2403.10094},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10094},
    primaryClass={cs.CV eess.IV}
}