RangeLDM: Fast Realistic LiDAR Point Cloud Generation

• URL: http://arxiv.org/abs/2403.10094v2
• Date: Tue, 10 Sep 2024 03:50:09 GMT
• Title: RangeLDM: Fast Realistic LiDAR Point Cloud Generation
• Authors: Qianjiang Hu, Zhimin Zhang, Wei Hu,
• Abstract summary: We introduce RangeLDM, a novel approach for rapidly generating high-quality range-view LiDAR point clouds. We achieve this by correcting range-view data distribution for accurate projection from point clouds to range images via Hough voting. We instruct the model to preserve 3D structural fidelity by devising a range-guided discriminator.
• Score: 12.868053836790194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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