RangeLDM: Fast Realistic LiDAR Point Cloud Generation

arXiv:2403.10094v1 Announce Type: new
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 …

abstract arxiv autonomous autonomous driving challenge cloud computational cost cs.cv data deep generative models driving eess.iv generative generative models issue lidar quality resources scaling sensors type