Learning to Generate Realistic LiDAR Point Clouds

@article{Zyrianov2022LearningTG,
  title={Learning to Generate Realistic LiDAR Point Clouds},
  author={Vlas Zyrianov and Xiyue Zhu and Shenlong Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.03954}
}
. We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This model allows us to sample diverse and high-quality point cloud samples with guaranteed physical feasibility and controllability. We validate the effectiveness of our method on… 

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