Learning to Generate Realistic LiDAR Point Clouds

  • \(^1\) University of Illinois at Urbana-Champaign

ECCV 2022



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 the challenging KITTI-360 and NuScenes datasets. The quantitative and qualitative results show that our approach produces more realistic samples than other generative models. Furthermore, LiDARGen can sample point clouds conditioned on inputs without retraining. We demonstrate that our proposed generative model could be directly used to densify LiDAR point clouds.



Sample Generation Process from Different Angles

Unconditional Samples

Related links

LiDARGen is implemented on top of the codebase of NCSNv2.



We thank Wei-Chiu Ma and Zhijian Liu for their feedback on early drafts and all the participants in the human perceptual quality study. The project is partially funded by the Illinois Smart Transportation Initiative STII-21-07. We also thank Nvidia for the Academic Hardware Grant.
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