Title
Learning to Generate Realistic LiDAR Point Clouds.
Abstract
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. Our code is available at: https://www.zyrianov.org/lidargen/.
Year
DOI
Venue
2022
10.1007/978-3-031-20050-2_2
European Conference on Computer Vision
Keywords
DocType
Citations 
LiDAR generation,Self-driving,Diffusion models
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Vlas Zyrianov100.34
Xiyue Zhu200.34
Shenlong Wang301.01