mlsanityData to 7 Oct 2026

Deep Generative Modeling of LiDAR Data

arXiv 1812.01180 ↗First posted 4 Dec 2018cs.CVcited by 70

Not on the map. Why?

Cited by 25 in World models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative data

Key facts. Cited by 70 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 7; 2023: 10; 2024: 17; 2025: 17; 2026: 9.

70 papers.

Sort:

By area:

  1. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 91
  2. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 76
  3. 2024Towards Realistic Scene Generation with LiDAR Diffusion Modelscited by 67
  4. 2025A Survey of World Models for Autonomous Drivingcited by 60
  5. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Modelscited by 53
  6. 2024LidarDM: Generative LiDAR Simulation in a Generated Worldcited by 53
  7. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
  8. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  9. 2023NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fieldscited by 44
  10. 2021Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networkscited by 42
  11. 2024DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenescited by 37
  12. 2021Learning to Drop Points for LiDAR Scan Synthesiscited by 27
38 more
  1. 2024Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformercited by 26
  2. 2024Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunitiescited by 24
  3. 2021Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Modelscited by 22
  4. 2023Dynamic LiDAR Re-simulation using Compositional Neural Fieldscited by 22
  5. 2024HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Drivingcited by 21
  6. 2022Generative Range Imaging for Learning Scene Priors of 3D LiDAR Datacited by 18
  7. 2022Learning to Simulate Realistic LiDARscited by 16
  8. 2022Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Librarycited by 14
  9. 2024X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarioscited by 13
  10. 2024OLiDM: Object-aware LiDAR Diffusion Models for Autonomous Drivingcited by 13
  11. 2025WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusioncited by 12
  12. 2024An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domainscited by 10
  13. 2024LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulationcited by 10
  14. 2022GeoPointGAN: Synthetic Spatial Data with Local Label Differential Privacycited by 9
  15. 2026UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Drivingcited by 9
  16. 2023Lidar Upsampling with Sliced Wasserstein Distancecited by 8
  17. 2025R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Modelcited by 7
  18. 2021Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Modelscited by 5
  19. 2022A Realism Metric for Generated LiDAR Point Cloudscited by 5
  20. 2024Taming Transformers for Realistic Lidar Point Cloud Generationcited by 5
  21. 2025U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequencescited by 5
  22. 2025OmniGen: Unified Multimodal Sensor Generation for Autonomous Drivingcited by 5
  23. 2021Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transportcited by 4
  24. 2022HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Drivingcited by 4
  25. 2024Fast LiDAR Upsampling using Conditional Diffusion Modelscited by 4
  26. 2024LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenescited by 4
  27. 2025A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generationcited by 4
  28. 2021DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencodercited by 3
  29. 2021Multi-Echo LiDAR for 3D Object Detectioncited by 3
  30. 2025L2RDaS: Synthesizing 4D Radar Tensors for Model Generalization via Dataset Expansioncited by 3
  31. 2021Quantifying point cloud realism through adversarially learned latent representationscited by 2
  32. 2023GLiDR: Topologically Regularized Graph Generative Network for Sparse LiDAR Point Cloudscited by 2
  33. 2025SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusioncited by 2
  34. 2025RadarGen: Automotive Radar Point Cloud Generation from Camerascited by 2
  35. 2026L3DR: 3D-aware LiDAR Diffusion and Rectificationcited by 2
  36. 2023MOVES: Movable and Moving LiDAR Scene Segmentation in Label-Free settings using Static Reconstructioncited by 1
  37. 2023Differentiable SLAM Helps Deep Learning-based LiDAR Perception Taskscited by 1
  38. 2023Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real Worldcited by 1

50 most cited of 70 All 70 (JSON)