mlsanityData to 7 Oct 2026

RangeLDM: Fast Realistic LiDAR Point Cloud Generation

arXiv 2403.10094 ↗First posted 15 Mar 2024cs.CV eess.IVcited by 45

On the map: World models → World modeling from LiDAR generation

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Abstract (arXiv)

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 data distribution for accurate projection from point clouds to range images via Hough voting, which has a critical impact on generative learning. We then compress the range images into a latent space with a variational autoencoder, and leverage a diffusion model to enhance expressivity. Additionally, we instruct the model to preserve 3D structural fidelity by devising a range-guided discriminator. Experimental results on KITTI-360 and nuScenes datasets demonstrate both the robust expressiveness and fast speed of our LiDAR point cloud generation.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). Placed in World modeling from LiDAR generation, one of 11 directions in World models. Builds on 78 of its 92 references that are in the map. First posted on arXiv 15 Mar 2024.

Builds on

78 of 92 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Learning to Generate Realistic LiDAR Point Clouds8 Sep
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation2 Dec

Other references · 76

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  5. 2020Denoising Diffusion Implicit Modelscited by 9,655
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2017Neural Discrete Representation Learningcited by 4,650
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  10. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  11. 2015Variational Inference with Normalizing Flowscited by 2,161
  12. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
38 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  2. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  3. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  4. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 939
  5. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  6. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  7. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 694
  8. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  9. 2021Score-based Generative Modeling in Latent Spacecited by 634
  10. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  11. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  12. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  13. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  14. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  15. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 409
  16. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 366
  17. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
  18. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  19. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  20. 2020Learning Gradient Fields for Shape Generationcited by 245
  21. 2017Geometric GANcited by 232
  22. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  23. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  24. 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
  25. 2021Projected GANs Converge Fastercited by 204
  26. 2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
  27. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  28. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  29. 2021RangeDet:In Defense of Range View for LiDAR-based 3D Object Detectioncited by 140
  30. 2020LiDARsim: Realistic LiDAR Simulation by Leveraging the Real Worldcited by 137
  31. 2020Pillar-based Object Detection for Autonomous Drivingcited by 137
  32. 2019LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Drivingcited by 133
  33. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
  34. 2022Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Cloudscited by 131
  35. 2021RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detectioncited by 115
  36. 2019SDM-NET: Deep Generative Network for Structured Deformable Meshcited by 113
  37. 2016Vehicle Detection from 3D Lidar Using Fully Convolutional Networkcited by 105
  38. 2018PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attentioncited by 105

50 most cited of 76 All 76 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 27; 2026: 10.

45 papers.

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  1. 20253D and 4D World Modeling: A Surveycited by 64
  2. 2025A Survey of World Models for Autonomous Drivingcited by 60
  3. 2024LidarDM: Generative LiDAR Simulation in a Generated Worldcited by 53
  4. 2024DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenescited by 37
  5. 2024DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulationcited by 34
  6. 2025The Role of World Models in Shaping Autonomous Driving: A Comprehensive Surveycited by 29
  7. 2024HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Drivingcited by 21
  8. 2025Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuningcited by 21
  9. 2025X-Scene: Large-Scale Driving Scene Generation with High Fidelity and Flexible Controllabilitycited by 20
  10. 2025LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequencescited by 14
  11. 2024X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarioscited by 13
  12. 2024Fast LiDAR Data Generation with Rectified Flowscited by 13
33 more
  1. 2025WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusioncited by 12
  2. 2025SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understandingcited by 10
  3. 2025La La LiDAR: Large-Scale Layout Generation from LiDAR Datacited by 10
  4. 2024LOGen: Toward Lidar Object Generation by Point Diffusioncited by 9
  5. 2026UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Drivingcited by 9
  6. 2024Multi-scale Latent Point Consistency Models for 3D Shape Generationcited by 6
  7. 2025Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scenecited by 5
  8. 2025Veila: Panoramic LiDAR Generation from a Monocular RGB Imagecited by 5
  9. 2025U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequencescited by 5
  10. 2025OmniGen: Unified Multimodal Sensor Generation for Autonomous Drivingcited by 5
  11. 2026GenAssets: Generating in-the-wild 3D Assets in Latent Spacecited by 4
  12. 2026World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applicationscited by 3
  13. 2025SG-LDM: Semantic-Guided LiDAR Generation via Latent-Aligned Diffusioncited by 2
  14. 2025Learning to Generate 4D LiDAR Sequencescited by 2
  15. 2025LiDAR-GS++:Improving LiDAR Gaussian Reconstruction via Diffusion Priorscited by 2
  16. 2025RadarGen: Automotive Radar Point Cloud Generation from Camerascited by 2
  17. 2026R3DPA: Leveraging 3D Representation Alignment and RGB Pretrained Priors for LiDAR Scene Generationcited by 2
  18. 2026L3DR: 3D-aware LiDAR Diffusion and Rectificationcited by 2
  19. 2025Physically Based Neural LiDAR Resimulationcited by 1
  20. 2025SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Drivingcited by 1
  21. 2025DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Drivingcited by 1
  22. 2025Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editingcited by 1
  23. 2025MultiEditor: Controllable Multimodal Object Editing for Driving Scenarios Using 3D Gaussian Splatting Priorscited by 0
  24. 2025Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generationcited by 0
  25. 2025DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentationcited by 0
  26. 2025LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Drivingcited by 0
  27. 2025LaGen: Towards Autoregressive LiDAR Scene Generationcited by 0
  28. 20254D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generationcited by 0
  29. 2026OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generationcited by 0
  30. 2026Solution for UCF UrbanTwin LUMPI Track: Sim-to-Real Urban LiDAR 3D Object Detectioncited by 0
  31. 2026Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detectioncited by 0
  32. 2026Geometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridgecited by 0
  33. 2026Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
  2. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
  3. 2023Rethinking Range View Representation for LiDAR Segmentation
  4. 2024Towards Realistic Scene Generation with LiDAR Diffusion Models
  5. 2024Simultaneous Diffusion Sampling for Conditional LiDAR Generation
  6. 2025LiDARDraft: Generating LiDAR Point Cloud from Versatile Inputs
  7. 2026CloudDiffusion: Diffusion-Based Scene Completion in the Point Cloud Domain
  8. 2025TopoLiDM: Topology-Aware LiDAR Diffusion Models for Interpretable and Realistic LiDAR Point Cloud Generation