# Fast LiDAR Data Generation with Rectified Flows arXiv: https://arxiv.org/abs/2412.02241 ยท HTML: https://mlsanity.com/paper/2412.02241/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Placed in World modeling from LiDAR generation, one of 11 directions in World models. - Builds on 30 of its 33 references that are in the map. - First posted on arXiv 3 Dec 2024. ## Placed in - World models / [World modeling from LiDAR generation](https://mlsanity.com/wm/world-modeling-from-lidar-generation/) - Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/) ## Builds on (most cited first, 30 of 30) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017) - [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022) - [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021) - [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020) - [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023) - [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021) - [All are Worth Words: A ViT Backbone for Diffusion Models](https://mlsanity.com/paper/2209.12152/) (2022) - [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021) - [Neighborhood Attention Transformer](https://mlsanity.com/paper/2204.07143/) (2022) - [3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions](https://mlsanity.com/paper/1905.06292/) (2019) - [Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers](https://mlsanity.com/paper/2401.11605/) (2024) - [Improving the Training of Rectified Flows](https://mlsanity.com/paper/2405.20320/) (2024) - [Learning to Generate Realistic LiDAR Point Clouds](https://mlsanity.com/paper/2209.03954/) (2022) - [UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation](https://mlsanity.com/paper/2311.01448/) (2023) - [Towards Realistic Scene Generation with LiDAR Diffusion Models](https://mlsanity.com/paper/2404.00815/) (2024) - [LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.09256/) (2023) - [RangeLDM: Fast Realistic LiDAR Point Cloud Generation](https://mlsanity.com/paper/2403.10094/) (2024) - [Learning to Drop Points for LiDAR Scan Synthesis](https://mlsanity.com/paper/2102.11952/) (2021) - [Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data](https://mlsanity.com/paper/2210.11750/) (2022) - [TULIP: Transformer for Upsampling of LiDAR Point Clouds](https://mlsanity.com/paper/2312.06733/) (2023) ## Built on by (most cited first, 14 of 14) - [3D and 4D World Modeling: A Survey](https://mlsanity.com/paper/2509.07996/) (2025) - [LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences](https://mlsanity.com/paper/2508.03692/) (2025) - [Towards foundational LiDAR world models with efficient latent flow matching](https://mlsanity.com/paper/2506.23434/) (2025) - [La La LiDAR: Large-Scale Layout Generation from LiDAR Data](https://mlsanity.com/paper/2508.03691/) (2025) - [U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences](https://mlsanity.com/paper/2512.02982/) (2025) - [L3DR: 3D-aware LiDAR Diffusion and Rectification](https://mlsanity.com/paper/2602.19064/) (2026) - [Learning to Generate 4D LiDAR Sequences](https://mlsanity.com/paper/2509.11959/) (2025) - [R3DPA: Leveraging 3D Representation Alignment and RGB Pretrained Priors for LiDAR Scene Generation](https://mlsanity.com/paper/2601.07692/) (2026) - [OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation](https://mlsanity.com/paper/2605.13815/) (2026) - [ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows](https://mlsanity.com/paper/2509.22695/) (2025) - [LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving](https://mlsanity.com/paper/2511.16049/) (2025) - [DRUM: Diffusion-based Raydrop-aware Unpaired Mapping for Sim2Real LiDAR Segmentation](https://mlsanity.com/paper/2603.26263/) (2026) - [Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling](https://mlsanity.com/paper/2607.14639/) (2026) - [Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors](https://mlsanity.com/paper/2610.19201/) (2026) ## Abstract (arXiv) Building LiDAR generative models holds promise as powerful data priors for restoration, scene manipulation, and scalable simulation in autonomous mobile robots. In recent years, approaches using diffusion models have emerged, significantly improving training stability and generation quality. Despite their success, diffusion models require numerous iterations of running neural networks to generate high-quality samples, making the increasing computational cost a potential barrier for robotics applications. To address this challenge, this paper presents R2Flow, a fast and high-fidelity generative model for LiDAR data. Our method is based on rectified flows that learn straight trajectories, simulating data generation with significantly fewer sampling steps compared to diffusion models. We also propose an efficient Transformer-based model architecture for processing the image representation of LiDAR range and reflectance measurements. Our experiments on unconditional LiDAR data generation using the KITTI-360 dataset demonstrate the effectiveness of our approach in terms of both efficiency and quality.