# World modeling from LiDAR generation

Area: [World models](https://mlsanity.com/wm/index.md). HTML: https://mlsanity.com/wm/world-modeling-from-lidar-generation/

## Key facts

- World modeling from LiDAR generation: 52 papers on arXiv since 2013 (data through 14 Oct 2026).
- 27 of its 52 mapped papers (52%) cite “Learning to Generate Realistic LiDAR Point Clouds” (2022, arXiv 2209.03954).
- 25 of its 52 mapped papers (48%) cite “LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models” (2023, arXiv 2309.09256).
- 2 new papers 17 Sep–14 Oct.

## Most cited here

- [Learning to Generate Realistic LiDAR Point Clouds](https://mlsanity.com/paper/2209.03954/) (2022), cited by 27 papers here
- [LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2309.09256/) (2023), cited by 25 papers here
- [Towards Realistic Scene Generation with LiDAR Diffusion Models](https://mlsanity.com/paper/2404.00815/) (2024), cited by 23 papers here
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016), cited by 20 papers here
- [RangeLDM: Fast Realistic LiDAR Point Cloud Generation](https://mlsanity.com/paper/2403.10094/) (2024), cited by 15 papers here
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017), cited by 13 papers here
- [Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion](https://mlsanity.com/paper/2403.13470/) (2024), cited by 12 papers here
- [Text2LiDAR: Text-guided LiDAR Point Cloud Generation via Equirectangular Transformer](https://mlsanity.com/paper/2407.19628/) (2024), cited by 12 papers here
- [Learning to Drop Points for LiDAR Scan Synthesis](https://mlsanity.com/paper/2102.11952/) (2021), cited by 11 papers here
- [Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data](https://mlsanity.com/paper/2210.11750/) (2022), cited by 8 papers here

## New (17 Sep–14 Oct)

- [Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors](https://mlsanity.com/paper/2610.19201/) (2026-10-13)
- [TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception](https://mlsanity.com/paper/2609.32013/) (2026-09-29)