# LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

arXiv: https://arxiv.org/abs/1905.07290 · HTML: https://mlsanity.com/paper/1905.07290/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 22 of 22)

- [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)
- [A Survey of World Models for Autonomous Driving](https://mlsanity.com/paper/2501.11260/) (2025)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [RangeLDM: Fast Realistic LiDAR Point Cloud Generation](https://mlsanity.com/paper/2403.10094/) (2024)
- [At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence](https://mlsanity.com/paper/2402.18587/) (2024)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving](https://mlsanity.com/paper/2412.01407/) (2024)
- [Learning to Simulate Realistic LiDARs](https://mlsanity.com/paper/2209.10986/) (2022)
- [Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View](https://mlsanity.com/paper/2104.11021/) (2021)
- [A Realism Metric for Generated LiDAR Point Clouds](https://mlsanity.com/paper/2208.14958/) (2022)
- [A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation](https://mlsanity.com/paper/2110.02739/) (2021)
- [Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation](https://mlsanity.com/paper/2206.07634/) (2022)
- [Text3DAug -- Prompted Instance Augmentation for LiDAR Perception](https://mlsanity.com/paper/2408.14253/) (2024)
- [Quantifying point cloud realism through adversarially learned latent representations](https://mlsanity.com/paper/2109.11775/) (2021)
- [RadarGen: Automotive Radar Point Cloud Generation from Cameras](https://mlsanity.com/paper/2512.17897/) (2025)
- [Attacking Motion Planners Using Adversarial Perception Errors](https://mlsanity.com/paper/2311.12722/) (2023)
- [GAN-Based LiDAR Intensity Simulation](https://mlsanity.com/paper/2311.15415/) (2023)
- [Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing](https://mlsanity.com/paper/2511.17269/) (2025)
- [Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning](https://mlsanity.com/paper/2201.05585/) (2022)
- [Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment](https://mlsanity.com/paper/2202.02666/) (2022)

## Abstract (arXiv)

In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms. Instead, sensors models can be learned from real data. The main challenge is the absence of paired data set, which makes traditional supervised learning techniques not suitable. In this work, we formulate the problem as image translation from unpaired data and employ CycleGANs to solve the sensor modeling problem for LiDAR, to produce realistic LiDAR from simulated LiDAR (sim2real). Further, we generate high-resolution, realistic LiDAR from lower resolution one (real2real). The LiDAR 3D point cloud is processed in Bird-eye View and Polar 2D representations. The experimental results show a high potential of the proposed approach.