mlsanityData to 7 Oct 2026

LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving

arXiv 1905.07290 ↗First posted 17 May 2019cs.CV cs.LG cs.ROcited by 22

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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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2019.

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Citing papers per year: 2021: 4; 2022: 6; 2023: 4; 2024: 5; 2025: 3.

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  1. 2022Learning to Generate Realistic LiDAR Point Cloudscited by 91
  2. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 76
  3. 2025A Survey of World Models for Autonomous Drivingcited by 60
  4. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
  5. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generationcited by 45
  6. 2024At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligencecited by 25
  7. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  8. 2024Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunitiescited by 24
  9. 2024HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Drivingcited by 21
  10. 2022Learning to Simulate Realistic LiDARscited by 16
  11. 2021Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye Viewcited by 10
  12. 2022A Realism Metric for Generated LiDAR Point Cloudscited by 5
10 more
  1. 2021A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulationcited by 4
  2. 2022Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentationcited by 4
  3. 2024Text3DAug -- Prompted Instance Augmentation for LiDAR Perceptioncited by 4
  4. 2021Quantifying point cloud realism through adversarially learned latent representationscited by 2
  5. 2025RadarGen: Automotive Radar Point Cloud Generation from Camerascited by 2
  6. 2023Attacking Motion Planners Using Adversarial Perception Errorscited by 1
  7. 2023GAN-Based LiDAR Intensity Simulationcited by 1
  8. 2025Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editingcited by 1
  9. 2022Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learningcited by 0
  10. 2022Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignmentcited by 0