mlsanityData to 7 Oct 2026

Domain Adaptation for Vehicle Detection from Bird's Eye View LiDAR Point Cloud Data

arXiv 1905.08955 ↗First posted 22 May 2019cs.CV cs.AIcited by 35

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Abstract (arXiv)

Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2019.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 7; 2024: 7; 2025: 3.

35 papers.

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  1. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  2. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  3. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  4. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  5. 2022MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentationcited by 76
  6. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
  7. 2021No-Reference Point Cloud Quality Assessment via Domain Adaptationcited by 37
  8. 2022SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloudcited by 37
  9. 2023Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labelingcited by 28
  10. 2021Domain Adaptation on Point Clouds via Geometry-Aware Implicitscited by 26
  11. 2022CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detectioncited by 26
  12. 2021Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detectioncited by 21
23 more
  1. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  2. 2023Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentationcited by 14
  3. 2024DG-PIC: Domain Generalized Point-In-Context Learning for Point Cloud Understandingcited by 13
  4. 2021Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye Viewcited by 10
  5. 2023Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learningcited by 10
  6. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  7. 2022Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matchingcited by 7
  8. 2021A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Cloudscited by 6
  9. 2024DiffuBox: Refining 3D Object Detection with Point Diffusioncited by 6
  10. 2024MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detectioncited by 6
  11. 2022BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detectioncited by 5
  12. 2024DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perceptioncited by 5
  13. 2024DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detectioncited by 5
  14. 2023MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Cloudscited by 2
  15. 2025From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Trainingcited by 1
  16. 2025BEVUDA++: Geometric-aware Unsupervised Domain Adaptation for Multi-View 3D Object Detectioncited by 1
  17. 2021Generation For Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Datacited by 0
  18. 2022Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learningcited by 0
  19. 2022Point-Syn2Real: Semi-Supervised Synthetic-to-Real Cross-Domain Learning for Object Classification in 3D Point Cloudscited by 0
  20. 2023NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarioscited by 0
  21. 2023A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systemscited by 0
  22. 2024Data-driven Camera and Lidar Simulation Models for Autonomous Driving: A Review from Generative Models to Volume Rendererscited by 0
  23. 2025Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detectioncited by 0