mlsanityData to 14 Oct 2026

Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

arXiv 2204.00132 ↗First posted 31 Mar 2022cs.CVcited by 10

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Abstract (arXiv)

This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs. We design a model DASE-ProPillars that can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses PointPillars as the baseline model with additional modules to improve the 3D detection performance. To prove the effectiveness of our proposed modules in DASE-ProPillars, we train and evaluate the model on two datasets, the open source A9-Dataset and a semi-synthetic infrastructure dataset created within the Regensburg Next project. We do several sets of experiments for each module in the DASE-ProPillars detector that show that our model outperforms the SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test set, while maintaining an inference speed of 45 Hz (22 ms). We apply domain adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset from the Regensburg Next project by applying transfer learning and achieve a 3D [email protected] of 93.49% on the Car class of the target test set using 40 recall positions.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 31 Mar 2022.

Builds on

25 of 42 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  6. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  7. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  8. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  9. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  10. 2018PointRCNN: 3D Object Proposal Generation and Detection from Point Cloudcited by 941
  11. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 748
  12. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
13 more
  1. 2021Voxel Transformer for 3D Object Detectioncited by 247
  2. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 159
  3. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 151
  4. 2019xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 129
  5. 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
  6. 2020Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Cloudscited by 116
  7. 2019TANet: Robust 3D Object Detection from Point Clouds with Triple Attentioncited by 108
  8. 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
  9. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  10. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  11. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 47
  12. 20193D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streamscited by 36
  13. 2019Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluationcited by 17

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 5.

10 papers.

  1. 2022A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Researchcited by 40
  2. 2023InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensorscited by 25
  3. 2025MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Drivingcited by 3
  4. 2024FedRSU: Federated Learning for Scene Flow Estimation on Roadside Unitscited by 2
  5. 2024PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systemscited by 2
  6. 2024Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detectioncited by 2
  7. 2025Towards Vision Zero: The TUM Traffic Accid3nD Datasetcited by 2
  8. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  9. 2025Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensorscited by 1
  10. 2025Safety-Critical Learning for Long-Tail Events: The TUM Traffic Accident Datasetcited by 1