mlsanityData to 7 Oct 2026

SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling

arXiv 2401.04230 ↗First posted 8 Jan 2024cs.CVcited by 8

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Abstract (arXiv)

We consider the problem of cross-sensor domain adaptation in the context of LiDAR-based 3D object detection and propose Stationary Object Aggregation Pseudo-labelling (SOAP) to generate high quality pseudo-labels for stationary objects. In contrast to the current state-of-the-art in-domain practice of aggregating just a few input scans, SOAP aggregates entire sequences of point clouds at the input level to reduce the sensor domain gap. Then, by means of what we call quasi-stationary training and spatial consistency post-processing, the SOAP model generates accurate pseudo-labels for stationary objects, closing a minimum of 30.3% domain gap compared to few-frame detectors. Our results also show that state-of-the-art domain adaptation approaches can achieve even greater performance in combination with SOAP, in both the unsupervised and semi-supervised settings.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 32 references that are in the map. First posted on arXiv 8 Jan 2024.

Builds on

24 of 32 references.

References · 24

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  3. 2020Center-based 3D Object Detection and Trackingcited by 1,143
  4. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 701
  5. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  6. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  7. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 150
  8. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  9. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  10. 20213D-MAN: 3D Multi-frame Attention Network for Object Detectioncited by 73
  11. 2022MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detectioncited by 65
  12. 2021Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistencycited by 63
12 more
  1. 2021ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 57
  2. 2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
  3. 2022SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloudcited by 37
  4. 2023DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Cloudscited by 31
  5. 2023Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detectioncited by 28
  6. 2021Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detectioncited by 21
  7. 2021See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptationcited by 15
  8. 2022Sparse2Dense: Learning to Densify 3D Features for 3D Object Detectioncited by 14
  9. 2023LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detectioncited by 12
  10. 2021Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye Viewcited by 10
  11. 2021FAST3D: Flow-Aware Self-Training for 3D Object Detectorscited by 10
  12. 2022Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detectioncited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 1.

8 papers.

  1. 2024UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gapscited by 12
  2. 2024MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detectioncited by 6
  3. 2024DPO: Dual-Perturbation Optimization for Test-time Adaptation in 3D Object Detectioncited by 5
  4. 2025CleverDistiller: Simple and Spatially Consistent Cross-modal Distillationcited by 3
  5. 2024VADet: Multi-frame LiDAR 3D Object Detection using Variable Aggregationcited by 2
  6. 2025MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Modelscited by 2
  7. 2025MFSeg: Efficient Multi-frame 3D Semantic Segmentationcited by 0
  8. 2026XD-MAP: Cross-Modal Domain Adaptation via Semantic Parametric Maps for Scalable Training Data Generationcited by 0