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Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds

arXiv 2007.08488 ↗First posted 16 Jul 2020cs.CV cs.LGcited by 116

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Abstract (arXiv)

We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the underlying surfaces before passing them to a segmentation network. Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain training pairs for SVCN requires no manual labeling. We also introduce local adversarial learning to model the surface prior. The recovered 3D surfaces serve as a canonical domain, from which semantic labels can transfer across different LiDAR sensors. Experiments and ablation studies with our new benchmark for cross-domain semantic labeling of LiDAR data show that the proposed approach provides 8.2-36.6% better performance than previous domain adaptation methods.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 27; 2023: 35; 2024: 14; 2025: 12; 2026: 9.

116 papers.

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  1. 2021ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 158
  2. 2023CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIPcited by 89
  3. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  4. 2022MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentationcited by 76
  5. 2023Point Cloud Forecasting as a Proxy for 4D Occupancy Forecastingcited by 76
  6. 2022Multi-modal Sensor Fusion for Auto Driving Perception: A Surveycited by 73
  7. 2021Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistencycited by 63
  8. 2021Weakly Supervised Learning of Rigid 3D Scene Flowcited by 60
  9. 2021Learning 3D Semantic Segmentation with only 2D Image Supervisioncited by 60
  10. 2021ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detectioncited by 57
  11. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  12. 2022LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detectioncited by 54
38 more
  1. 2022CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentationcited by 54
  2. 2021A Survey on Deep Domain Adaptation for LiDAR Perceptioncited by 46
  3. 20233D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Cloudscited by 46
  4. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  5. 2023Three Pillars improving Vision Foundation Model Distillation for Lidarcited by 43
  6. 2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
  7. 2021Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentationcited by 40
  8. 2022SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloudcited by 37
  9. 2022GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentationcited by 35
  10. 2023Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentationcited by 33
  11. 2023LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDARcited by 31
  12. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  13. 2022Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Cloudscited by 28
  14. 2021Domain Adaptation on Point Clouds via Geometry-Aware Implicitscited by 26
  15. 2022CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detectioncited by 26
  16. 2022Domain generalization of 3D semantic segmentation in autonomous drivingcited by 25
  17. 2024UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weathercited by 24
  18. 2022MATE: Masked Autoencoders are Online 3D Test-Time Learnerscited by 23
  19. 2023Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learningcited by 23
  20. 2023SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptationcited by 21
  21. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  22. 2023Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentationcited by 20
  23. 2023Learning to Adapt SAM for Segmenting Cross-domain Point Cloudscited by 20
  24. 2021Unsupervised Domain Adaptation for LiDAR Panoptic Segmentationcited by 19
  25. 2022Generative Range Imaging for Learning Scene Priors of 3D LiDAR Datacited by 18
  26. 2023From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Cloudscited by 18
  27. 2023S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Realitycited by 18
  28. 2023MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentationcited by 17
  29. 2021NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Droppingcited by 16
  30. 2022LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentationcited by 16
  31. 2023Instant Domain Augmentation for LiDAR Semantic Segmentationcited by 16
  32. 2021See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptationcited by 15
  33. 2024Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 15
  34. 2022Sparse2Dense: Learning to Densify 3D Features for 3D Object Detectioncited by 14
  35. 2023Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentationcited by 14
  36. 2023MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detectioncited by 14
  37. 2023BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentationcited by 14
  38. 2023LiDAR-UDA: Self-ensembling Through Time for Unsupervised LiDAR Domain Adaptationcited by 14

50 most cited of 116 All 116 (JSON)