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Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds

arXiv 2003.13035 ↗First posted 29 Mar 2020cs.CVcited by 71

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Abstract (arXiv)

Point clouds provide intrinsic geometric information and surface context for scene understanding. Existing methods for point cloud segmentation require a large amount of fully labeled data. Using advanced depth sensors, collection of large scale 3D dataset is no longer a cumbersome process. However, manually producing point-level label on the large scale dataset is time and labor-intensive. In this paper, we propose a weakly supervised approach to predict point-level results using weak labels on 3D point clouds. We introduce our multi-path region mining module to generate pseudo point-level label from a classification network trained with weak labels. It mines the localization cues for each class from various aspects of the network feature using different attention modules. Then, we use the point-level pseudo labels to train a point cloud segmentation network in a fully supervised manner. To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network. In our setting, the 3D weak labels only indicate the classes that appeared in our input sample. We discuss both scene- and subcloud-level weakly labels on raw 3D point cloud data and perform in-depth experiments on them. On ScanNet dataset, our result trained with subcloud-level labels is compatible with some fully supervised methods.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 20; 2023: 19; 2024: 8; 2025: 7; 2026: 2.

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  1. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  2. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  3. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  4. 2021ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentationcited by 52
  5. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 49
  6. 2022LESS: Label-Efficient Semantic Segmentation for LiDAR Point Cloudscited by 41
  7. 2023GrowSP: Unsupervised Semantic Segmentation of 3D Point Cloudscited by 34
  8. 20213D Spatial Recognition without Spatially Labeled 3Dcited by 31
  9. 2022Weakly Supervised Semantic Segmentation for Large-Scale Point Cloudcited by 27
  10. 2022VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modelingcited by 21
  11. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  12. 2021Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approachcited by 17
38 more
  1. 2022LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentationcited by 16
  2. 20232D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervisioncited by 16
  3. 2021Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Miningcited by 15
  4. 2022Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentationcited by 15
  5. 2022COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentationcited by 15
  6. 2023PointDC:Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clusteringcited by 15
  7. 2024Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 15
  8. 2023Point-GCC: Universal Self-supervised 3D Scene Pre-training via Geometry-Color Contrastcited by 14
  9. 2023BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentationcited by 14
  10. 2022Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learningcited by 13
  11. 2022Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentationcited by 13
  12. 2023All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentationcited by 13
  13. 2021A new weakly supervised approach for ALS point cloud semantic segmentationcited by 12
  14. 2023CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentationcited by 12
  15. 20243D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidancecited by 12
  16. 2022Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervisioncited by 11
  17. 2021GA-NET: Global Attention Network for Point Cloud Semantic Segmentationcited by 10
  18. 2024From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervisioncited by 10
  19. 2022GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentationcited by 9
  20. 2025LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Cloudscited by 8
  21. 2021Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneckcited by 7
  22. 2022PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Cloudscited by 7
  23. 2022MISSU: 3D Medical Image Segmentation via Self-distilling TransUNetcited by 7
  24. 2023ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotationscited by 7
  25. 2023Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentationcited by 7
  26. 2023You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentationcited by 6
  27. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  28. 2021Point Discriminative Learning for Data-efficient 3D Point Cloud Analysiscited by 5
  29. 2022Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentationcited by 5
  30. 2022One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learningcited by 5
  31. 2023Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentationcited by 5
  32. 2023MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Drivingcited by 5
  33. 2021Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Cloudscited by 4
  34. 2022Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervisioncited by 4
  35. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  36. 2021Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentationcited by 3
  37. 2021On Adversarial Robustness of Point Cloud Semantic Segmentationcited by 3
  38. 2022SL3D: Self-supervised-Self-labeled 3D Recognitioncited by 3

50 most cited of 71 All 71 (JSON)