mlsanityData to 7 Oct 2026

Scribble-Supervised LiDAR Semantic Segmentation

arXiv 2203.08537 ↗First posted 16 Mar 2022cs.CVcited by 69

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Abstract (arXiv)

Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised performance, developing efficient methods that take advantage of realistic weak supervision have yet to be explored. In this paper, we propose using scribbles to annotate LiDAR point clouds and release ScribbleKITTI, the first scribble-annotated dataset for LiDAR semantic segmentation. Furthermore, we present a pipeline to reduce the performance gap that arises when using such weak annotations. Our pipeline comprises of three stand-alone contributions that can be combined with any LiDAR semantic segmentation model to achieve up to 95.7% of the fully-supervised performance while using only 8% labeled points. Our scribble annotations and code are available at github.com/ouenal/scribblekitti.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Builds on 41 of its 59 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

41 of 59 references.

References · 41

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  4. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
  5. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  6. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  7. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  8. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  9. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  10. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  11. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  12. 2020Rethinking Pre-training and Self-trainingcited by 352
29 more
  1. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 330
  2. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  3. 2021Unbiased Teacher for Semi-Supervised Object Detectioncited by 305
  4. 2015BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentationcited by 238
  5. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  6. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  7. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  8. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  9. 2016Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentationcited by 198
  10. 2021RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentationcited by 173
  11. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  12. 2018On Regularized Losses for Weakly-supervised CNN Segmentationcited by 121
  13. 2015Constrained Convolutional Neural Networks for Weakly Supervised Segmentationcited by 100
  14. 2020AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentationcited by 87
  15. 2021Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 87
  16. 2021PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Trainingcited by 80
  17. 2021Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigationcited by 78
  18. 2020Weakly Supervised 3D Object Detection from Lidar Point Cloudcited by 67
  19. 20203D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentationcited by 58
  20. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  21. 2019Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Modelcited by 53
  22. 2020Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistencycited by 52
  23. 2018Learning to Segment Medical Images with Scribble-Supervision Alonecited by 48
  24. 2020KPRNet: Improving projection-based LiDAR semantic segmentationcited by 47
  25. 2020Structured Consistency Loss for semi-supervised semantic segmentationcited by 39
  26. 2021Self-Supervised Learning for Semi-Supervised Temporal Action Proposalcited by 39
  27. 2020Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Cloudscited by 24
  28. 2018Deep semi-supervised segmentation with weight-averaged consistency targetscited by 21
  29. 2021Seminar Learning for Click-Level Weakly Supervised Semantic Segmentationcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 23; 2024: 21; 2025: 14; 2026: 6.

69 papers.

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  1. 2022HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentationcited by 163
  2. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  3. 2023Segment Any Point Cloud Sequences by Distilling Vision Foundation Modelscited by 123
  4. 2022LaserMix for Semi-Supervised LiDAR Semantic Segmentationcited by 92
  5. 2023UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebasecited by 57
  6. 2024Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Drivingcited by 53
  7. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  8. 2023GrowSP: Unsupervised Semantic Segmentation of 3D Point Cloudscited by 34
  9. 2023Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentationcited by 33
  10. 20244D Contrastive Superflows are Dense 3D Representation Learnerscited by 32
  11. 2024Multi-Space Alignments Towards Universal LiDAR Segmentationcited by 24
  12. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
38 more
  1. 2025LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenescited by 19
  2. 2023Benchmarking the Robustness of LiDAR Semantic Segmentation Modelscited by 18
  3. 20232D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervisioncited by 16
  4. 2022Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learningcited by 15
  5. 2022COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentationcited by 15
  6. 2024RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentationcited by 15
  7. 2023Open-Vocabulary Camouflaged Object Segmentationcited by 14
  8. 2023All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentationcited by 13
  9. 2023360^circ from a Single Camera: A Few-Shot Approach for LiDAR Segmentationcited by 12
  10. 2024An Empirical Study of Training State-of-the-Art LiDAR Segmentation Modelscited by 12
  11. 2023Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentationcited by 11
  12. 2024Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understandingcited by 11
  13. 2025LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Drivingcited by 11
  14. 2024MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detectioncited by 9
  15. 2024Label-efficient Semantic Scene Completion with Scribble Annotationscited by 9
  16. 2023Scribble-Supervised RGB-T Salient Object Detectioncited by 8
  17. 2023COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous drivingcited by 8
  18. 2025LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Cloudscited by 8
  19. 2023Discwise Active Learning for LiDAR Semantic Segmentationcited by 7
  20. 2025Towards Learning to Complete Anything in Lidarcited by 7
  21. 2025Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representationscited by 7
  22. 20232D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentationcited by 6
  23. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6
  24. 2022One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learningcited by 5
  25. 2023SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representationscited by 5
  26. 2023ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentationcited by 5
  27. 2024Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Modelscited by 5
  28. 2024ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentationcited by 5
  29. 2024Multi-modal NeRF Self-Supervision for LiDAR Semantic Segmentationcited by 5
  30. 2025NUC-Net: Non-uniform Cylindrical Partition Network for Efficient LiDAR Semantic Segmentationcited by 5
  31. 2024Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasetscited by 4
  32. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  33. 2025SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistencycited by 4
  34. 2026Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Cloudscited by 4
  35. 2023Interactive Segmentation for Diverse Gesture Types Without Contextcited by 3
  36. 2024AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scanscited by 3
  37. 2024Scale Disparity of Instances in Interactive Point Cloud Segmentationcited by 3
  38. 2025CleverDistiller: Simple and Spatially Consistent Cross-modal Distillationcited by 3

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