mlsanityData to 14 Oct 2026

Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs

arXiv 1711.09869 ↗First posted 27 Nov 2017cs.CV cs.LG cs.NEcited by 385

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Abstract (arXiv)

We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).

Key facts. Cited by 385 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 83; 2022: 83; 2023: 83; 2024: 49; 2025: 57; 2026: 30.

385 papers.

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  1. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  2. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  3. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  4. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 289
  5. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 267
  6. 2021Learning Distilled Collaboration Graph for Multi-Agent Perceptioncited by 194
  7. 2022Vision GNN: An Image is Worth Graph of Nodescited by 186
  8. 2021Spherical Message Passing for 3D Graph Networkscited by 168
  9. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 167
  10. 2021InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referringcited by 141
  11. 2023Weakly Supervised 3D Open-vocabulary Segmentationcited by 135
  12. 2023Transformer-Based Visual Segmentation: A Surveycited by 124
38 more
  1. 2022PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Modelscited by 118
  2. 2022Superpoint Transformer for 3D Scene Instance Segmentationcited by 116
  3. 2025Sonata: Self-Supervised Learning of Reliable Point Representationscited by 110
  4. 2021Fast Point Transformercited by 104
  5. 2021Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networkscited by 99
  6. 2023OneFormer3D: One Transformer for Unified Point Cloud Segmentationcited by 95
  7. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  8. 2021Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 87
  9. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 86
  10. 2022Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptionscited by 80
  11. 2023RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Drivingcited by 76
  12. 20213D Semantic Scene Completion: a Surveycited by 74
  13. 2024Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategycited by 73
  14. 2021Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusioncited by 72
  15. 20253D-LLaVA: Towards Generalist 3D LMMs with Omni Superpoint Transformercited by 68
  16. 20214D Panoptic LiDAR Segmentationcited by 65
  17. 2021PTTR: Relational 3D Point Cloud Object Tracking with Transformercited by 65
  18. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  19. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  20. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  21. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  22. 2023Efficient 3D Semantic Segmentation with Superpoint Transformercited by 57
  23. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  24. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  25. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  26. 2022A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspectivecited by 56
  27. 2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
  28. 2021SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Networkcited by 54
  29. 20213D Siamese Voxel-to-BEV Tracker for Sparse Point Cloudscited by 54
  30. 2025Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spacescited by 54
  31. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 53
  32. 2023Using a Waffle Iron for Automotive Point Cloud Semantic Segmentationcited by 53
  33. 2023ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolutioncited by 53
  34. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  35. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 50
  36. 2022Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentationcited by 47
  37. 2021MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronizationcited by 46
  38. 2021FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decodingcited by 46

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