mlsanityData to 7 Oct 2026

SEGCloud: Semantic Segmentation of 3D Point Clouds

arXiv 1710.07563 ↗First posted 20 Oct 2017cs.CVcited by 178

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Abstract (arXiv)

3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.

Key facts. Cited by 178 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2017.

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Citing papers per year: 2021: 55; 2022: 43; 2023: 38; 2024: 20; 2025: 14; 2026: 8.

178 papers.

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  1. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  2. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  3. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  4. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 288
  5. 2021Self-Supervised Pretraining of 3D Features on any Point-Cloudcited by 217
  6. 2021PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Cloudscited by 211
  7. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 166
  8. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  9. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  10. 2023Weakly Supervised 3D Open-vocabulary Segmentationcited by 133
  11. 2021Hierarchical Aggregation for 3D Instance Segmentationcited by 126
  12. 2021Mix3D: Out-of-Context Data Augmentation for 3D Scenescited by 126
38 more
  1. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 121
  2. 2025Sonata: Self-Supervised Learning of Reliable Point Representationscited by 108
  3. 2021Fast Point Transformercited by 103
  4. 2021Bidirectional Projection Network for Cross Dimension Scene Understandingcited by 95
  5. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  6. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 85
  7. 2023RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Drivingcited by 76
  8. 20213D Semantic Scene Completion: a Surveycited by 74
  9. 2021SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Cloudscited by 64
  10. 2021Weakly Supervised Learning of Rigid 3D Scene Flowcited by 60
  11. 2021Adaptive Graph Convolution for Point Cloud Analysiscited by 60
  12. 2021Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perceptioncited by 57
  13. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  14. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  15. 2022STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Datasetcited by 52
  16. 2021Learning Semantic Segmentation of Large-Scale Point Clouds with Random Samplingcited by 51
  17. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 49
  18. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  19. 2021PVT: Point-Voxel Transformer for Point Cloud Learningcited by 42
  20. 2023Instance Neural Radiance Fieldcited by 39
  21. 2021Lite-HDSeg: LiDAR Semantic Segmentation Using Lite Harmonic Dense Convolutionscited by 37
  22. 2021HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embeddingcited by 36
  23. 2023SAM-guided Graph Cut for 3D Instance Segmentationcited by 35
  24. 2022Enable Deep Learning on Mobile Devices: Methods, Systems, and Applicationscited by 32
  25. 2021Deep Learning Based 3D Segmentation: A Surveycited by 30
  26. 2022Weakly Supervised Semantic Segmentation for Large-Scale Point Cloudcited by 27
  27. 2021LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignmentcited by 26
  28. 20223D Segmentation of Humans in Point Clouds with Synthetic Datacited by 24
  29. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  30. 2023Clustering based Point Cloud Representation Learning for 3D Analysiscited by 23
  31. 2021Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component Reasoningcited by 22
  32. 2023Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 21
  33. 2022PVNAS: 3D Neural Architecture Search with Point-Voxel Convolutioncited by 16
  34. 2022Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classificationcited by 16
  35. 2022PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognitioncited by 16
  36. 2023ConDaFormer: Disassembled Transformer with Local Structure Enhancement for 3D Point Cloud Understandingcited by 16
  37. 2024KPConvX: Modernizing Kernel Point Convolution with Kernel Attentioncited by 16
  38. 2021Point Cloud Learning with Transformercited by 15

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