mlsanityData to 14 Oct 2026

Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement

arXiv 2308.03177 ↗First posted 6 Aug 2023cs.CVcited by 19

Not on the map. Why?

Abstract (arXiv)

Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper proposes a novel approach to improve point cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods that directly utilize categorical information from support prototypes to recognize novel classes in query samples, our method identifies two critical aspects that substantially enhance model performance by reducing contextual gaps between support prototypes and query features. Specifically, we (1) adapt support background prototypes to match query context while removing extraneous cues that may obscure foreground and background in query samples, and (2) holistically rectify support prototypes under the guidance of query features to emulate the latter having no semantic gap to the query targets. Our proposed designs are agnostic to the feature extractor, rendering them readily applicable to any prototype-based methods. The experimental results on S3DIS and ScanNet demonstrate notable practical benefits, as our approach achieves significant improvements while still maintaining high efficiency. The code for our approach is available at https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-Enhancement

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 49 of its 59 references that are in the map. First posted on arXiv 6 Aug 2023.

Builds on

49 of 59 references.

References · 49

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  5. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  7. 2016Matching Networks for One Shot Learningcited by 2,255
  8. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  9. 2020Point Transformercited by 1,470
  10. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,208
  11. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  12. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
37 more
  1. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  2. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 585
  3. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  4. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  5. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  6. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 406
  7. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  8. 2017One-Shot Learning for Semantic Segmentationcited by 337
  9. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  10. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  11. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  12. 2018Low-Shot Learning from Imaginary Datacited by 181
  13. 2022Stratified Transformer for 3D Point Cloud Segmentationcited by 167
  14. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  15. 2020SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registrationcited by 142
  16. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  17. 2021Semi-supervised Semantic Segmentation with Directional Context-aware Consistencycited by 112
  18. 2021ResLT: Residual Learning for Long-tailed Recognitioncited by 96
  19. 2021Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentationcited by 87
  20. 2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
  21. 2020Few-shot 3D Point Cloud Semantic Segmentationcited by 77
  22. 2018Memory Matching Networks for One-Shot Image Recognitioncited by 65
  23. 2020Generalized Few-shot Semantic Segmentationcited by 59
  24. 2023Decoupled Kullback-Leibler Divergence Losscited by 58
  25. 2019Few-Shot Learning via Saliency-guided Hallucination of Samplescited by 49
  26. 2022Generalized Parametric Contrastive Learningcited by 45
  27. 2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
  28. 2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
  29. 2022Spatial Pruned Sparse Convolution for Efficient 3D Object Detectioncited by 26
  30. 2022DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentationcited by 23
  31. 2023Learning Context-aware Classifier for Semantic Segmentationcited by 23
  32. 2023Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completioncited by 19
  33. 2022Region Rebalance for Long-Tailed Semantic Segmentationcited by 17
  34. 2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphscited by 14
  35. 2023Message Intercommunication for Inductive Relation Reasoningcited by 8
  36. 2023Structure Guided Multi-modal Pre-trained Transformer for Knowledge Graph Reasoningcited by 8
  37. 2023Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approachcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 7.

19 papers.

Sort:

By area:

  1. 2024Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentationcited by 20
  2. 2024Rethinking Few-shot 3D Point Cloud Semantic Segmentationcited by 18
  3. 2024No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentationcited by 18
  4. 2026FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Mergingcited by 18
  5. 2025Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Modelcited by 17
  6. 2025Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Priorcited by 15
  7. 2025Taylor Series-Inspired Local Structure Fitting Network for Few-shot Point Cloud Semantic Segmentationcited by 14
  8. 2024Localization and Expansion: A Decoupled Framework for Point Cloud Few-shot Semantic Segmentationcited by 6
  9. 2026Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLMcited by 5
  10. 2024Towards Practical Human Motion Prediction with LiDAR Point Cloudscited by 2
  11. 2025EPSegFZ: Efficient Point Cloud Semantic Segmentation for Few- and Zero-Shot Scenarios with Language Guidancecited by 1
  12. 2025Open-world Point Cloud Semantic Segmentation: A Human-in-the-loop Frameworkcited by 0
7 more
  1. 2025Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentationcited by 0
  2. 2025White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentationcited by 0
  3. 2026Rethinking Multimodal Few-Shot 3D Point Cloud Segmentation: From Fused Refinement to Decoupled Arbitrationcited by 0
  4. 2026Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentationcited by 0
  5. 2026Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchorscited by 0
  6. 2026Training-Free Open-Vocabulary 3D Point-Cloud Segmentation on the Generalized Few-Shot Benchmarkcited by 0
  7. 2026DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decodingcited by 0