mlsanityData to 7 Oct 2026

Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

arXiv 2210.05593 ↗First posted 11 Oct 2022cs.CVcited by 14

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Abstract (arXiv)

Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This paper considers few-shot 3D point cloud object detection, where only a few annotated samples of novel classes are needed with abundant samples of base classes. To this end, we propose Prototypical VoteNet to recognize and localize novel instances, which incorporates two new modules: Prototypical Vote Module (PVM) and Prototypical Head Module (PHM). Specifically, as the 3D basic geometric structures can be shared among categories, PVM is designed to leverage class-agnostic geometric prototypes, which are learned from base classes, to refine local features of novel categories.Then PHM is proposed to utilize class prototypes to enhance the global feature of each object, facilitating subsequent object localization and classification, which is trained by the episodic training strategy. To evaluate the model in this new setting, we contribute two new benchmark datasets, FS-ScanNet and FS-SUNRGBD. We conduct extensive experiments to demonstrate the effectiveness of Prototypical VoteNet, and our proposed method shows significant and consistent improvements compared to baselines on two benchmark datasets.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 49 of its 59 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

49 of 59 references.

References · 49

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37 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
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  12. 2019A Baseline for Few-Shot Image Classificationcited by 298
  13. 2020Frustratingly Simple Few-Shot Object Detectioncited by 280
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  16. 2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 6; 2026: 2.

14 papers.

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  1. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  2. 2024Commonsense Prototype for Outdoor Unsupervised 3D Object Detectioncited by 13
  3. 2023Generalized Few-Shot Point Cloud Segmentation Via Geometric Wordscited by 12
  4. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  5. 2024Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similaritycited by 4
  6. 2025Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypescited by 3
  7. 2025Few-Shot Learning in Video and 3D Object Detection: A Surveycited by 2
  8. 2026Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environmentscited by 2
  9. 2022PartCom: Part Composition Learning for 3D Open-Set Recognitioncited by 1
  10. 2024CP-VoteNet: Contrastive Prototypical VoteNet for Few-Shot Point Cloud Object Detectioncited by 1
  11. 2025From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learningcited by 0
  12. 2025CasPoinTr: Point Cloud Completion with Cascaded Networks and Knowledge Distillationcited by 0
2 more
  1. 2025TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decodercited by 0
  2. 2026Unified Unsupervised and Sparsely-Supervised 3D Object Detection by Semantic Pseudo-Labeling and Prototype Learningcited by 0