mlsanityData to 14 Oct 2026

Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

arXiv 2402.15823 ↗First posted 24 Feb 2024cs.CVcited by 16

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Abstract (arXiv)

This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on time-consuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method.Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://github.com/auniquesun/PPT.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 64 of its 70 references that are in the map. First posted on arXiv 24 Feb 2024.

Builds on

64 of 70 references.

References · 64

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
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  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
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  9. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
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  11. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  12. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
38 more
  1. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  2. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  3. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,918
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
  5. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  7. 2022Visual Prompt Tuningcited by 1,681
  8. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
  9. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  10. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  11. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
  12. 2019How Can We Know What Language Models Know?cited by 959
  13. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  14. 2020PCT: Point cloud transformercited by 732
  15. 2021GPT Understands, Toocited by 716
  16. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 694
  17. 2022MaPLe: Multi-modal Prompt Learningcited by 687
  18. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  19. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  20. 2019Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Datacited by 532
  21. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 463
  22. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  23. 2021SLIP: Self-supervision meets Language-Image Pre-trainingcited by 436
  24. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  25. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
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  31. 2023Self-regulating Prompts: Foundational Model Adaptation without Forgettingcited by 272
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  38. 2020Unsupervised Point Cloud Pre-Training via Occlusion Completioncited by 186

50 most cited of 64 All 64 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 7.

16 papers.

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  1. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  2. 2024Positional Prompt Tuning for Efficient 3D Representation Learningcited by 15
  3. 2025PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adaptercited by 9
  4. 2024Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysiscited by 6
  5. 2025MoST: Efficient Monarch Sparse Tuning for 3D Representation Learningcited by 6
  6. 2025On Geometry-Enhanced Parameter-Efficient Fine-Tuning for 3D Scene Segmentationcited by 4
  7. 2026PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understandingcited by 2
  8. 2025MS-DGCNN++: Multi-Scale Dynamic Graph Convolution with Scale-Dependent Normalization for Robust LiDAR Tree Species Classificationcited by 1
  9. 2025Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availabilitycited by 1
  10. 2026SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognitioncited by 1
  11. 2025Point Cloud Quantization through Multimodal Prompting for 3D Understandingcited by 0
  12. 2026LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learningcited by 0
4 more
  1. 2026P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Modelscited by 0
  2. 2026Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuningcited by 0
  3. 2026Point-Selection Fine-Tuning Framework for Robust Point Cloud Classificationcited by 0
  4. 2026PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Multimodal Robust Prompt Distillation for 3D Point Cloud Models
  2. 2024CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation
  3. 2026PointRFT: Explicit Reinforcement Fine-tuning for Point Cloud Few-shot Learning
  4. 2026PointLLM-R: Enhancing 3D Point Cloud Reasoning via Chain-of-Thought
  5. 2026CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation
  6. 20223D Point Cloud Pre-training with Knowledge Distillation from 2D Images
  7. 2026Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
  8. 2022APSNet: Attention Based Point Cloud Sampling