# Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

arXiv: https://arxiv.org/abs/2402.15823 · HTML: https://mlsanity.com/paper/2402.15823/

## 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 (most cited first, 30 of 64)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Scaling Vision Transformers to 22 Billion Parameters](https://mlsanity.com/paper/2302.05442/) (2023)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)

## Built on by (most cited first, 16 of 16)

- [Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning](https://mlsanity.com/paper/2410.08114/) (2024)
- [Positional Prompt Tuning for Efficient 3D Representation Learning](https://mlsanity.com/paper/2408.11567/) (2024)
- [PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter](https://mlsanity.com/paper/2505.20941/) (2025)
- [Point-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis](https://mlsanity.com/paper/2410.20406/) (2024)
- [MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning](https://mlsanity.com/paper/2503.18368/) (2025)
- [On Geometry-Enhanced Parameter-Efficient Fine-Tuning for 3D Scene Segmentation](https://mlsanity.com/paper/2505.22444/) (2025)
- [PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding](https://mlsanity.com/paper/2604.04933/) (2026)
- [MS-DGCNN++: Multi-Scale Dynamic Graph Convolution with Scale-Dependent Normalization for Robust LiDAR Tree Species Classification](https://mlsanity.com/paper/2507.12602/) (2025)
- [Resilient Multimodal Industrial Surface Defect Detection with Uncertain Sensors Availability](https://mlsanity.com/paper/2509.02962/) (2025)
- [SLNet: A Super-Lightweight Geometry-Adaptive Network for 3D Point Cloud Recognition](https://mlsanity.com/paper/2603.07454/) (2026)
- [Point Cloud Quantization through Multimodal Prompting for 3D Understanding](https://mlsanity.com/paper/2511.12079/) (2025)
- [LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning](https://mlsanity.com/paper/2604.10780/) (2026)
- [P3T: Prototypical Point-level Prompt Tuning with Enhanced Generalization for 3D Vision-Language Models](https://mlsanity.com/paper/2604.15703/) (2026)
- [Domain Generalizable Adaptation of 3D Vision-Language Models via Regularized Fine-Tuning](https://mlsanity.com/paper/2606.18472/) (2026)
- [Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification](https://mlsanity.com/paper/2607.19711/) (2026)
- [PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning](https://mlsanity.com/paper/2608.02129/) (2026)

## 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.