# Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

arXiv: https://arxiv.org/abs/2410.03644 · HTML: https://mlsanity.com/paper/2410.03644/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 62 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 30 of 45)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [PointConv: Deep Convolutional Networks on 3D Point Clouds](https://mlsanity.com/paper/1811.07246/) (2018)
- [SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks](https://mlsanity.com/paper/2006.10503/) (2020)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Unlearnable Examples: Making Personal Data Unexploitable](https://mlsanity.com/paper/2101.04898/) (2021)
- [Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis](https://mlsanity.com/paper/2105.01288/) (2021)
- [Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline](https://mlsanity.com/paper/2106.05304/) (2021)
- [Adversarial Examples Make Strong Poisons](https://mlsanity.com/paper/2106.10807/) (2021)
- [Rotation Invariant Convolutions for 3D Point Clouds Deep Learning](https://mlsanity.com/paper/1908.06297/) (2019)
- [Adversarial Attack and Defense on Point Sets](https://mlsanity.com/paper/1902.10899/) (2019)
- [A Backdoor Attack against 3D Point Cloud Classifiers](https://mlsanity.com/paper/2104.05808/) (2021)
- [Precise Statistical Analysis of Classification Accuracies for Adversarial Training](https://mlsanity.com/paper/2010.11213/) (2020)
- [Transferable Unlearnable Examples](https://mlsanity.com/paper/2210.10114/) (2022)
- [The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization](https://mlsanity.com/paper/2002.11080/) (2020)
- [Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples](https://mlsanity.com/paper/2301.01217/) (2022)
- [PointBA: Towards Backdoor Attacks in 3D Point Cloud](https://mlsanity.com/paper/2103.16074/) (2021)
- [Robust Unlearnable Examples: Protecting Data Against Adversarial Learning](https://mlsanity.com/paper/2203.14533/) (2022)
- [Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis](https://mlsanity.com/paper/2303.08134/) (2023)
- [DarkSAM: Fooling Segment Anything Model to Segment Nothing](https://mlsanity.com/paper/2409.17874/) (2024)
- [RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning](https://mlsanity.com/paper/2202.13094/) (2022)

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

- [PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation](https://mlsanity.com/paper/2412.16651/) (2024)
- [MTL-UE: Learning to Learn Nothing for Multi-Task Learning](https://mlsanity.com/paper/2505.05279/) (2025)
- [Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking](https://mlsanity.com/paper/2507.07483/) (2025)
- [SegTrans: Transferable Adversarial Examples for Segmentation Models](https://mlsanity.com/paper/2510.08922/) (2025)
- [A Survey on Unlearnable Data](https://mlsanity.com/paper/2503.23536/) (2025)
- [T2UE: Generating Unlearnable Examples from Text Descriptions](https://mlsanity.com/paper/2508.03091/) (2025)
- [Full spectrum Unlearnable Examples via Spectral Equalization](https://mlsanity.com/paper/2606.26719/) (2026)
- [Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information](https://mlsanity.com/paper/2603.03725/) (2026)
- [Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models](https://mlsanity.com/paper/2604.22552/) (2026)
- [ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration](https://mlsanity.com/paper/2503.06986/) (2025)
- [Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving](https://mlsanity.com/paper/2604.23105/) (2026)
- [Learning to Generate Cross-Task Unexploitable Examples](https://mlsanity.com/paper/2512.13416/) (2025)
- [UTOPIA: Unlearnable Tabular Data via Decoupled Shortcut Embedding](https://mlsanity.com/paper/2602.07358/) (2026)
- [VFACamou: View-Fused Adversarial Camouflage for Environment-Adaptive Physical Evasion](https://mlsanity.com/paper/2606.19736/) (2026)
- [ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization](https://mlsanity.com/paper/2606.21146/) (2026)
- [Semantic Privacy Protection with Utility Preservation for 3D Point Clouds](https://mlsanity.com/paper/2609.13823/) (2026)

## Abstract (arXiv)

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, ie, even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at urlhttps://github.com/CGCL-codes/UnlearnablePC