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 \url{https://github.com/CGCL-codes/UnlearnablePC}

翻译：传统不可学习策略已被提出用于防止未经授权用户对2D图像数据进行训练。随着包含敏感信息的3D点云数据日益增多，这类新型数据的未授权使用也已成为严峻问题。为此，我们提出了首个完整的3D点云不可学习框架，包含两个核心流程：（i）我们提出一种不可学习数据保护方案，通过类别自适应分配策略建立类间设置，并为样本分配多重变换；（ii）我们提出一种数据恢复方案，利用类间逆矩阵变换，从而实现对不可学习数据的授权专属训练。这一恢复过程是现有不可学习文献中普遍忽视的实际问题，即即使授权用户也难以从3D不可学习数据中获取知识。理论与实证结果（涵盖6个数据集、16种模型和2项任务）均证明了我们所提不可学习框架的有效性。代码已发布于 \url{https://github.com/CGCL-codes/UnlearnablePC}