Abstract: 3D model matching plays a vital role in model copyright protection and transaction facilitation by effectively preventing redundant authentication and enabling convenience for research, testing, and management in related fields. However, traditional matching approaches predominantly rely on plaintext matching, which, despite ensuring a certain level of matching accuracy and robustness, falls short in data privacy protection. To address this gap, ciphertext matching performs matching computations on encrypted data, thus enabling model matching while safeguarding data privacy. This approach offers significant practical value and broad application prospects. Therefore, this paper presents three matching strategies. 1) Under plaintext conditions, precise registration of 3D point clouds is achieved via the Iterative Closest Point (ICP) algorithm, followed by model matching using peak signaltonoise ratio (PSNR). 2) Under plaintext conditions, 3D point cloud features are extracted using the PointNet deep learning model, and feature similarity is calculated via cosine similarity. 3) Under ciphertext conditions, the extracted features are encrypted using homomorphic encryption. Cosine similarity is then used to compute the similarity of the encrypted features, thereby effectively protecting data privacy.

Key words: 3D model matching, iterative closest point, PointNet, homomorphic encryption, peak signaltonoise ratio, cosine similarity

摘要： 3维模型匹配在模型版权保护与交易过程中具有重要作用，可有效避免重复认证，为相关领域的研究、测试与管理提供便利.然而，传统匹配方法主要依赖明文匹配，虽然能保证一定的匹配准确性和鲁棒性，但在数据隐私保护方面存在不足.针对这一问题，密文匹配通过在数据加密状态下进行匹配运算，有效实现了在保护数据隐私的同时完成模型匹配，具有重要的应用价值和推广潜力.因此，实现了3种匹配策略：1)明文状态下基于迭代最近点(iterative closest point, ICP)算法的3维点云精准配准，并结合峰值信噪比进行模型匹配；2)明文状态下通过PointNet深度学习算法提取3维点云特征，利用余弦相似度计算特征相似度；3)密文状态下利用同态加密技术将提取的特征加密，再利用余弦相似度计算加密特征的相似度，从而有效保护数据隐私.

关键词: 3维模型匹配, 迭代最近点, PointNet, 同态加密, 峰值信噪比, 余弦相似度