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

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