A watermarking algorithm is proposed in this paper to address the copyright protection issue of implicit 3D models. The algorithm involves embedding watermarks into the images in the training set through an embedding network, and subsequently utilizing the NeRF model for 3D modeling. A copyright verifier is employed to generate a backdoor image by providing a secret perspective as input to the neural radiation field. Subsequently, a watermark extractor is devised using the hyperparameterization method of the neural network to extract the embedded watermark image from that perspective. In a black box scenario, if there is a suspicion that the 3D model has been used without authorization, the verifier can extract watermarks from a secret perspective to verify network copyright. Experimental results demonstrate that the proposed algorithm effectively safeguards the copyright of 3D models. Furthermore, the extracted watermarks exhibit favorable visual effects and demonstrate robust resistance against various types of noise attacks.

翻译：本文提出了一种针对隐式三维模型版权保护问题的水印算法。该算法通过嵌入网络将水印信息注入训练集图像，进而利用NeRF模型进行三维建模。版权验证器通过向神经辐射场提供秘密视角输入来生成后门图像，并采用神经网络超参数化方法设计水印提取器，从该视角中提取嵌入的水印图像。在黑盒场景下，若怀疑三维模型被未经授权使用，验证器可通过秘密视角提取水印以验证网络版权。实验结果表明，所提算法能有效保护三维模型的版权，且提取的水印具有良好视觉效果，并对多种噪声攻击展现出稳健的抵抗能力。