3D Gaussian Splatting (3DGS) has become a crucial method for acquiring 3D assets. To protect the copyright of these assets, digital watermarking techniques can be applied to embed ownership information discreetly within 3DGS models. However, existing watermarking methods for meshes, point clouds, and implicit radiance fields cannot be directly applied to 3DGS models, as 3DGS models use explicit 3D Gaussians with distinct structures and do not rely on neural networks. Naively embedding the watermark on a pre-trained 3DGS can cause obvious distortion in rendered images. In our work, we propose an uncertainty-based method that constrains the perturbation of model parameters to achieve invisible watermarking for 3DGS. At the message decoding stage, the copyright messages can be reliably extracted from both 3D Gaussians and 2D rendered images even under various forms of 3D and 2D distortions. We conduct extensive experiments on the Blender, LLFF and MipNeRF-360 datasets to validate the effectiveness of our proposed method, demonstrating state-of-the-art performance on both message decoding accuracy and view synthesis quality.
三维高斯点散射（3DGS）已成为获取三维资产的重要方法。为了保护这些资产的版权，可将数字水印技术应用于3DGS模型，在其中隐秘地嵌入所有权信息。然而，现有的针对网格、点云和隐式辐射场的水印方法无法直接应用于3DGS模型，因为3DGS模型使用显式的三维高斯，具有独特的结构，并且不依赖神经网络。将水印直接嵌入预训练的3DGS模型中会导致渲染图像出现明显的失真。在我们的研究中，我们提出了一种基于不确定性的方案，通过限制模型参数的扰动，实现对3DGS的隐形水印嵌入。在信息解码阶段，即使在各种三维和二维失真情况下，仍能可靠地从3D高斯和2D渲染图像中提取出版权信息。我们在Blender、LLFF和MipNeRF-360数据集上进行了大量实验，以验证所提方法的有效性，显示了在信息解码准确性和视图合成质量上的领先表现。