GaussianMarker：具备不确定性感知的3D高斯泼溅版权保护方法
GaussianMarker: Uncertainty-Aware Copyright Protection of 3D Gaussian Splatting
AI总结：
针对现有水印方法无法适配3DGS模型、直接嵌入水印易致渲染失真的问题，提出基于不确定性的参数扰动约束不可见水印方法GaussianMarker，可在多失真场景下可靠解码，在多数据集上实现解码精度与合成质量的SOTA表现。
摘要：
3D高斯泼溅（3D Gaussian Splatting，3DGS）已成为获取3D资产的关键方法。为保护这类资产的版权，可应用数字水印技术将所有权信息隐秘嵌入3DGS模型中。然而，现有的网格、点云和隐式辐射场水印方法无法直接应用于3DGS模型，因为3DGS模型采用结构独特的显式3D高斯，且不依赖神经网络。直接在预训练的3DGS上嵌入水印会导致渲染图像出现明显失真。在本研究中，我们提出一种基于不确定性的方法，通过约束模型参数的扰动实现3DGS的不可见水印。在消息解码阶段，即使遭受多种形式的3D和2D失真，也能从3D高斯和2D渲染图像中可靠提取版权消息。我们在Blender、LLFF和MipNeRF-360数据集上开展了大量实验，验证了所提方法的有效性，证明其在消息解码准确率和视图合成质量上均达到当前最优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
