3D-GSW：用于鲁棒水印的3D高斯泼溅
3D-GSW: 3D Gaussian Splatting for Robust Watermarking
AI总结：
本文提出3D-GSW方法，通过Frequency-Guided Densification、梯度掩码和小波子带损失，在3D Gaussian Splatting模型中嵌入鲁棒水印，兼顾高渲染质量与实时效率。
摘要：
随着3D Gaussian Splatting (3D-GS)受到广泛关注且商业应用增加，通过水印技术防止未经授权使用3D-GS模型和渲染图像的需求日益重要。本文提出一种用于3D-GS的鲁棒水印方法，保护模型及其渲染图像的版权。该方法在保持高渲染质量的同时，对渲染图像失真和模型攻击保持鲁棒。为实现这些目标，我们提出Frequency-Guided Densification (FGD)，根据3D高斯对渲染质量的贡献移除它们，从而增强实时渲染和消息鲁棒性。FGD利用离散傅里叶变换在高频区域分割3D高斯，提升渲染质量。此外，我们对3D高斯采用梯度掩码，并设计小波子带损失以增强渲染质量。实验表明，该方法将消息不可见且鲁棒地嵌入渲染图像中，能抵御包括模型失真在内的各种攻击。该方法在渲染质量和水印鲁棒性上均取得优越性能，同时提升了实时渲染效率。项目页面：https://kuai-lab.github.io/cvpr20253dgsw/
英文摘要：
As 3D Gaussian Splatting (3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures copyright of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality. To achieve these objectives, we present Frequency-Guided Densification (FGD), which removes 3D Gaussians based on their contribution to rendering quality, enhancing real-time rendering and the robustness of the message. FGD utilizes Discrete Fourier Transform to split 3D Gaussians in high-frequency areas, improving rendering quality. Furthermore, we employ a gradient mask for 3D Gaussians and design a wavelet-subband loss to enhance rendering quality. Our experiments show that our method embeds the message in the rendered images invisibly and robustly against various attacks, including model distortion. Our method achieves superior performance in both rendering quality and watermark robustness while improving real-time rendering efficiency. Project page: https://kuai-lab.github.io/cvpr20253dgsw/
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。
