用单次前向传播实现3D高斯点云的通用水印保护
MarkSplatter: Generalizable Watermarking for 3D Gaussian Splatting Model via Splatter Image Structure
- 将无结构3D高斯转为拼贴图像格式，支持任意信息嵌入
- 通过感知热图预测保留视觉质量，水印不可见
- 基于密集分割的提取机制，小区域对象仍可可靠恢复
3D高斯点云（3DGS）日益流行，亟需有效版权保护。现有方法对每条预设消息均需耗时微调。本文提出首个通用化水印框架，仅需一次前向传播即可实现基于拼贴图像的3DGS模型高效保护。我们设计GaussianBridge，将无结构3D高斯转换为拼贴图像格式，使任意信息可直接通过神经网络嵌入。为保证不可感知性，提出基于高斯不确定性与感知一致性的热图预测策略，确保视觉质量。为提升鲁棒性，开发基于密集分割的提取机制，在渲染视图中水印对象占比极小时仍能可靠恢复信息。项目主页：https://kevinhuangxf.github.io/marksplatter。
原文摘要 · Abstract (English)
The growing popularity of 3D Gaussian Splatting (3DGS) has intensified the need for effective copyright protection. Current 3DGS watermarking methods rely on computationally expensive fine-tuning procedures for each predefined message. We propose the first generalizable watermarking framework that enables efficient protection of Splatter Image-based 3DGS models through a single forward pass. We introduce GaussianBridge that transforms unstructured 3D Gaussians into Splatter Image format, enabling direct neural processing for arbitrary message embedding. To ensure imperceptibility, we design a Gaussian-Uncertainty-Perceptual heatmap prediction strategy for preserving visual quality. For robust message recovery, we develop a dense segmentation-based extraction mechanism that maintains reliable extraction even when watermarked objects occupy minimal regions in rendered views. Project page: https://kevinhuangxf.github.io/marksplatter.
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