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.
@article{arxiv.2509.00757,
title = {MarkSplatter: Generalizable Watermarking for 3D Gaussian Splatting Model via Splatter Image Structure},
author = {Xiufeng Huang and Ziyuan Luo and Qi Song and Ruofei Wang and Renjie Wan},
journal= {arXiv preprint arXiv:2509.00757},
year = {2025}
}