Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?

TL;DR

This paper introduces GSPure, a novel framework for effectively removing watermarks from 3D Gaussian Splatting assets, addressing vulnerabilities in existing watermarking methods and enhancing copyright protection.

Contribution

GSPure is the first dedicated watermark purification method for 3D Gaussian Splatting, improving robustness and generalization over prior approaches.

Findings

GSPure reduces watermark PSNR by up to 16.34dB.

It preserves scene fidelity with less than 1dB PSNR loss.

Outperforms existing watermark removal techniques in effectiveness.

Abstract

3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership verification. However, can existing 3D Gaussian watermarking approaches genuinely guarantee robust protection of the 3D assets? In this paper, for the first time, we systematically explore and validate possible vulnerabilities of 3DGS watermarking frameworks. We demonstrate that conventional watermark removal techniques designed for 2D images do not effectively generalize to the 3DGS scenario due to the specialized rendering pipeline and unique attributes of each gaussian primitives. Motivated by this insight, we propose GSPure, the first watermark purification framework specifically for 3DGS watermarking representations. By analyzing view-dependent rendering contributions and exploiting geometrically accurate feature clustering, GSPure precisely isolates and effectively removes watermark-related Gaussian primitives while preserving scene integrity. Extensive experiments demonstrate that our GSPure achieves the best watermark purification performance, reducing watermark PSNR by up to 16.34dB while minimizing degradation to original scene fidelity with less than 1dB PSNR loss. Moreover, it consistently outperforms existing methods in both effectiveness and generalization. Our code is available at https://github.com/insightlab-CG-3DV/GSPure.