Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 27 Nov 2025.
Builds on
17 of 29 references.
References · 17
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
- 2023GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splattingcited by 331
- 2021Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networkscited by 313
- 2021Adversarial Neuron Pruning Purifies Backdoored Deep Modelscited by 230
- 2023GS-IR: 3D Gaussian Splatting for Inverse Renderingcited by 220
- 2024CityGaussian: Real-time High-quality Large-Scale Scene Rendering with Gaussianscited by 190
- 20243DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videoscited by 156
- 2023CompGS: Smaller and Faster Gaussian Splatting with Vector Quantizationcited by 127
- 2024FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Renderingcited by 85
- 2024CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussianscited by 62
- 2024EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Imagescited by 56
5 more
- 2020WDNet: Watermark-Decomposition Network for Visible Watermark Removalcited by 37
- 2024PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relightingcited by 17
- 2025VRSplat: Fast and Robust Gaussian Splatting for Virtual Realitycited by 17
- 2025SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganographycited by 14
- 2024SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensorscited by 5
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Citing papers per year: 2026: 2.
2 papers.