3D Gaussian Splatting (3DGS) has become a mainstream representation for real-time 3D scene synthesis, enabling applications in virtual and augmented reality, robotics, and 3D content creation. Its rising commercial value and explicit parametric structure raise emerging intellectual property (IP) protection concerns, prompting a surge of research on 3DGS IP protection. However, current progress remains fragmented, lacking a unified view of the underlying mechanisms, protection paradigms, and robustness challenges. To address this gap, we present the first systematic survey on 3DGS IP protection and introduce a bottom-up framework that examines (i) underlying Gaussian-based perturbation mechanisms, (ii) passive and active protection paradigms, and (iii) robustness threats under emerging generative AI era, revealing gaps in technical foundations and robustness characterization and indicating opportunities for deeper investigation. Finally, we outline six research directions across robustness, efficiency, and protection paradigms, offering a roadmap toward reliable and trustworthy IP protection for 3DGS assets.

翻译：三维高斯溅射（3DGS）已成为实时三维场景合成的主流表征方法，在虚拟与增强现实、机器人技术及三维内容创作等领域得到广泛应用。其日益增长的商业价值与显式参数化结构引发了新兴的数字产权保护问题，促使针对3DGS产权保护的研究迅速涌现。然而，当前进展仍呈碎片化状态，缺乏对底层机制、保护范式及鲁棒性挑战的统一认知。为填补这一空白，本文首次对3DGS产权保护进行系统性综述，并提出自底向上的分析框架，涵盖：（i）基于高斯扰动的底层机制；（ii）被动与主动保护范式；（iii）生成式人工智能新时代下的鲁棒性威胁。通过该框架揭示了技术基础与鲁棒性表征方面的研究缺口，并指出深入探索的潜在方向。最后，我们围绕鲁棒性、效率及保护范式三大维度提出六个研究路径，为构建可靠可信的3DGS资产产权保护体系提供发展路线图。