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Intellectual Property Protection for 3D Gaussian Splatting Assets: A Survey

Longjie Zhao, Ziming Hong, Jiaxin Huang, Runnan Chen, Mingming Gong, Tongliang Liu

TL;DR

Let $G=\{g_i\}$ denote the Gaussian primitives composing a 3D Gaussian Splatting asset and the paper targets reliable IP protection in this high-value domain. The authors propose a bottom-up framework that starts from Gaussian-based perturbations, proceeds to IP protection tasks (passive and active), and culminates in robustness threats in the AIGC era, cataloging 24 methods within this structure. They highlight six future directions across robustness, efficiency, and protection paradigms, including universal perturbations and built-in security for reconstruction, to guide research and deployment. Overall, the survey provides a comprehensive, theory-grounded roadmap for trustworthy, scalable IP protection of 3DGS assets in real-world content pipelines.

Abstract

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.

Intellectual Property Protection for 3D Gaussian Splatting Assets: A Survey

TL;DR

Let denote the Gaussian primitives composing a 3D Gaussian Splatting asset and the paper targets reliable IP protection in this high-value domain. The authors propose a bottom-up framework that starts from Gaussian-based perturbations, proceeds to IP protection tasks (passive and active), and culminates in robustness threats in the AIGC era, cataloging 24 methods within this structure. They highlight six future directions across robustness, efficiency, and protection paradigms, including universal perturbations and built-in security for reconstruction, to guide research and deployment. Overall, the survey provides a comprehensive, theory-grounded roadmap for trustworthy, scalable IP protection of 3DGS assets in real-world content pipelines.

Abstract

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.
Paper Structure (36 sections, 4 equations, 1 figure, 1 table)

This paper contains 36 sections, 4 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Preliminary
  3. 3D Gaussian Splatting (3DGS).
  4. Taxonomy for 3DGS IP protection.
  5. General 3DGS IP protection objective.
  6. Gaussian-based Perturbations
  7. Attribute Selection Mechanism
  8. Spherical Harmonics perturbation.
  9. Hybrid-attributes perturbation.
  10. Auxiliary attribute coupling.
  11. Distribution Strategy
  12. Fourier frequency guidance.
  13. Uncertainty guidance.
  14. Global strategy.
  15. Perturbation Injection Pipeline
  16. ...and 21 more sections

Figures (1)

  • Figure 1: We introduce a bottom-up framework that summarize 3DGS IP protection from (a) underlying mechanisms of Gaussian-based perturbation (\ref{['sec:perturbations']}), through (b) 3DGS IP protection tasks (\ref{['sec:passive', 'sec:active']}), to (c) emerging robustness threats in AIGC era (\ref{['sec:robustness']}).