在3D高斯点云中隐藏不可见版权信息，修复渲染质量不降
ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting
- 利用3D-GS的梯度优化与知识蒸馏嵌入隐秘信息
- 信息可恢复且渲染质量几乎无损，保真度接近原始模型
- 适合数字版权保护场景，尤其适用于新兴3D重建数据
随着3D重建技术的快速发展，3D数据的广泛传播已成为趋势。尽管传统视觉数据（如图像、视频）及基于NeRF的格式已有成熟的版权保护技术，但针对新兴的3D高斯点云（3D-GS）格式的隐写技术尚未充分探索。为此，我们提出ConcealGS，一种在3D-GS中嵌入隐式信息的创新方法。通过引入基于3D-GS的知识蒸馏与梯度优化策略，ConcealGS克服了基于NeRF模型的局限性，提升了隐式信息的鲁棒性与3D重建质量。我们在多种潜在应用场景下评估了ConcealGS，实验结果表明，该方法不仅能成功恢复隐式信息，且对渲染质量几乎无影响，为未来向3D模型中嵌入不可见、可恢复信息提供了新路径。
原文摘要 · Abstract (English)
With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and videos) and NeRF-based formats already have mature techniques for copyright protection, steganographic techniques for the emerging 3D Gaussian Splatting (3D-GS) format have yet to be fully explored. To address this, we propose ConcealGS, an innovative method for embedding implicit information into 3D-GS. By introducing the knowledge distillation and gradient optimization strategy based on 3D-GS, ConcealGS overcomes the limitations of NeRF-based models and enhances the robustness of implicit information and the quality of 3D reconstruction. We evaluate ConcealGS in various potential application scenarios, and experimental results have demonstrated that ConcealGS not only successfully recovers implicit information but also has almost no impact on rendering quality, providing a new approach for embedding invisible and recoverable information into 3D models in the future.
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