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.
随着 3D 重建技术的快速发展，3D 数据的广泛分发正成为未来的趋势。尽管传统视觉数据（如图像和视频）以及基于 NeRF 的格式在版权保护方面已有成熟技术，但针对新兴的 3D 高斯点绘制（3D-GS）格式的隐写技术尚未得到充分研究。为此，我们提出了一种创新方法 ConcealGS，用于将隐式信息嵌入到 3D-GS 中。 ConcealGS 通过引入基于 3D-GS 的知识蒸馏和梯度优化策略，克服了基于 NeRF 的模型的局限性，并提升了隐式信息的鲁棒性以及 3D 重建的质量。我们在多种潜在应用场景中评估了 ConcealGS，实验结果表明，ConcealGS 不仅能够成功恢复嵌入的隐式信息，而且几乎对渲染质量没有影响，为未来在 3D 模型中嵌入不可见且可恢复的信息提供了一种全新的方法。