CompMarkGS：面向压缩 3D Gaussian Splatting 的鲁棒水印
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting
AI总结：
针对 3DGS 压缩尤其量化破坏水印的问题，提出 CompMarkGS，通过锚点属性嵌入、量化失真训练及频率感知策略实现压缩鲁棒水印与高质量渲染。
摘要：
随着 3D Gaussian Splatting（3DGS）凭借高质量、实时渲染能力而越来越多地被各类学术和商业应用采用，版权保护需求也在不断增长。同时，其较大的模型尺寸需要高效压缩以进行存储和传输。然而，压缩技术，尤其是基于量化的方法，会破坏现有 3DGS 水印方法的完整性，因此需要一种能够抵抗压缩的新方法。为确保压缩条件下可靠的水印检测，我们提出一种容忍压缩的 3DGS 水印方法，可保持水印完整性和渲染质量。该方法采用基于锚点的 3DGS，将水印嵌入锚点属性，尤其是锚点特征，以增强安全性和渲染质量。我们还提出量化失真层，在训练期间注入量化噪声，从而在基于量化的压缩后保留水印。此外，我们采用频率感知锚点增长策略，通过有效识别高频区域中的 Gaussian 来提升渲染质量，并使用 HSV 损失减轻颜色伪影，以进一步改善渲染质量。大量实验表明，所提方法即使在压缩下也能保留水印，并保持较高渲染质量。
英文摘要：
As 3D Gaussian Splatting (3DGS) is increasingly adopted in various academic and commercial applications due to its high-quality and real-time rendering capabilities, the need for copyright protection is growing. At the same time, its large model size requires efficient compression for storage and transmission. However, compression techniques, especially quantization-based methods, degrade the integrity of existing 3DGS watermarking methods, thus creating the need for a novel methodology that is robust against compression. To ensure reliable watermark detection under compression, we propose a compression-tolerant 3DGS watermarking method that preserves watermark integrity and rendering quality. Our approach utilizes an anchor-based 3DGS, embedding the watermark into anchor attributes, particularly the anchor feature, to enhance security and rendering quality. We also propose a quantization distortion layer that injects quantization noise during training, preserving the watermark after quantization-based compression. Moreover, we employ a frequency-aware anchor growing strategy that enhances rendering quality by effectively identifying Gaussians in high-frequency regions, and an HSV loss to mitigate color artifacts for further rendering quality improvement. Extensive experiments demonstrate that our proposed method preserves the watermark even under compression and maintains high rendering quality.
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