English(EN) PrinGS: Privacy Preservation in Gaussian Splatting

新框架可匿名化3D高斯溅射中的敏感数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了PrinGS，一种用于3D高斯溅射（3DGS）表示中隐私保护的新型框架。该方法可以匿名化已训练3D场景中的敏感视觉信息，例如人脸和文本。PrinGS可以在不到一秒的时间内匿名化内容，同时保持整体场景保真度和几何形状。此外，还引入了一种“设计即隐私”的训练策略，以防止在初始优化过程中嵌入敏感数据。 AI

影响引入了匿名化3D表示中敏感视觉数据的方法，可能有助于更安全地共享和部署3D场景。

排序理由发布了一篇研究论文，详细介绍了3D高斯溅射中隐私保护的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Gaurav Behera, Abhinav Raundhal, Archisha Panda, Saumya Balina, Gargi Shroff, Ponnurangam Kumaraguru · 2026-10-28 04:00

PrinGS：高斯溅射中的隐私保护

arXiv:2610.36168v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has rapidly become the preferred representation for high-fidelity novel view synthesis and 3D scene reconstruction. However, the learned representation encodes sensitive visual information including huma…

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PrinGS：高斯溅射中的隐私保护

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