3D Gaussian Splatting (3DGS) revolutionized novel view rendering. Instead of inferring from dense spatial points, as implicit representations do, 3DGS uses sparse Gaussians. This enables real-time performance but increases space requirements, hindering applications such as immersive communication. 3DGS compression emerged as a field aimed at alleviating this issue. While impressive progress has been made, at low rates, compression introduces artifacts that degrade visual quality significantly. We introduce NiFi, a method for extreme 3DGS compression through restoration via artifact-aware, diffusion-based one-step distillation. We show that our method achieves state-of-the-art perceptual quality at extremely low rates, down to 0.1 MB, and towards 1000x rate improvement over 3DGS at comparable perceptual performance. The code will be open-sourced upon acceptance.

翻译：三维高斯泼溅（3DGS）技术彻底革新了新视角渲染领域。与隐式表示方法从密集空间点进行推断不同，3DGS采用稀疏高斯分布进行建模。这一特性使其能够实现实时渲染性能，但同时也增加了存储空间需求，从而阻碍了沉浸式通信等应用场景的发展。3DGS压缩技术应运而生，旨在缓解这一矛盾。尽管该领域已取得显著进展，但在低码率条件下，压缩过程会引入严重影响视觉质量的伪影。本文提出NiFi方法，通过基于伪影感知的扩散式单步蒸馏修复技术，实现极致的3DGS压缩。实验表明，该方法在极低码率（低至0.1 MB）条件下仍能保持业界领先的感知质量，在同等感知性能水平下，较原始3DGS实现了近1000倍的码率优化。相关代码将在论文录用后开源。