Abstract
Neural scene representations, such as 3D Gaussian Splatting (3DGS), have enabled high-quality neural rendering; however, their large storage and transmission costs hinder deployment in resource-constrained environments. Existing compression methods either rely on costly optimization, which is slow and scene-specific, or adopt training-free pruning and quantization, which degrade rendering quality under high compression ratios. In contrast, recent data-driven approaches provide a promising direction to overcome this trade-off, enabling efficient compression while preserving high rendering quality. We introduce \textbf{ExGS}, a novel feed-forward framework that unifies \textbf{Universal Gaussian Compression} (UGC) with \textbf{GaussPainter} for \textbf{Ex}treme 3D\textbf{GS} compression. \textbf{UGC} performs re-optimization-free pruning to aggressively reduce Gaussian primitives while retaining only essential information, whereas \textbf{GaussPainter} leverages powerful diffusion priors with mask-guided refinement to restore high-quality renderings from heavily pruned Gaussian scenes. Unlike conventional inpainting, GaussPainter not only fills in missing regions but also enhances visible pixels, yielding substantial improvements in degraded renderings. To ensure practicality, it adopts a lightweight VAE and a one-step diffusion design, enabling real-time restoration. Our framework can even achieve over $100\times$ compression (reducing a typical 354.77 MB model to about 3.31 MB) while preserving fidelity and significantly improving image quality under challenging conditions. These results highlight the central role of diffusion priors in bridging the gap between extreme compression and high-quality neural rendering. Our code repository will be released at \href{this https URL}{here}.
Abstract (translated)
神经场景表示，如3D高斯点阵（3DGS），已经实现了高质量的神经渲染；然而，它们庞大的存储和传输成本阻碍了在资源受限环境中的部署。现有压缩方法要么依赖于昂贵的优化过程，这既慢且针对特定场景，要么采用无需训练的剪枝和量化技术，在高压缩比下会降低渲染质量。相比之下，近期的数据驱动方法为克服这一权衡提供了有前景的方向，能够在保持高质量渲染的同时实现高效的压缩。我们介绍了**ExGS**，一种新的前馈框架，它将**通用高斯压缩（UGC）**与**GaussPainter**相结合，用于极端的3D**GS**压缩。**UGC**执行无重新优化的剪枝来激进地减少高斯原语数量，同时保留关键信息；而**GaussPainter**则利用强大的扩散先验和掩码引导的细化从被大量裁减的高斯场景中恢复高质量渲染图像。与传统插值不同，GaussPainter不仅填补缺失区域，还增强可见像素的质量，在质量退化的情况下取得了显著改进。为了确保实用性，该框架采用了一个轻量级的VAE（变分自编码器）和一步扩散设计，实现了实时修复。我们的框架甚至可以在超过100倍压缩（将一个典型的354.77MB模型减少到大约3.31MB）的同时保持保真度，并在挑战性条件下显著提高图像质量。这些结果突显了扩散先验在极端压缩与高质量神经渲染之间的桥梁作用。我们的代码库将在[此处](https://this https URL)发布。
URL
https://arxiv.org/abs/2509.24758
