While 3D Gaussian Splatting (3DGS) achieves impressive real-time rendering, it frequently struggles to synthesize high-frequency textures, a limitation heavily exacerbated in memory-constrained and rate-distortion-optimized (RDO) pipelines. To address this, we propose a versatile 2D perceptual wrapper that enhances the rendered outputs of existing 3DGS representations in a content- and view-dependent manner. Our method leverages a lightweight synthesis network conditioned on pseudo-random Gaussian noise to synthesize perceptually plausible textures. Supervised by Wasserstein Distortion, the network learns to match local feature statistics rather than strictly enforcing pixel-wise reconstruction fidelity, effectively mitigating the blurriness inherent in standard frameworks. We demonstrate the broad applicability of our plug-and-play approach across vanilla, memory-constrained, and RDO 3DGS methods. Comprehensive subjective and objective experiments confirm that our method significantly improves over existing baselines, yielding superior perceptual quality at sharply reduced file or model sizes.

翻译：摘要：尽管3D高斯泼洒（3DGS）实现了令人瞩目的实时渲染，但它常难以合成高频纹理，这一问题在内存受限及率失真优化（RDO）管线中尤为严重。为解决此问题，我们提出了一种通用的2D感知包裹层，能以内容与视角相关的方式增强现有3DGS表征的渲染输出。该方法利用基于伪随机高斯噪声的轻量级合成网络，生成感知上合理的纹理。通过Wasserstein失真监督，该网络学习匹配局部特征统计量，而非严格保持逐像素重建保真度，从而有效缓解标准框架固有的模糊性。我们证明了这种即插即用方法在原始、内存受限及RDO 3GS方法中的广泛适用性。全面的主观与客观实验表明，该方法在显著减小文件或模型尺寸的同时，在现有基线基础上实现了质的提升，生成了优越的感知质量。