用感知包装器提升3D高斯点云的纹理细节，兼顾画质与压缩效率。
Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting
- 用伪随机噪声控制的轻量网络生成逼真纹理，不追求像素级还原。
- 在保持低文件/模型尺寸下，显著提升视觉质量，尤其在内存受限场景。
- 可直接接入现有3DGS流程，适合关注渲染质量和压缩率的研究者。
尽管3D高斯点云（3DGS）实现高效实时渲染，但在合成高频纹理方面仍存在明显不足，尤其在内存受限和率失真优化（RDO）管道中更为严重。为此，我们提出一种通用的2D感知包装器，以内容与视角依赖的方式增强现有3DGS表示的渲染输出。该方法利用轻量级合成网络，基于伪随机高斯噪声生成感知上合理的纹理。通过Wasserstein失真监督，网络学习匹配局部特征统计而非严格像素重建，有效缓解了标准框架中的模糊问题。我们在原始、内存受限及RDO 3DGS方法上验证了该方案的广泛适用性。综合主观与客观实验表明，本方法显著优于现有基线，在大幅降低文件或模型尺寸的同时，实现了更优的感知质量。
原文摘要 · Abstract (English)
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.
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