Perceptual Wrapper with 3D-Anchored Gaussian Noise for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) delivers impressive real-time rendering of 3D scenes but remains limited in its ability to represent high-frequency textures, especially under memory constraints and Rate-Distortion Optimization (RDO). Recent methods augment the Gaussian primitives with texture maps, improving the rendering quality given a fixed Gaussian count but introduce substantial storage overhead. Towards this end, we present a general, model-agnostic perceptual wrapper that enhances 3DGS renderings via content- and view-adaptive texture synthesis. Our approach employs a lightweight per-scene network conditioned on the base rendering and 3D-anchored pseudo-random Gaussian noise to synthesize realistic textures, effectively offloading high-frequency appearance details from the underlying 3DGS representation. Guided by Wasserstein Distortion (WD), the network learns to match local feature statistics rather than enforcing pixel-wise fidelity, reducing the oversmoothing associated with traditional reconstruction objectives. Extensive subjective and objective evaluations across RDO and non-RDO 3DGS baselines demonstrate that our perceptual wrapper delivers improved perceptual quality with minimal overhead and substantially reduced storage requirements at comparable perceptual quality levels.