Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting
For practitioners using 3DGS in memory-constrained or rate-distortion-optimized pipelines, this method offers a plug-and-play solution to improve visual quality without sacrificing efficiency.
The paper proposes a 2D perceptual wrapper for 3D Gaussian Splatting that uses a lightweight synthesis network conditioned on pseudo-random noise to enhance high-frequency textures. The method improves perceptual quality while reducing file/model sizes, outperforming existing baselines in subjective and objective tests.
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.