Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2606.11782v1
• Date: Wed, 10 Jun 2026 08:14:47 GMT
• Title: Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting
• Abstract summary: 3D Gaussian Splatting (3DGS) achieves real-time rendering but struggles to synthesize high-frequency textures.<n>We propose a versatile 2D perceptual wrapper that enhances the rendered outputs of existing 3DGS representations in a content- and view-dependent manner.
• Score: 8.053023331297602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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