Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes
Yuang Shi, Simone Gasparini, Géraldine Morin, Chenggang Yang, Wei Tsang Ooi · 2025
3D Gaussian Splatting (3DGS) has emerged as a promising representation for photorealistic rendering of 3D scenes. However, its high storage requirements pose significant challenges for practical applications. We observe that Gaussians exhibit distinct roles and characteristics: some capture high-frequency features like edges and contours, while others represent broader, smoother regions. Based on this observation, we propose a novel hybrid representation that categorizes Gaussians into (i) Sketch Gaussians, which define scene boundaries, and (ii) Patch Gaussians, which cover smooth regions. Sketch Gaussians are efficiently encoded using parametric models, leveraging their geometric coherence, while Patch Gaussians undergo optimized pruning and retraining to maintain volumetric consistency and storage efficiency. Our comprehensive evaluation across diverse indoor and outdoor scenes demonstrates that this structure-aware approach achieves up to 11.52 % improvement in PSNR, 1.83 % in SSIM, and 19.20 % in LPIPS at equivalent model sizes, and correspondingly, for an indoor scene, our model maintains the visual quality with 7.9 % of the original model size.