Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenes
Yuang Shi, Simone Gasparini, G\'eraldine Morin, Chenggang Yang, Wei Tsang Ooi

TL;DR
This paper introduces a hybrid 3D Gaussian representation that categorizes Gaussians into sketch and patch types, significantly reducing storage needs while maintaining high-quality photorealistic scene rendering.
Contribution
It proposes a novel categorization and encoding scheme for 3D Gaussians, improving storage efficiency and rendering quality in 3D scene representations.
Findings
Achieves up to 32.62% improvement in PSNR
Reduces model size to 2.3% of original for indoor scenes
Enhances volumetric rendering quality across diverse scenes
Abstract
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 that are analogous to traditional artistic techniques -- Like how artists first sketch outlines before filling in broader areas with color, some Gaussians capture high-frequency features like edges and contours; While other Gaussians represent broader, smoother regions, that are analogous to broader brush strokes that add volume and depth to a painting. 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Processing and 3D Reconstruction · 3D Shape Modeling and Analysis · Human Pose and Action Recognition
