SAGS: Structure-Aware 3D Gaussian Splatting

TL;DR

SAGS introduces a structure-aware 3D Gaussian Splatting method that implicitly encodes scene geometry, leading to improved rendering quality, reduced storage, and fewer artifacts compared to previous geometry-agnostic approaches.

Contribution

The paper presents a novel structure-aware Gaussian Splatting approach using a local-global graph representation for better scene encoding and a lightweight interpolation scheme for compactness.

Findings

State-of-the-art rendering performance on benchmarks.

Up to 24× reduction in scene size with a simple interpolation scheme.

Effective mitigation of floating artifacts and distortions.

Abstract

Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-time neural rendering overcoming the computational burden of volumetric methods. Following the pioneering work of 3D-GS, several methods have attempted to achieve compressible and high-fidelity performance alternatives. However, by employing a geometry-agnostic optimization scheme, these methods neglect the inherent 3D structure of the scene, thereby restricting the expressivity and the quality of the representation, resulting in various floating points and artifacts. In this work, we propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene, which reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets. SAGS is founded on a local-global graph representation that facilitates the learning of complex scenes and enforces meaningful point displacements that preserve the scene's geometry. Additionally, we introduce a lightweight version of SAGS, using a simple yet effective mid-point interpolation scheme, which showcases a compact representation of the scene with up to 24$\times$ size reduction without the reliance on any compression strategies. Extensive experiments across multiple benchmark datasets demonstrate the superiority of SAGS compared to state-of-the-art 3D-GS methods under both rendering quality and model size. Besides, we demonstrate that our structure-aware method can effectively mitigate floating artifacts and irregular distortions of previous methods while obtaining precise depth maps. Project page https://eververas.github.io/SAGS/.