Unbounded-GS: Extending 3D Gaussian Splatting With Hybrid Representation for Unbounded Large-Scale Scene Reconstruction

Abstract

Modeling large-scale scenes from multi-view images is challenging due to the trade-off dilemma between visual quality and computational cost. Existing NeRF-based methods have made advancements in neural implicit representation through volumetric ray-marching, but still struggle to deal with cubically growing sampling space in large-scale scenes. Fortunately, the rendering approach based on 3D Gaussian splatting (3DGS) has shown promising results, inspiring further exploration in the splatting setting. However, 3DGS has the limitation of inadequate Gaussian points for modeling distant backgrounds, leading to “splotchy” artifacts. To address this problem, we introduce a novel hybrid neural representation called Unbounded 3D Gaussian. For foreground area, we employs an explicit 3D Gaussian representation to efficiently model the geometry and appearance through splatting weighted Gaussians. For far-away background, we additionally introduce an implicit module comprising Multi-layer Perceptions (MLPs) to directly predict far-away background colors from positional encodings of view positions and ray directions. Furthermore, we design a seamless blending mechanism between the color predictions of the explicit splatting and implicit branches to reconstruct holistic scenes. Extensive experiments demonstrate that our proposed Unbounded-GS inherits the advantages of both faster convergence and high-fidelity rendering quality.