3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-Consistent 2D Diffusion Priors

Abstract

Novel-view synthesis aims to generate novel views of a scene from multiple inputimages or videos, and recent advancements like 3D Gaussian splatting (3DGS)have achieved notable success in producing photorealistic renderings with efficientpipelines. However, generating high-quality novel views under challenging settings,such as sparse input views, remains difficult due to insufficient information inunder-sampled areas, often resulting in noticeable artifacts. This paper presents3DGS-Enhancer, a novel pipeline for enhancing the representation quality of3DGS representations. We leverage 2D video diffusion priors to address thechallenging 3D view consistency problem, reformulating it as achieving temporalconsistency within a video generation process. 3DGS-Enhancer restores view-consistent latent features of rendered novel views and integrates them with theinput views through a spatial-temporal decoder. The enhanced views are thenused to fine-tune the initial 3DGS model, significantly improving its renderingperformance. Extensive experiments on large-scale datasets of unbounded scenesdemonstrate that 3DGS-Enhancer yields superior reconstruction performance andhigh-fidelity rendering results compared to state-of-the-art methods. The projectwebpage is https://xiliu8006.github.io/3DGS-Enhancer-project.