GaussianEnhancer: A General Rendering Enhancer for Gaussian Splatting

Abstract

Gaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated exceptional performance in real-time novel view synthesis (NVS), emerging as a transformative technology in the fields of explicit rendering and computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when using high-quality training frameworks, their outputs often exhibit severe rendering artifacts, such as noise and blurriness. A reasonable approach is to perform post-processing to restore clear details. In this paper, we propose GaussianEnhancer, a general network-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models while preserving the original network’s performance. Specifically, we design a degradation modeling method tailored to the GS-style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we introduce a spatial information fusion framework, consisting of view fusion and depth modulation modules, which can blend highly correlated high-quality training images and leverage the depth information of the target image to complete the rendering details. Through our GaussianEnhancer, we are able to effectively eliminate the rendering artifacts of GS models and generate highly realistic synthetic views.