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

该研究解决了在稀疏输入视图下生成高质量新视图的挑战，提出了3DGS-增强器，一个新颖的管道，通过2D视频扩散先验来增强3D高斯表示的质量。其主要发现是，通过恢复渲染新视图的一致潜在特征，并与输入视图集成，该方法显著提高了渲染性能，实验证明其在大型无限场景数据集上的重建性能和渲染质量超过了现有技术。

3DGS-增强器：利用视图一致的2D扩散先验增强无限制3D高斯点云