Recent developments in neural rendering techniques have greatly enhanced the
rendering of photo-realistic 3D scenes across both academic and commercial
fields. The latest method, known as 3D Gaussian Splatting (3D-GS), has set new
benchmarks for rendering quality and speed. Nevertheless, the limitations of
3D-GS become pronounced in synthesizing new viewpoints, especially for views
that greatly deviate from those seen during training. Additionally, issues such
as dilation and aliasing arise when zooming in or out. These challenges can all
be traced back to a single underlying issue: insufficient sampling. In our
paper, we present a bootstrapping method that significantly addresses this
problem. This approach employs a diffusion model to enhance the rendering of
novel views using trained 3D-GS, thereby streamlining the training process. Our
results indicate that bootstrapping effectively reduces artifacts, as well as
clear enhancements on the evaluation metrics. Furthermore, we show that our
method is versatile and can be easily integrated, allowing various 3D
reconstruction projects to benefit from our approach.

我们的研究论文提出了一种基于扩散模型的引导方法，通过改进经过训练的 3D-GS 来增强新视角的渲染效果，显著减少了伪影，并在评估指标上获得明显的改进。此外，我们的方法具有通用性，可以轻松整合到各种 3D 重建项目中。