  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.
