  3D Gaussians have recently emerged as a highly efficient representation for
3D reconstruction and rendering. Despite its high rendering quality and speed
at high resolutions, they both deteriorate drastically when rendered at lower
resolutions or from far away camera position. During low resolution or far away
rendering, the pixel size of the image can fall below the Nyquist frequency
compared to the screen size of each splatted 3D Gaussian and leads to aliasing
effect. The rendering is also drastically slowed down by the sequential alpha
blending of more splatted Gaussians per pixel. To address these issues, we
propose a multi-scale 3D Gaussian splatting algorithm, which maintains
Gaussians at different scales to represent the same scene. Higher-resolution
images are rendered with more small Gaussians, and lower-resolution images are
rendered with fewer larger Gaussians. With similar training time, our algorithm
can achieve 13\%-66\% PSNR and 160\%-2400\% rendering speed improvement at
4$\times$-128$\times$ scale rendering on Mip-NeRF360 dataset compared to the
single scale 3D Gaussian splitting. Our code and more results are available on
our project website https://jokeryan.github.io/projects/ms-gs/
