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×-128× scale rendering on Mip-NeRF360 dataset compared to the single scale 3D Gaussian splatting.
3D高斯最近已成为3D重建和渲染的一个高效表示方法。尽管在高分辨率下其渲染质量和速度都很高，但在较低分辨率或远距离摄像机位置进行渲染时，它们都会急剧恶化。在低分辨率或远距离渲染时，与每个飞溅的3D高斯的屏幕尺寸相比，图像的像素大小可能会低于奈奎斯特频率，从而导致走样效应。渲染速度也因为每个像素飞溅更多高斯的顺序阿尔法混合而大大减慢。为了解决这些问题，我们提出了一种多尺度3D高斯飞溅算法，它保持了不同尺度的高斯来表示同一场景。高分辨率图像使用更多的小高斯渲染，而低分辨率图像使用更少的大高斯渲染。在类似的训练时间内，与单尺度3D高斯飞溅相比，我们的算法在Mip-NeRF360数据集上的4×-128×尺度渲染中可以实现13％-66％的PSNR和160％-2400％的渲染速度提升。