3D Gaussian Splatting has recently been embraced as a versatile and effective
method for scene reconstruction and novel view synthesis, owing to its
high-quality results and compatibility with hardware rasterization. Despite its
advantages, Gaussian Splatting's reliance on high-quality point cloud
initialization by Structure-from-Motion (SFM) algorithms is a significant
limitation to be overcome. To this end, we investigate various initialization
strategies for Gaussian Splatting and delve into how volumetric reconstructions
from Neural Radiance Fields (NeRF) can be utilized to bypass the dependency on
SFM data. Our findings demonstrate that random initialization can perform much
better if carefully designed and that by employing a combination of improved
initialization strategies and structure distillation from low-cost NeRF models,
it is possible to achieve equivalent results, or at times even superior, to
those obtained from SFM initialization.

研究表明，通过巧妙设计的随机初始化和低成本 NeRF 模型的结构提炼，可以在不依赖 SFM 数据的情况下实现与 SFM 初始化相当甚至更好的结果。