高斯飞溅需要SFM初始化吗？

Does Gaussian Splatting need SFM Initialization?

April 18, 2024

作者: Yalda Foroutan, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

cs.AI

摘要

最近，3D 高斯点云喷洒被广泛应用于场景重建和新视角合成，因为它能够产生高质量的结果，并且与硬件光栅化兼容。尽管具有诸多优点，但高斯点云喷洒对由运动结构（SFM）算法进行高质量点云初始化的依赖是一个需要克服的重要限制。为此，我们研究了用于高斯点云喷洒的各种初始化策略，并探讨了如何利用神经辐射场（NeRF）的体积重建来规避对 SFM 数据的依赖性。我们的研究结果表明，如果精心设计，随机初始化可以表现得更好，并且通过采用改进的初始化策略和从低成本 NeRF 模型中提取结构，可以实现与由 SFM 初始化获得的等效结果，甚至有时更优越的效果。

English

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.