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.

翻译：三维高斯溅射因其高质量的重建效果及与硬件光栅化的良好兼容性，近年来已成为场景重建和新视角合成领域广泛采用的高效方法。尽管优势显著，高斯溅射对运动恢复结构算法提供的高质量点云初始化的依赖，仍是亟需突破的关键局限。为此，我们系统研究了高斯溅射的多种初始化策略，深入探讨了如何利用神经辐射场的体积重建来替代对SFM数据的依赖。研究结果表明，经过精心设计的随机初始化可取得显著更优的效果；通过融合改进的初始化策略与低成本NeRF模型的结构蒸馏技术，能够获得与SFM初始化相当甚至更优的重建结果。