3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When trained with randomly initialized point clouds, 3DGS fails to maintain its ability to produce high-quality images, undergoing large performance drops of 4-5 dB in PSNR. Through extensive analysis of SfM initialization in the frequency domain and analysis of a 1D regression task with multiple 1D Gaussians, we propose a novel optimization strategy dubbed RAIN-GS (Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting), that successfully trains 3D Gaussians from random point clouds. We show the effectiveness of our strategy through quantitative and qualitative comparisons on multiple datasets, largely improving the performance in all settings. Our project page and code can be found at https://ku-cvlab.github.io/RAIN-GS.

通过分析频域中的结构化运动初始化和多个一维高斯回归任务的分析，我们提出了一种名为RAIN-GS（放松3D高斯点云精确初始化约束）的新优化策略，成功地从随机点云训练3D高斯效果，并通过多个数据集进行定量和定性比较，大幅提高了在所有设置中的性能。

放宽三维高斯平铺的准确初始化约束