  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 the quality of the initial point
cloud deteriorates, such as in the presence of noise or when using randomly
initialized point cloud, 3DGS often undergoes large performance drops. To
address this limitation, we propose a novel optimization strategy dubbed
RAIN-GS (Relaing Accurate Initialization Constraint for 3D Gaussian Splatting).
Our approach is based on an in-depth analysis of the original 3DGS optimization
scheme and the analysis of the SfM initialization in the frequency domain.
Leveraging simple modifications based on our analyses, RAIN-GS successfully
trains 3D Gaussians from sub-optimal point cloud (e.g., randomly initialized
point cloud), effectively relaxing the need for accurate initialization. We
demonstrate the efficacy of our strategy through quantitative and qualitative
comparisons on multiple datasets, where RAIN-GS trained with random point cloud
achieves performance on-par with or even better than 3DGS trained with accurate
SfM point cloud. Our project page and code can be found at
https://ku-cvlab.github.io/RAIN-GS.
