Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting
Jaewoo Jung Jisang Han Honggyu An Jiwon Kang Seonghoon Park Seungryong Kim
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.