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.
3D高斯喷溅（3DGS）最近在实时新视角合成和3D重建方面展示了令人印象深刻的能力。然而，3DGS严重依赖于从运动结构（SfM）方法得出的准确初始化。当用随机初始化的点云进行训练时，3DGS未能维持其产生高质量图像的能力，PSNR性能下降了4-5 dB。通过对SfM初始化在频域的广泛分析和对使用多个一维高斯的一维回归任务的分析，我们提出了一种名为RAIN-GS（放宽3D高斯喷溅的准确初始化约束）的新优化策略，成功地从随机点云训练3D高斯。我们通过在多个数据集上的定量和定性比较展示了我们策略的有效性，大大改善了所有设置中的性能。