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.

翻译：3D高斯溅射（3DGS）近期在实时新视角合成与三维重建中展现出卓越性能。然而，3DGS严重依赖于从运动恢复结构（SfM）方法获得的精确初始化点云。当初始点云质量下降时（例如存在噪声或使用随机初始化点云），3DGS的性能常出现显著衰退。为突破此限制，我们提出名为RAIN-GS（松弛3D高斯溅射精确初始化约束）的新型优化策略。该方法基于对原始3DGS优化方案的深入分析，并结合频域视角对SfM初始化的理论剖析。通过基于分析结果的简洁改进，RAIN-GS成功实现了从次优点云（如随机初始化点云）训练3D高斯模型，有效降低了对精确初始化的依赖。我们在多个数据集上通过定量与定性对比验证了该策略的有效性：使用随机点云训练的RAIN-GS达到了与精确SfM点云训练的3DGS相当甚至更优的性能。项目页面与代码详见https://ku-cvlab.github.io/RAIN-GS。