Standard 3D Gaussian Splatting (3DGS) relies on known or pre-computed camera poses and a sparse point cloud, obtained from structure-from-motion (SfM) preprocessing, to initialize and grow 3D Gaussians. We propose a novel SfM-Free 3DGS (SFGS) method for video input, eliminating the need for known camera poses and SfM preprocessing. Our approach introduces a hierarchical training strategy that trains and merges multiple 3D Gaussian representations -- each optimized for specific scene regions -- into a single, unified 3DGS model representing the entire scene. To compensate for large camera motions, we leverage video frame interpolation models. Additionally, we incorporate multi-source supervision to reduce overfitting and enhance representation. Experimental results reveal that our approach significantly surpasses state-of-the-art SfM-free novel view synthesis methods. On the Tanks and Temples dataset, we improve PSNR by an average of 2.25dB, with a maximum gain of 3.72dB in the best scene. On the CO3D-V2 dataset, we achieve an average PSNR boost of 1.74dB, with a top gain of 3.90dB. The code is available at https://github.com/jibo27/3DGS_Hierarchical_Training.

本研究解决了传统3D高斯点云技术对已知相机姿态和需经过SfM预处理的稀疏点云的依赖问题。作者提出了一种新颖的无SfM 3D高斯点云技术（SFGS），通过层级训练策略优化多个3D高斯表示，从而实现对整个场景的统一建模。实验结果表明，该方法在视图合成效果上显著优于当前最先进的无SfM技术，提升了多个数据集的图像质量。 

无SfM的层级训练3D高斯点云技术