Abstract: This study addressed the limitations of existing 3D point cloud completion methods, which rely on paired data and supervised learning, resulting in high data costs and limited generalization capabilities. This paper proposed a novel 3D point cloud completion method without requiring paired data for training by integrating 3D Gaussian splatting and Score Distillation Sampling (SDS) techniques. In the proposed method, incomplete point clouds were transformed into 3D Gaussian models, which were iteratively optimized using a pre-trained 2D diffusion model to predict complete point clouds and fulfill the completion task. To enhance the initial 3D Gaussian models, the authors introduced a point densification technique that increased the density of input point clouds. Additionally, a progressive camera sampling strategy was adopted during the early optimization stages to control the camera sampling range, thereby improving optimization efficiency. The experimental results demonstrate that the proposed method outperforms existing approaches on the RedWood-3DScan dataset. Ablation studies further validate the effectiveness of the optimization strategies, confirming the superiority of the proposed method in handling real-world data.