Point cloud completion is crucial for downstream tasks in 3D visual perception. However, existing methods often struggle to generalize to real-world scans due to their heavy reliance on abundant paired point clouds for training and their neglect of the distribution shift between training and testing datasets. To address these limitations, this paper explores a practical and challenging setting: ''source-free domain adaptive point cloud completion'', where a well-trained source model must adapt to the target data distribution without access to source data, aiming to improve completion performance. To tackle this problem, we propose a novel method called ''Dual-Stage Preservation and Fusion'' (DSPF), which comprises two key training stages tailored to this new setting. In the source preservation stage, we introduce graph structural alignment and marginal feature alignment to preserve and transfer essential knowledge from the source domain. In the target fusion stage, we design a self-supervised loss to capture the geometric structure of target instances and establish a bidirectional interaction mechanism to transfer partial source knowledge to the target distribution. Extensive experiments on various cross-domain point cloud completion benchmarks demonstrate that our proposed DSPF significantly outperforms existing methods, validating its effectiveness and robustness in source-free domain adaptation scenarios. Our code is available at https://github.com/ZhiXia-SEU/DSPF.