Point cloud completion aims to infer complete point clouds based on partial 3D point cloud inputs. Various previous methods apply coarse-to-fine strategy networks for generating complete point clouds. However, such methods are not only relatively time-consuming but also cannot provide representative complete shape features based on partial inputs. In this paper, a novel feature alignment fast point cloud completion network (FACNet) is proposed to directly and efficiently generate the detailed shapes of objects. FACNet aligns high-dimensional feature distributions of both partial and complete point clouds to maintain global information about the complete shape. During its decoding process, the local features from the partial point cloud are incorporated along with the maintained global information to ensure complete and time-saving generation of the complete point cloud. Experimental results show that FACNet outperforms the state-of-the-art on PCN, Completion3D, and MVP datasets, and achieves competitive performance on ShapeNet-55 and KITTI datasets. Moreover, FACNet and a simplified version, FACNet-slight, achieve a significant speedup of 3–10 times over other state-of-the-art methods.