Point cloud completion is of significant importance in 3D scene modeling because point clouds collected from sensors tend to be sparse and incomplete. Existing methods preferto transform2D grid points to the underlying surface of output point clouds. However, the point clouds they complete commonly retain the original plane structure and fail to recover details. To solve the problem, we propose 3D Grid Transformation Network. Unlike transforming 3 D grid points to point clouds directly, we calculate their weights for the reconstructed point clouds. Our method can break the topology of the 3D grid structure and make the underlying shape of output point clouds closer to the ground truth. We validate our method on point cloud completion and experimental results show that our method can achieve better quantitative results and obtain fine-grained details.