The creation of three-dimensional (3D) models is a challenging problem, and the existing point cloud-based reconstruction methods have achieved some success by directly generating a point cloud in a single stage. However, these methods have certain limitations and cannot accurately reconstruct 3D point cloud models with complex surface structures. We propose a learning-based reconstruction method to generate dense point clouds by learning multiple features of sparse point clouds. First, the image encoder embedded in the attention mechanism is used to improve the attention of the network to the target local area, and the decoder is used to generate a sparse point cloud. Second, a point cloud feature extraction block was designed to extract the effective features describing the sparse point cloud. Finally, the decoder was used to generate dense point clouds to complete the point cloud refinement. By evaluating the targets with different surface structures, verifying the effectiveness of the network by comparing with other reconstruction methods with different principles, and carrying out measurement experiments on real objects, the 3D error of the point cloud obtained is <2 mm, which meets the practical requirements.