Depth images of objects can be easily obtained by depth cameras, but they can only provide limited shape information. Current widely learning-based methods generate complete 3D shapes from images, but reconstructed 3D models have low resolutions and noise. To this end, this paper proposes a 3D Cascade Shape Completion Network (3DCSCN) for predicting the complete 3D structure from a single-depth view. 3DCSCN uses an encoder-decoder network to generate rough prediction results and then introduces a point refinement network to update points with high uncertainty for fine-grained prediction results. The experimental results show that 3DCSCN is better than the current methods by an improvement in average IoU and CE by 0.91% and 3.83% on the public ShapeNet dataset, respectively.