3D face recognition (FR) is a popular topic in computer vision, since 3D face data is invariant to pose and illumination condition changes which easily affect the performance of 2D FR. Though many 3D solutions have achieved impressive performances on public high-quality 3D face databases, few works concentrate on low-quality 3D FR. As the quality of 3D face acquired by widely used low-cost RGB-D sensors is really low, more robust methods are required to achieve satisfying performance on these 3D face data. To address this issue, we propose a novel two-stage pipeline to improve the performance of 3D FR. In the first stage, we utilize pix2pix network to restore the quality of low-quality face. In the second stage, we launch a multi-quality fusion network (MQFNet) to fuse the features from different qualities and enhance FR performance. Our proposed network achieves the state-of-The-Art performance on the Lock3DFace database. Furthermore, extensive controlled experiments are conducted to demonstrate the effectiveness of each model of our network.