With the rapid advancement of 3D scanners and 3D point cloud acquisition technology, the application of 3D point clouds has been increasingly expanding in various fields. However, due to the limitations of 3D sensors, the collected point clouds are often sparse and non-uniform. In this work, we for the first time introduce local tactile information into the point cloud super-resolution task to aid in enhancing the resolution of the point cloud using fine-grained local details. Specifically, the local tactile point cloud is denser and more accurate compared to the low-resolution point cloud. By leveraging tactile information, we can obtain better local features. Therefore, we propose a feature extraction module that can efficiently fuse visual information with dense local tactile information. This module leverages the features from both modalities to achieve improved super-resolution results. In addition, we introduced a point cloud super-resolution dataset that includes tactile information. Qualitative and quantitative experiments show that our present work performs much better than existing similar works those do not include tactile information, both in terms of handling low-resolution inputs and revealing high-fidelity details.