Abstract: To achieve human-like skin tactile perception with super-resolution, the method of introducing a soft layer on sensing array has attracted increasing attention. Due to the limitations of sensing units principle, most existing tactile sensors can only sense normal force. However, multi-dimensional force information is important for robot manipulation. To address this, we propose a tactile sensing unit based on a tiny monolithic tri-cantilever structure that decouples three-dimensional force. A reconstruction algorithm combined both model-based and learning-based approaches is then proposed to detect the three-dimensional force applied to the sensing unit. These units are arranged in an array and covered with a soft silicone layer which induces traction-coupling effects. By leveraging deep learning, our tactile sensor can estimate the magnitude and position of external three-dimensional force with super-resolution. Experiments have shown that our tactile sensor achieves a Mean Absolute Error (MAE) of 0.19\,N for three-dimensional force estimation and 0.49\,mm for contact localization. Notably, this corresponds to a 26-fold improvement in spatial resolution, surpassing the state-of-the-art literature. Then the benefits and potential applications of our proposed sensors are validated in several tasks, including the teleoperative transfer of a test tube into a rack and stable robotic grasping under external interference. These demonstrate the practicality of our design and provide new solutions for tactile sensors.