A fast local reference frame (LRF) construction method based on the signed surface variation is proposed, which can adapt to the real-time applications such as self-driving, face recognition, object detection. The z-axis of the LRF is generated based on the concavity of the local surface of keypoint. The x-axis is constructed by the weighted vector sum of a set of projection vectors of the local neighborhoods around keypoint. The performance of the proposed LRF is evaluated on six standard datasets and compared with six state-of-the-art LRF construction methods (e.g., BOARD, FLARE, SHOT, RoPS and TOLDI). Experimental results validate the high repeatability, robustness, universality and time efficiency of our method.