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...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.展开更多
基金Youth Program of National Natural Science Foundation of China(Nos.41901415,61801481)。
文摘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.
