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
Obtaining a 3D feature description with high descriptiveness and robustness under complicated nuisances is a significant and challenging task in 3D feature matching.This paper proposes a novel feature description cons...Obtaining a 3D feature description with high descriptiveness and robustness under complicated nuisances is a significant and challenging task in 3D feature matching.This paper proposes a novel feature description consisting of a stable local reference frame(LRF)and a feature descriptor based on local spatial voxels.First,an improved LRF was designed by incorporating distance weights into Z-and X-axis calculations.Subsequently,based on the LRF and voxel segmentation,a feature descriptor based on voxel homogenization was proposed.Moreover,uniform segmentation of cube voxels was performed,considering the eigenvalues of each voxel and its neighboring voxels,thereby enhancing the stability of the description.The performance of the descriptor was strictly tested and evaluated on three public datasets,which exhibited high descriptiveness,robustness,and superior performance compared with other current methods.Furthermore,the descriptor was applied to a 3D registration trial,and the results demonstrated the reliability of our approach.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.
基金the National Natural Science Foundation of China,No.51705469the Zhengzhou University Youth Talent Enterprise Cooperative Innovation Team Support Program Project(2021,2022).
文摘Obtaining a 3D feature description with high descriptiveness and robustness under complicated nuisances is a significant and challenging task in 3D feature matching.This paper proposes a novel feature description consisting of a stable local reference frame(LRF)and a feature descriptor based on local spatial voxels.First,an improved LRF was designed by incorporating distance weights into Z-and X-axis calculations.Subsequently,based on the LRF and voxel segmentation,a feature descriptor based on voxel homogenization was proposed.Moreover,uniform segmentation of cube voxels was performed,considering the eigenvalues of each voxel and its neighboring voxels,thereby enhancing the stability of the description.The performance of the descriptor was strictly tested and evaluated on three public datasets,which exhibited high descriptiveness,robustness,and superior performance compared with other current methods.Furthermore,the descriptor was applied to a 3D registration trial,and the results demonstrated the reliability of our approach.
