福建师范大学学报（自然科学版）2017，Vol.33Issue(2)：1-9,17,10.DOI:10.12046/j.issn.1000-5277.2017.02.001
基于点云的SUSAN特征点检测算法在三维重建中的应用
The Application of SUSAN Keypoint Detection Based on Point Cloud in 3D Reconstruction
摘要
Abstract
Aiming at the problem of the keypoint detection algorithms in the process of 3D reconstruction,a smallest univalue segment assimilating nucleus (SUSAN) algorithm based on point cloud is proposed and has been applied to initial registration in 3D reconstruction process.Firstly,the algorithm selected the candidate keypoints by obtaining 3D univalue segment assimilating nucleus of each point with kd-tree structure.Secondly,the features of key points are described by using fast point feature histogram (FPFH).Then,we worked out the transformation matrix using singular value decomposition (SVD) method and got the result of initial alignment of two point clouds.Experiments show that the algorithm has high efficiency and can offer accurate matching of feature points and a good initial position for accurate registration.关键词
三维重建/SUSAN算子/特征描述/快速点特征直方图/迭代最近点算法Key words
3D reconstruction/SUSAN operator/feature description/fast point feature histogram/iterative closest points (ICP)分类
信息技术与安全科学引用本文复制引用
庄恩泽,吴献..基于点云的SUSAN特征点检测算法在三维重建中的应用[J].福建师范大学学报（自然科学版）,2017,33(2):1-9,17,10.基金项目
福建省教育厅资助项目(JA12079) （JA12079）
福建师范大学教学改革研究项目(I201503039) （I201503039）
