点云配准是三维模型重建中的关键步骤。针对传统初配准方法效率低等问题,提出一种结合点云特征的超四点快速鲁棒匹配算法(super four point fast robust matching algorithm,Super4PCS)。首先对点云数据进行尺度不变特征提取,凸显点云...点云配准是三维模型重建中的关键步骤。针对传统初配准方法效率低等问题,提出一种结合点云特征的超四点快速鲁棒匹配算法(super four point fast robust matching algorithm,Super4PCS)。首先对点云数据进行尺度不变特征提取,凸显点云的局部特征;然后把提取的特征点作为Super4PCS算法的初始值,以便实现源点云与目标点云的初配准;最后在初配准的基础上利用最近点迭代(ICP)算法进行精确配准。通过斯坦福兔子点云及实测点云数据对比分析,表明该算法具有更好的配准性能。展开更多
Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,t...Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.展开更多
文摘点云配准是三维模型重建中的关键步骤。针对传统初配准方法效率低等问题,提出一种结合点云特征的超四点快速鲁棒匹配算法(super four point fast robust matching algorithm,Super4PCS)。首先对点云数据进行尺度不变特征提取,凸显点云的局部特征;然后把提取的特征点作为Super4PCS算法的初始值,以便实现源点云与目标点云的初配准;最后在初配准的基础上利用最近点迭代(ICP)算法进行精确配准。通过斯坦福兔子点云及实测点云数据对比分析,表明该算法具有更好的配准性能。
基金Project(61002022)supported by the National Natural Science Foundation of ChinaProject(2012M512168)supported by China Postdoctoral Science Foundation
文摘Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.
