用于三维点云表示的扩展点特征直方图算法

摘要

Local feature extraction plays an important role in related point cloud applications.Therefore,an EPFH (extended point feature histograms)descriptor for the local feature representation of 3D point cloud was proposed.Each point pair was represented by several invariant pairwise point attributes.Then,a local reference frame was defined for a keypoint and the neighboring points of the keypoint were transformed into the local reference frame.These pairwise points attributing between the neighboring points and the keypoint were accumulated into several sub-features in a set of subspaces.These sub-features were finally concatenated and compressed into an overall feature descriptor.The EPFH descriptor was tested by a popular publicly available Bologna dataset and was compared with several existing methods.Experimental results show that the proposed EPFH method outperforms several existing methods under different levels of noise and point cloud resolutions.%局部特征提取在点云相关应用中具有十分重要的作用，因此提出一种用于点云局部特征表示的扩展点特征直方图描述子。针对邻域点的两两点对提出一系列不变量；在特征点上构建一个局部参考坐标框架以获得特征描述子对旋转和平移的不变性；将关键点局部邻域划分成多个子空间，并依据每个子空间中的点对不变量构建一个直方图；将所有直方图串联起来得到扩展点特征直方图特征描述子。采用Bologna公共数据集对扩展点特征直方图特征描述子的性能进行测试，并与多个现有算法进行对比。结果表明，扩展点特征直方图特征描述子获得了良好的性能，其结果优于多个现有的特征描述子。