关键词: 多尺度, 谱描述器, 特征点, 数据配准, 离散数据

Abstract: In order to align partly overlapped data clouds measured from different view points, a multi-scale feature points detection algorithm was proposed. A few feature points can be extracted from large number of original data quickly. This algorithm consists of three steps: discrete curvature computing, bilateral filtering and feature points detecting. The number of feature points can be controlled by scale parameter approximately. For each feature point, the authors proposed local shape spectral descriptor to identify its local shape characteristic. Firstly, an affinity matrix was constructed using distance and curvature information of points in neighborhood of a feature point, and then a few of eigenvalues of affinity matrix were used to form a shape descriptor, with which the correspondence between different data sets can be computed easily. Some examples prove that the method is robust and efficient for aligning large number of data with noise.

Key words: multi-scale, spectral descriptor, feature point, data matching, discrete data