基于多尺度特征点识别与局部谱特征的离散数据匹配

Multi-scale feature points detection and local region spectral descriptor for matching unorganized points data

为了实现部分重叠且不同视角的测量数据配准,提出多尺度特征点检测算法,可以从大量的原始数据中提取少量特征点。该算法包括离散曲率计算、双边滤波和特征点计算等步骤,特征点个数可以由尺度参数粗略控制。提出局部形状谱描述器来描述每个特征点的局部形状特性,首先利用局域点的距离和曲率信息构造关系矩阵,然后通过计算关系矩阵的特征值来构造谱描述器,利用该描述器可以方便地计算不同点集中各个特征点的对应关系,进而实现两个数据点集的配准。通过实例验证了该算法有较好的抗噪性和运行速度。

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.