Matching Algorithm of 3D Point Clouds Based on Multiscale Features and Covariance Matrix Descriptors

The three-dimensional (3D) point cloud is one of the most promising tools for representing and identifying 3D objects. The critical step for matching is to find the appropriate feature descriptors. Two prevalent descriptors are global feature descriptor and local feature descriptor. The former represents the geometric and topological properties of the neighborhood in the entire 3D model, but it can not recognize the covered areas. The local descriptor focuses on narrow neighborhoods, while coarse areas are still present for disambiguation. In this paper, we present a novel matching algorithm of 3D point clouds based on multiple scale features and covariance matrix descriptors. By the combination of the curvature and eigenvalue variation, the key points are detected precisely under multiple scales. Furthermore, we develop a three-scale covariance matrix descriptor to demonstrate local features of the key points. The three-scale covariance matrix descriptor includes the geometric angles, dimensionality, the ratio of projection length and the difference of the curvature, which can describe the local geometric features of the key points more clearly and make feature descriptors more distinguished, especially for key points which are similar in a small range but are not similar in a large range. Besides, a bidirectional proportion strategy is used to find the optimal matching pairs. The algorithm efficiently reduces the mismatching error compared with some local descriptor. Moreover, it is more robust to high noise. Experiments show the efficiency and the robustness of the proposed algorithm for matching three-dimensional point clouds with Gaussian noise and deformed shapes.