Efficient Calculation of Multi-Scale Features for MMS Point Clouds

Abstract

Point clouds acquired by Mobile Mapping System (MMS) are useful for creating 3D maps that can be used for autonomous driving and infrastructure development. However, many applications require semantic labels to each point of the point clouds, and the manual labeling process is very time consuming and expensive. Therefore, there is a strong need to develop a method to automatically assigning semantic labels. For automatic labeling tasks, classification methods using multiscale features are effective because multiscale features include features of various scales of roadside objects. Multiscale features are calculated using points inside spheres of multiscale radii centered at each point in a point cloud. When calculating multiscale features that are useful for classifying MMS point clouds, it is necessary to calculate features using relatively large radii. However, when calculating multiscale features using wide range of neighbor points, existing methods, such as kd-tree, require unacceptably long computation time for neighbor search. In this paper, we propose a method to calculate multiscale features in practical time for semantic labeling of large-scale point clouds. In our method, an MMS point cloud is first divided into small spherical regions. Then, radius search using multiscale radii is performed, and multiscale features are calculated using those neighbor points. Our experimental results showed that our method achieved significantly faster computational performance than conventional methods, and multiscale features could be calculated from large-scale point clouds in practical time.