Classification-based scene modeling for urban point clouds

Abstract

The three-dimensional modeling of urban scenes is an important topic that can be used for various applications. We present a comprehensive strategy to reconstruct a scene from urban point clouds. First, the urban point clouds are classified into the ground points, planar points on the ground, and nonplanar points on the ground by using the support vector machine algorithm which takes several differential geometry properties as features. Second, the planar points and nonplanar points on the ground are segmented into patches by using different segmentation methods. A collection of characteristics of point cloud segments like height, size, topological relationship, and ratio between the width and length are applied to extract different objects after removing the unwanted segments. Finally, the buildings, ground, and trees in the scene are reconstructed, resulting in a hybrid model representing the urban scene. Experimental results demonstrate that the proposed method can be used as a robust way to reconstruct the scene from the massive point clouds.