Unsupervised Segmentation of Point Clouds From Buildings Using Hierarchical Clustering Based on Gestalt Principles

Abstract

Segmentation is a fundamental step for parsing the point clouds of three-dimensional (3-D) scenes, which normally contain a wide variety of complex objects and structures with a large amount of points. In this paper, we propose a voxel-based point cloud segmentation method using Gestalt principles under a hierarchical clustering framework, allowing a completely automatic but parametric process for segmenting 3-D scenes of buildings. The voxel-based data structure can increase the efficiency and robustness of the segmentation process. By the use of Gestalt principles, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, which can be applied to general applications. The clustering of patches in our method is carried out on the basis of the local geometric information, which is modeled by the probabilistic formulation and solved by the graphical model. Experiments using terrestrial laser scanning dataset have demonstrated that our proposed method can achieve good results, especially for complex scenes and nonplanar surfaces of objects. The quantitative comparison between our method and other representative segmentation methods (i.e., region growing, voxel-based incremental segmentation, locally convex connected patches, etc.) also confirms the effectiveness and efficiency of our method, with overall $F_1$ measures better than 0.66 for our datasets under complex situations of urban scenes with irregular shaped buildings.