Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds

Abstract

As one of the most common features in the man-made environments, straight lines play an important role in many applications. In this paper, we present a new framework to extract line segments from large-scale point clouds. The proposed method is fast to produce results, easy for implementation and understanding, and suitable for various point cloud data. The key idea is to segment the input point cloud into a collection of facets efficiently. These facets provide sufficient information for determining linear features in the local planar region and make line segment extraction become relatively convenient. Moreover, we introduce the concept “number of false alarms” into 3-D point cloud context to filter the false positive line segment detections. We test our approach on various types of point clouds acquired from different ways. We also compared the proposed method with several other methods and provide both quantitative and visual comparison results. The experimental results show that our algorithm is efficient and effective, and produce more accurate and complete line segments than the comparative methods. To further verify the accuracy of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs these line segments on point clouds registration.