Accurate and complete line segment extraction for large-scale point clouds

Abstract

Line segment extraction from point clouds is critical for analyzing and understanding large-scale scenes. The main challenge is to generate line segments accurately as well as completely. However, state-of-the-art approaches continue to struggle with this issue. In this paper, we propose a novel method for effectively generating line segments from large-scale point clouds. To this end, we design a weighted centroid displacement scheme for identifying comprehensive feature points. Then, we employ an L1-median optimization to refine the identified feature points to perceive geometric edges on the underlying surface accurately. Following that, we generate complete and concise line segments from the refined feature points by designing three geometric operators: clustering, exclusion, and assimilation. The clustering operator generates the initial line segments based on optimized feature points, and the exclusion operator and the assimilation operator ensure the completeness and continuity of these line segments. We evaluate our approach on various scene point clouds, such as TLS, MLS, and ALS data. Extensive experimental results show that our method can outperform the competing approaches in terms of both accuracy and efficiency.

Keywords

• Line segment extraction
• Large-scale point clouds
• Feature point detection