Details

Autor(en) / Beteiligte

• Hu, Zongtian
• Chen, Chi
• Yang, Bisheng
• Wang, Zhiye
• Ma, Ruiqi
• Wu, Weitong
• Sun, Wenlu

Titel

Geometric feature enhanced line segment extraction from large-scale point clouds with hierarchical topological optimization

Ist Teil von

• International journal of applied earth observation and geoinformation, 2022-08, Vol.112, p.102858, Article 102858

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2022

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• •Enhanced geometric and multi-scale deep edge features were fused to locate 3D lines.•Hierarchical topological optimization was performed to refine the 3D lines.•The proposed method improved the Completeness and Correctness of the structure lines extracted from large-scale point clouds collected in complex urban environment. As the most common primitives, line segments play an essential role in the vectorized reconstruction of artificial scenes. In this paper, a geometric feature enhanced line extraction method with Hierarchical Topological Optimization is proposed to extract line segments from large-scale point clouds. Firstly, 3D projection plane regions are extracted from point clouds using region growing and merging; Secondly, the point clouds in the detected planar region are projected onto 2D grids for efficiently geometry features enhanced line detection. Specifically, the edge feature index of each point inside each projection grid is calculated and fused to represent the edge-geometry enhanced feature. The likelihood of edge presenting on the projection grids is produced using a pre-trained convolutional neural network that combines multi-scale edge locating outputs. Then, line segments on the projected edge maps are extracted by the MCMLSD algorithm and further back-projected into 3D space to form the 3D line segment candidates. Thirdly, the hierarchical topological relationships between the contour and line segments are used to optimize the candidate 3D line segments. The optimization process consists of merging perceptually accurate 3D line segments, closing the contour line based on the contour and merging plane intersection lines based on line segments. The point clouds of seven large-scale outdoor scenes from the Semantic 3D and WHU-TLS public datasets are selected for qualitative and quantitative evaluations. Experiments show that the proposed approach can efficiently extract line segments that represent the geometric characteristics of the scenes comprehensively and accurately. Compared with the SOTA line segments extraction algorithms (i.e., Lu et al. (2019), Zhang et al. (2020a)), the proposed method filters out major miscellaneous/broken lines and extract more complete line segments. The Completeness and Correctness of the line segments extraction results reach 86% on average compared with manual labeled ground truth (dl, ds: 0.5,0.5), at an average processing speed of 25,000 points per second.