Automatic segmentation of Lidar data into coplanar point clusters using an octree-based split-and-merge algorithm

Miao Wang, Yi Hsing Tseng

研究成果: Article

37 引文 斯高帕斯(Scopus)

摘要

Lidar (light detection and ranging) point cloud data contain abundant three-dimensional (3D) information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical lidar dataset of artificial structures. To explore implicitly contained spatial information, this study developed an automatic scheme to segment a lidar point cloud dataset into coplanar point clusters. The central mechanism of the proposed method is a split-and-merge segmentation based on an octree structure. Plane fitting serves as an engine in the mechanism that evaluates how well a group of points fits to a plane. Segmented coplanar points and derived parameters of their best-fit plane are obtained through the process. This paper also provides algorithms to derive various geometric properties of segmented coplanar points, including inherent properties of a plane, intersections of planes, and properties of point distribution on a plane. Several successful cases of handling airborne and terrestrial lidar data as well as a combination of the two are demonstrated. This method should improve the efficiency of object modelling using lidar data.

原文English
頁(從 - 到)407-420
頁數14
期刊Photogrammetric Engineering and Remote Sensing
76
發行號4
DOIs
出版狀態Published - 2010 四月

All Science Journal Classification (ASJC) codes

  • Computers in Earth Sciences

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