Lidar (light detection and ranging) data implicitly contains abundant three-dimensional spatial information. The segmentation of lidar point clouds is the key procedure for transforming implicit spatial information into explicit spatial information. Common criteria used for point cloud segmentation are proximity and coherence of point distribution. An effective segmentation algorithm may apply various steps or combinations of criteria depending on the application. This paper proposes a four-step segmentation method for lidar point clouds to deliver incremental segmentation results. Segmentation results of each step can provide the fundamental data for the next step. In the first step, the input point cloud is organised into an octree-structured voxel space, in which the point neighbourhood is established. In the second step, connected voxels which are not empty are grouped to obtain grouped points based on proximity. The third step is a coplanar point segmentation based on both coherence and proximity, which was performed on each point group obtained in the second step. Finally, neighbouring coplanar point groups are merged into "co-surface" point groups based on the criteria of plane connection and intersection. This scheme enables an incremental retrieval and analysis of a large lidar data-set. Experimental results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial lidar data. It is anticipated that the incremental segmentation results will be useful for object modelling using lidar data.
All Science Journal Classification (ASJC) codes
- Engineering (miscellaneous)
- Computer Science Applications
- Computers in Earth Sciences
- Earth and Planetary Sciences (miscellaneous)