Segmentation of LiDAR Point Cloud Based on Similarity Measures in Multi-dimension Euclidean Space
The segmentation of LiDAR point cloud is a key but difficult step for 3D reconstruction of architecture. Many researchers have tried to develop segmentation methods including edge-based, surface-based and cluster-based segmentation, etc. In this paper, we present a point data segmentation method based on similarity measures in multi-dimension Euclidean space. The main workflow of this method is made by calculating point normal vector, transforming color values, calculating Euclidean distance and angle in multi-dimension space, comparing the similarity among adjacent points, and segmenting the raw points set at last. The proposed method takes the both advantages of geo-metrical segmentation and color-metrical segmentation as compared with three different segmentation methods. It has been applied to LiDAR point data obtained by TLS (terrain laser scanner), the experiment results show that the segmentation method is promising.
KeywordsLiDAR Point cloud segmentation Euclidean Space
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