Abstract
Abstract. Automatic processing and object extraction from 3D laser point cloud is one of the major research topics in the field of photogrammetry. Segmentation is an essential step in the processing of laser point cloud, and the quality of extracted objects from laser data is highly dependent on the validity of the segmentation results. This paper presents a new approach for reliable and efficient segmentation of planar patches from a 3D laser point cloud. In this method, the neighbourhood of each point is firstly established using an adaptive cylinder while considering the local point density and surface trend. This neighbourhood definition has a major effect on the computational accuracy of the segmentation attributes. In order to efficiently cluster planar surfaces and prevent introducing ambiguities, the coordinates of the origin's projection on each point's best fitted plane are used as the clustering attributes. Then, an octree space partitioning method is utilized to detect and extract peaks from the attribute space. Each detected peak represents a specific cluster of points which are located on a distinct planar surface in the object space. Experimental results show the potential and feasibility of applying this method for segmentation of both airborne and terrestrial laser data.
Highlights
Laser scanning is considered as a leading technology for the acquisition of high density three-dimensional spatial information
Many methodologies have been suggested for the segmentation of 3D laser data in the past decade, which are generally categorized in three classes: region growing, model fitting methods, and clustering of attributes
The proposed segmentation approach can be applied for both airborne and terrestrial laser data; the defined thresholds in this algorithm should be adjusted for different laser datasets based on the scanning system characteristics and segmentation objectives
Summary
Laser scanning is considered as a leading technology for the acquisition of high density three-dimensional spatial information. Tovari and Pfeifer (2005) introduced a region growing segmentation method for airborne laser data They used the estimated normal vector for each point and its distance to the growing plane as the similarity criteria for growing the seed point. Pu and Vosselman (2006) proposed a region growing method for terrestrial laser data based on proximity of points and global planarity criteria These methods are highly dependent on selected seed points and errors in defining these points will affect the segmentation process (Besl and Jain, 1998). Filin and Pfeifer (2006) introduced a segmentation method based on the normal vectors derived using a slope adaptive neighbourhood They used the slopes of the normal vector in the X and Y directions and height difference between the point and its neighbourhood as the clustering attributes. This height difference attribute was used to guarantee the distinction between parallel planes, which share the same normal vector slopes. Biosca and Lerma (2008) suggested a fuzzy clustering approach in combination with a similarity-based cluster merging for segmentation of a terrestrial laser point cloud. Kim et al (2007) proposed a method for segmentation of planar
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