Abstract
The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm.
Highlights
LiDAR (Light Detection and Ranging) is an active remote sensing technology that can provide dense sets of point cloud of a scanned target
The filtered dataset is organized by a K-D tree, and the region growing algorithm is implemented on the basis of an open-source Point Cloud Library (PCL)
The histograms generated by datasets 1 and 2 are shown in Figs 4 and 5, respectively
Summary
LiDAR (Light Detection and Ranging) is an active remote sensing technology that can provide dense sets of point cloud of a scanned target. This technology has been tagged as an emerging practical technique for 3D modeling of smart cities in recent year due to its non-invasive nature, high precision, high resolution, and rapid and flexible data acquisition. The extraction of buildings from the point cloud is a prerequisite in urban 3D modeling. One category is to directly extract the building point cloud after classifying the LiDAR data according to some features. Rottensteiner and Briese [1] combined height difference thresholds, point cloud depths, and image texture features to extract buildings.
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