isprs archives XLI B3 11 2016
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For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud
1) The initial building contours are extracted from the classification results of LiDAR point cloud. 2) The internal energy and area energy of the initial contour are
The dataset considered here represents the test and training site of the Technical Center in Meppen , Northern Germany. A DSM sampled with a resolution of 0.5m from an ALS point
Then, 3D structure lines are obtained from the 3D cloud points acquired with 3D cameras and projected onto the 2D images to generate 2D structure lines, which are combined with the
In this study, approaches (Approach 1 and Approach 2) have been proposed for the automatic point based classification of raw LiDAR point cloud with the combine
The next subject of the analysis was glass bottle of perfumes with a different type of glass. During the scanning, it was observed that the glass objects are
In this study, we propose a processing of point cloud data by extracting geometrical features of 3D point cloud using Fast Point Feature Histogram (FPFH) and a classification of
This method implies a mainly planar scene to avoid mismatches; (ii) coregistration of both the dense 3D point clouds from RGB images and from TIR images by coregistering 2D
