2008
DOI: 10.1016/j.isprsjprs.2007.07.010
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Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods

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Cited by 164 publications
(81 citation statements)
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“…However, the weakness of this approach is connected with a proper choice of the seed points. Furthermore, selecting different seed points may results different segmentation regions [4]. In addition, this algorithm tends to generate distorted boundaries due to segment objects in the region level instead of pixel level [5].…”
Section: Related Workmentioning
confidence: 99%
“…However, the weakness of this approach is connected with a proper choice of the seed points. Furthermore, selecting different seed points may results different segmentation regions [4]. In addition, this algorithm tends to generate distorted boundaries due to segment objects in the region level instead of pixel level [5].…”
Section: Related Workmentioning
confidence: 99%
“…Applications usually require extensive processing of point cloud for extracting information on the features of interest to derive the final product (Biosca and Lerma, 2008;Vosselman et al, 2004). Analysis options with a set of unstructured 3D points are limited (Biosca and Lerma, 2008).…”
Section: Motivationmentioning
confidence: 99%
“…Analysis options with a set of unstructured 3D points are limited (Biosca and Lerma, 2008). Apart from the fact that the point data are noisy and not perfectly sampled, lidar acquisitions may also have poor sampling for almost vertical scans (Golovinsky and Funkhouser, 2009).…”
Section: Motivationmentioning
confidence: 99%
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“…All these methodologies have been applied to relatively simple datasets that do not represent the typical complexity of natural scenes. With terrestrial laser scanning data, extraction of planar faces has mostly been considered, e.g., Dold and Brenner [19], Biosca and Lerma [20] who use fuzzy clustering to extract planar surfaces, or Wang and Tseng [21] who use an octree based approach. Gorte [22] extracts planar faces using a panoramic representation of the range data.…”
Section: Introductionmentioning
confidence: 99%