Clustering irregular spaced lidar TINs for 3D reconstruction. Shorter, N., Kasparis, T., Georgiopoulos, M., & Anagnostopoulos, G. C. 2008.
Clustering irregular spaced lidar TINs for 3D reconstruction [link]Paper  abstract   bibtex   
Several sets of features, existent in triangulated, irregularly spaced LiDAR data, are extracted, conditioned, and presented to a number of clustering algorithms with the intent to recognize planar structures within the data. From those planar structures, encoded by the clustering algorithms, 3D models are then reconstructed. The purpose of this paper is to evaluate the performance of these clustering algorithms' ability to accurately cluster coplanar triangles into groups correlating to a given, depicted structure's roof planes. Several preprocessing, input conditioning procedures are presented. Also, a post processing planar regression algorithm is implemented to further refine the clustering algorithms' results to realize 3D reconstructed models of the LiDAR points. Furthermore, membership criterions, for a given triangle to correctly belong to a roof cluster, are proposed. Measures in which to evaluate the performance of the clustering algorithms ability to accurately encode the triangulated LiDAR data are also proposed. Copyright © 2008 by the International Institute of Informatics and Systemics.
@Conference{Shorter2008,
  author        = {Shorter, N.S. and Kasparis, T. and Georgiopoulos, Michael and Anagnostopoulos, Georgios C.},
  title         = {Clustering irregular spaced lidar TINs for 3D reconstruction},
  booktitle     = {Engineering and Technological Innovation (IMETI 2008), Proceedings of the International Multi-Conference on},
  year          = {2008},
  volume        = {1},
  pages         = {209-214},
  abstract      = {{Several sets of features, existent in triangulated, irregularly spaced
	LiDAR data, are extracted, conditioned, and presented to a number
	of clustering algorithms with the intent to recognize planar structures
	within the data. From those planar structures, encoded by the clustering
	algorithms, 3D models are then reconstructed. The purpose of this
	paper is to evaluate the performance of these clustering algorithms'
	ability to accurately cluster coplanar triangles into groups correlating
	to a given, depicted structure's roof planes. Several preprocessing,
	input conditioning procedures are presented. Also, a post processing
	planar regression algorithm is implemented to further refine the
	clustering algorithms' results to realize 3D reconstructed models
	of the LiDAR points. Furthermore, membership criterions, for a given
	triangle to correctly belong to a roof cluster, are proposed. Measures
	in which to evaluate the performance of the clustering algorithms
	ability to accurately encode the triangulated LiDAR data are also
	proposed. Copyright © 2008 by the International Institute of Informatics
	and Systemics.}},
  affiliation   = {University of Central Florida, FL, 32816, United States; Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, 32816, United States; Electrical and Computer Engineering Department, University of Central Florida, Orlando, FL 32816, United States; Department of Electrical and Computer Engineering, Florida Institute of Technology, Melbourne, FL 32901, United States},
  document_type = {Conference Paper},
  journal       = {IMETI 2008 - International Multi-Conference on Engineering and Technological Innovation, Proceedings},
  owner         = {georgio},
  source        = {Scopus},
  timestamp     = {2015.08.16},
  url           = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84893166355&partnerID=40&md5=6cbf8e62e7e87ced82ccc48c056ca319},
}

Downloads: 0