FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making...

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Main Authors: T. Hackel, J. D. Wegner, K. Schindler
Format: Article
Language:English
Published: Copernicus Publications 2016-06-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/177/2016/isprs-annals-III-3-177-2016.pdf
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spelling doaj-3b4fc50053034b1c951b6bfd1a0bf6d02020-11-25T01:40:05ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502016-06-01III-317718410.5194/isprs-annals-III-3-177-2016FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITYT. Hackel0J. D. Wegner1K. Schindler2Photogrammetry and Remote Sensing, ETH Zürich, Zürich, SwitzerlandPhotogrammetry and Remote Sensing, ETH Zürich, Zürich, SwitzerlandPhotogrammetry and Remote Sensing, ETH Zürich, Zürich, SwitzerlandWe describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/177/2016/isprs-annals-III-3-177-2016.pdf
collection DOAJ
language English
format Article
sources DOAJ
author T. Hackel
J. D. Wegner
K. Schindler
spellingShingle T. Hackel
J. D. Wegner
K. Schindler
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet T. Hackel
J. D. Wegner
K. Schindler
author_sort T. Hackel
title FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
title_short FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
title_full FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
title_fullStr FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
title_full_unstemmed FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY
title_sort fast semantic segmentation of 3d point clouds with strongly varying density
publisher Copernicus Publications
series ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 2194-9042
2194-9050
publishDate 2016-06-01
description We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.
url http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-3/177/2016/isprs-annals-III-3-177-2016.pdf
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AT kschindler fastsemanticsegmentationof3dpointcloudswithstronglyvaryingdensity
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