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Open access
Date
2016Type
- Conference Paper
ETH Bibliography
yes
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Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000126659Publication status
publishedExternal links
Editor
Journal / series
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesVolume
Pages / Article No.
Publisher
CopernicusEvent
Subject
Semantic Classification; Scene Understanding; Point Clouds; LIDAR; Features; MultiscaleOrganisational unit
03886 - Schindler, Konrad / Schindler, Konrad
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ETH Bibliography
yes
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