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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| 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 |
| Summary: | 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. |
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| ISSN: | 2194-9042 2194-9050 |