Volume III-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 177-184, 2016
https://doi.org/10.5194/isprs-annals-III-3-177-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 177-184, 2016
https://doi.org/10.5194/isprs-annals-III-3-177-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Jun 2016

03 Jun 2016

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

Timo Hackel, Jan D. Wegner, and Konrad Schindler Timo Hackel et al.
  • Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland

Keywords: Semantic Classification, Scene Understanding, Point Clouds, LIDAR, Features, Multiscale

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.