The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXVIII-5/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 97–102, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-97-2011
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 97–102, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-97-2011
 
03 Sep 2012
03 Sep 2012

DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

J. Demantké, C. Mallet, N. David, and B. Vallet J. Demantké et al.
  • Universit´e Paris Est, Laboratoire MATIS, IGN 73, avenue de Paris 94165 Saint-Mand´e, Cedex, France

Keywords: point cloud, adaptive neighborhood, scale selection, multi-scale analysis, feature, PCA, eigenvalues, dimensionality

Abstract. This papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for finding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving significant hints for classification and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods.