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Attribute Filtering of Urban Point Clouds Using Max-Tree on Voxel Data

Florent Guiotte 1, 2 Sébastien Lefèvre 2 Thomas Corpetti 1
1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper deals with morphological characterization of un-structured 3D point clouds issued from LiDAR data. A large majority of studies first rasterize 3D point clouds onto regular 2D grids and then use standard 2D image processing tools for characterizing data. In this paper, we suggest instead to keep the 3D structure as long as possible in the process. To this end, as raw LiDAR point clouds are unstructured, we first propose some voxelization strategies and then extract some morphological features on voxel data. The results obtained with attribute filtering show the ability of this process to efficiently extract useful information .
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Submitted on : Wednesday, November 13, 2019 - 6:30:25 PM
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Florent Guiotte, Sébastien Lefèvre, Thomas Corpetti. Attribute Filtering of Urban Point Clouds Using Max-Tree on Voxel Data. Mathematical Morphology and Its Applications to Signal and Image Processing, pp.391-402, 2019, ⟨10.1007/978-3-030-20867-7_30⟩. ⟨hal-02343890⟩

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