Authors:
Regina Pohle-Fröhlich
1
;
Aaron Bohm
2
;
Peer Ueberholz
2
;
Maximilian Korb
1
and
Steffen Goebbels
1
Affiliations:
1
Institute for Pattern Recognition, Faculty of Electrical Engineering and Computer, Niederrhein University of Applied Sciences, Reinarzstr. 49, 47805 Krefeld and Germany
;
2
Institute for High Performance Computing, Faculty of Electrical Engineering and Computer Science, Niederrhein University of Applied Sciences, Reinarzstr. 49, 47805 Krefeld and Germany
Keyword(s):
Building Reconstruction, Deep Learning, Convolutional Neural Networks, Point Clouds.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
The given paper investigates deep neural networks (DNNs) for segmentation of roof regions in the context of 3D building reconstruction. Point clouds as well as derived depth and density images are used as input data. For 3D building model generation we follow a data driven approach, because it allows the reconstruction of roofs with more complex geometries than model driven methods. For this purpose, we need a preprocessing step that segments roof regions of buildings according to the orientation of their slopes. In this paper, we test three different DNNs and compare results with standard methods using thresholds either on gradients of 2D height maps or on point normals. For the application of a U-Net, we transform point clouds to structured 2D height maps, too. PointNet and PointNet++ directly accept unstructured point clouds as input data. Compared to classical gradient and normal based threshold methods, our experiments with U-Net and PointNet++ lead to better segmentation of roo
f structures.
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