SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Select a Subject
Refine Ontology Subject

Institute for High Performance Computing, Faculty of Electrical Engineering and Computer Science, Niederrhein University of Applied Sciences, Reinarzstr. 49, 47805 Krefeld and Germany

Context (please use complete words only)

Field

Sort by

1 Papers

Roof Segmentation based on Deep Neural Networks

Topics: Segmentation and Grouping; Machine Learning Technologies for Vision; Deep Learning for Visual Understanding

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

Digital Object Identifier: 10.5220/0007343803260333

Proceedings of VISAPP 2019/VISIGRAPP, ISBN: 978-989-758-354-4

Quick 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 roof structures.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Image and Video Analysis

Segmentation and Grouping