Quality Enhancement Techniques for Building Models Derived from Sparse Point Clouds

Steffen Goebbels, Regina Pohle-Fröhlich

2017

Abstract

This paper describes processing steps that improve both geometric consistency and appearance of CityGML models. In addition to footprints from cadastral data and sparse point clouds obtained from airborne laser scanning, we use true orthophotos to better detect and model edges. Also, procedures to heal self-intersection of polygons and non-planarity of roof facets are presented. Additionally, the paper describes an algorithm to cut off invisible parts of walls. We incorporate these processing steps into our data based framework for building model generation from sparse point clouds. Results are presented for German cities of Krefeld and Leverkusen.

Paper Citation

in Harvard Style

Goebbels S. and Pohle-Fröhlich R. (2017). Quality Enhancement Techniques for Building Models Derived from Sparse Point Clouds . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017) ISBN 978-989-758-224-0, pages 93-104. DOI: 10.5220/0006103300930104

in Bibtex Style

@conference{grapp17,
author={Steffen Goebbels and Regina Pohle-Fröhlich},
title={Quality Enhancement Techniques for Building Models Derived from Sparse Point Clouds},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)},
year={2017},
pages={93-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006103300930104},
isbn={978-989-758-224-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, (VISIGRAPP 2017)
TI - Quality Enhancement Techniques for Building Models Derived from Sparse Point Clouds
SN - 978-989-758-224-0
AU - Goebbels S.
AU - Pohle-Fröhlich R.
PY - 2017
SP - 93
EP - 104
DO - 10.5220/0006103300930104