ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS
3D city models in Level-of-Detail 2 (LoD2) are nowadays inevitable for many applications such as solar radiation calculation and energy demand estimation. City-wide models are required which can solely be acquired by fully automatic approaches. In this paper we propose a novel method for the 3D-reco...
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2019-09-01
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Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-23f2290780ca4fa39063d067bddec86d2020-11-25T01:54:29ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502019-09-01IV-4-W8435010.5194/isprs-annals-IV-4-W8-43-2019ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDSY. Dehbi0A. Henn1G. Gröger2V. Stroh3L. Plümer4Institute for Geodesy and Geoinformation, University of Bonn, GermanyTerrestris GmbH & Co KG, Bonn, GermanyCPA ReDev GmbH, Siegburg, GermanyInstitute for Geodesy and Geoinformation, University of Bonn, GermanyFaculty of Geosciences and Environmental Engineering, SWJTUC, Chengdu, China3D city models in Level-of-Detail 2 (LoD2) are nowadays inevitable for many applications such as solar radiation calculation and energy demand estimation. City-wide models are required which can solely be acquired by fully automatic approaches. In this paper we propose a novel method for the 3D-reconstruction of LoD2 buildings with structured roofs and dormers from LIDAR data. We apply a hybrid strategy which combines the strengths of top-down and bottom-up methods. The main contribution is the introduction of an <i>active sampling</i> strategy which applies a cascade of filters focusing on promising samples in an early stage and avoiding the pitfalls of RANSAC based approaches. Such filters are based on prior knowledge represented by (non-parametric) density distributions. Samples are pairs of surflets, i.e. 3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs imply immediately important roof parameters such as azimuth, inclination and ridge height, as well as parameters for internal precision and consistency, giving a good base for assessment and ranking. Ranking of samples leads to a small number of promising hypotheses. Model selection is based on predictions for example of ridge positions which can easily be falsified based on the given observations. Our approach does not require building footprints as prerequisite. They are derived in a preprocessing step using machine learning methods, in particular Support Vector Machines (SVM).https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W8/43/2019/isprs-annals-IV-4-W8-43-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Y. Dehbi A. Henn G. Gröger V. Stroh L. Plümer |
spellingShingle |
Y. Dehbi A. Henn G. Gröger V. Stroh L. Plümer ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
Y. Dehbi A. Henn G. Gröger V. Stroh L. Plümer |
author_sort |
Y. Dehbi |
title |
ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS |
title_short |
ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS |
title_full |
ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS |
title_fullStr |
ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS |
title_full_unstemmed |
ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS |
title_sort |
active sampling and model based prediction for fast and robust detection and reconstruction of complex roofs in 3d point clouds |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2019-09-01 |
description |
3D city models in Level-of-Detail 2 (LoD2) are nowadays inevitable for many applications such as solar radiation calculation and energy demand estimation. City-wide models are required which can solely be acquired by fully automatic approaches. In this paper we propose a novel method for the 3D-reconstruction of LoD2 buildings with structured roofs and dormers from LIDAR data. We apply a hybrid strategy which combines the strengths of top-down and bottom-up methods. The main contribution is the introduction of an <i>active sampling</i> strategy which applies a cascade of filters focusing on promising samples in an early stage and avoiding the pitfalls of RANSAC based approaches. Such filters are based on prior knowledge represented by (non-parametric) density distributions. Samples are pairs of surflets, i.e. 3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs imply immediately important roof parameters such as azimuth, inclination and ridge height, as well as parameters for internal precision and consistency, giving a good base for assessment and ranking. Ranking of samples leads to a small number of promising hypotheses. Model selection is based on predictions for example of ridge positions which can easily be falsified based on the given observations. Our approach does not require building footprints as prerequisite. They are derived in a preprocessing step using machine learning methods, in particular Support Vector Machines (SVM). |
url |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W8/43/2019/isprs-annals-IV-4-W8-43-2019.pdf |
work_keys_str_mv |
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