Details
| Original language | English |
|---|---|
| Title of host publication | 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011 |
| Pages | 16-24 |
| Number of pages | 9 |
| Publication status | Published - Dec 2011 |
| Event | 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011 - Chicago, IL, United States Duration: 1 Nov 2011 → 4 Nov 2011 |
Publication series
| Name | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
|---|
Abstract
This paper presents a generative statistical approach to 3D building roof reconstruction from airborne laser scanning point clouds. In previous works bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Since the laser scanning data of urban scenes often contain extra structures and artefacts due to tree clutter, reflection from windows, water features, etc., bottom-up reconstructions may result in a number of incomplete or irregular roof parts. We propose a new top-down statistical method for roof reconstruction, in which the bottom-up efforts mentioned above are no more required. Based on a predefined primitive library we conduct a generative modeling to construct the target roof that fit the data. Allowing overlapping, primitives are assembled and, if necessary, merged to present the entire roof. The selection of roof primitives, as well as the sampling of their parameters, is driven by the Reversible Jump Markov Chain Monte Carlo technique. Experiments are performed on both low-resolution (1m) and high-resolution (0.18m) data-sets. For high-resolution data we also show the possibility to reconstruct smaller roof features, such as chimneys and dormers. The results show robustness despite the clutter and flaws in the data points and plausibility in reconstruction.
Keywords
- 3D reconstruction, building, LIDAR, model selection, point cloud, RJMCMC, statistical modeling
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Information Systems
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19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011. 2011. p. 16-24 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - 3D building roof reconstruction from point clouds via generative models
AU - Huang, Hai
AU - Brenner, Claus
AU - Sester, Monika
PY - 2011/12
Y1 - 2011/12
N2 - This paper presents a generative statistical approach to 3D building roof reconstruction from airborne laser scanning point clouds. In previous works bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Since the laser scanning data of urban scenes often contain extra structures and artefacts due to tree clutter, reflection from windows, water features, etc., bottom-up reconstructions may result in a number of incomplete or irregular roof parts. We propose a new top-down statistical method for roof reconstruction, in which the bottom-up efforts mentioned above are no more required. Based on a predefined primitive library we conduct a generative modeling to construct the target roof that fit the data. Allowing overlapping, primitives are assembled and, if necessary, merged to present the entire roof. The selection of roof primitives, as well as the sampling of their parameters, is driven by the Reversible Jump Markov Chain Monte Carlo technique. Experiments are performed on both low-resolution (1m) and high-resolution (0.18m) data-sets. For high-resolution data we also show the possibility to reconstruct smaller roof features, such as chimneys and dormers. The results show robustness despite the clutter and flaws in the data points and plausibility in reconstruction.
AB - This paper presents a generative statistical approach to 3D building roof reconstruction from airborne laser scanning point clouds. In previous works bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Since the laser scanning data of urban scenes often contain extra structures and artefacts due to tree clutter, reflection from windows, water features, etc., bottom-up reconstructions may result in a number of incomplete or irregular roof parts. We propose a new top-down statistical method for roof reconstruction, in which the bottom-up efforts mentioned above are no more required. Based on a predefined primitive library we conduct a generative modeling to construct the target roof that fit the data. Allowing overlapping, primitives are assembled and, if necessary, merged to present the entire roof. The selection of roof primitives, as well as the sampling of their parameters, is driven by the Reversible Jump Markov Chain Monte Carlo technique. Experiments are performed on both low-resolution (1m) and high-resolution (0.18m) data-sets. For high-resolution data we also show the possibility to reconstruct smaller roof features, such as chimneys and dormers. The results show robustness despite the clutter and flaws in the data points and plausibility in reconstruction.
KW - 3D reconstruction
KW - building
KW - LIDAR
KW - model selection
KW - point cloud
KW - RJMCMC
KW - statistical modeling
UR - http://www.scopus.com/inward/record.url?scp=84863023825&partnerID=8YFLogxK
U2 - 10.1145/2093973.2093977
DO - 10.1145/2093973.2093977
M3 - Conference contribution
AN - SCOPUS:84863023825
SN - 9781450310314
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 16
EP - 24
BT - 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
T2 - 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2011
Y2 - 1 November 2011 through 4 November 2011
ER -