Details
| Original language | English |
|---|---|
| Pages (from-to) | 127-138 |
| Number of pages | 12 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 154 |
| Early online date | 11 Jun 2019 |
| Publication status | Published - Aug 2019 |
Abstract
This paper presents a new framework for automatically creating compact building models from aerial LiDAR point clouds, where each point is known to belong to the class building. The approach addresses the issues of non-uniform point density and outlier detection to extract and refine semantic roof structures by a sequence of operations on a label map. We first partition the points into some coarse regions based on a region growing method over the Triangulated Irregular Network (TIN) model. The region label IDs are then projected to a 2D grid map, which is used to refine the roof regions and their boundaries. We design an energy optimization approach on the label map to optimize the region labels. In order to regularize the contours of roof regions extracted from the label map, we propose a new method for refining contour segment vertices, which iteratively filters the normals of contour segments and uses them to guide the update of contour vertices. The effectiveness of this method is evaluated on LiDAR point clouds from different scenes, and its performance is validated by extensive comparisons to state-of-the-art techniques.
Keywords
- Graph cut, LiDAR, Normal vector guidance, TIN model, Urban modelling
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
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In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 154, 08.2019, p. 127-138.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Modelling of buildings from aerial LiDAR point clouds using TINs and label maps
AU - Li, Minglei
AU - Rottensteiner, Franz
AU - Heipke, Christian
N1 - Funding Information: The authors would like to thank all data providers for sharing their data. The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) ( Cramer, 2010 ): http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html . This work was supported in part by the National Natural Science Foundation of China under Grant: 41801342 and the Natural Science Foundation of Jiangsu Province , China, under Grant: BK20170781 .
PY - 2019/8
Y1 - 2019/8
N2 - This paper presents a new framework for automatically creating compact building models from aerial LiDAR point clouds, where each point is known to belong to the class building. The approach addresses the issues of non-uniform point density and outlier detection to extract and refine semantic roof structures by a sequence of operations on a label map. We first partition the points into some coarse regions based on a region growing method over the Triangulated Irregular Network (TIN) model. The region label IDs are then projected to a 2D grid map, which is used to refine the roof regions and their boundaries. We design an energy optimization approach on the label map to optimize the region labels. In order to regularize the contours of roof regions extracted from the label map, we propose a new method for refining contour segment vertices, which iteratively filters the normals of contour segments and uses them to guide the update of contour vertices. The effectiveness of this method is evaluated on LiDAR point clouds from different scenes, and its performance is validated by extensive comparisons to state-of-the-art techniques.
AB - This paper presents a new framework for automatically creating compact building models from aerial LiDAR point clouds, where each point is known to belong to the class building. The approach addresses the issues of non-uniform point density and outlier detection to extract and refine semantic roof structures by a sequence of operations on a label map. We first partition the points into some coarse regions based on a region growing method over the Triangulated Irregular Network (TIN) model. The region label IDs are then projected to a 2D grid map, which is used to refine the roof regions and their boundaries. We design an energy optimization approach on the label map to optimize the region labels. In order to regularize the contours of roof regions extracted from the label map, we propose a new method for refining contour segment vertices, which iteratively filters the normals of contour segments and uses them to guide the update of contour vertices. The effectiveness of this method is evaluated on LiDAR point clouds from different scenes, and its performance is validated by extensive comparisons to state-of-the-art techniques.
KW - Graph cut
KW - LiDAR
KW - Normal vector guidance
KW - TIN model
KW - Urban modelling
UR - http://www.scopus.com/inward/record.url?scp=85067031997&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2019.06.003
DO - 10.1016/j.isprsjprs.2019.06.003
M3 - Article
AN - SCOPUS:85067031997
VL - 154
SP - 127
EP - 138
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -