Constructing 3D City Models by Merging Aerial and Ground Views
IEEE Computer Graphics and Applications
Automatic Generation of High-Quality Building Models from Lidar Data
IEEE Computer Graphics and Applications
3D Building Detection and Modeling from Aerial LIDAR Data
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Integrating LiDAR, Aerial Image and Ground Images for Complete Urban Building Modeling
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A Bayesian Approach to Building Footprint Extraction from Aerial LIDAR Data
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Topology Repair of Solid Models Using Skeletons
IEEE Transactions on Visualization and Computer Graphics
Aerial Lidar Data Classification using AdaBoost
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Urban site modeling from LiDAR
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Generating raster DEM from mass points via TIN streaming
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
3D building reconstruction from LiDAR data
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
2.5D dual contouring: a robust approach to creating building models from Aerial LiDAR point clouds
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Exploiting semantics of web services for geospatial data fusion
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Spatial Semantics and Ontologies
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part I
| Hi-index | 0.00 |
This paper presents an automatic algorithm which reconstructs building models from airborne LiDAR (light detection and ranging) data of urban areas. While our algorithm inherits the typical building reconstruction pipeline, several major distinct features are developed to enhance efficiency and robustness: 1) we design a novel vegetation detection algorithm based on differential geometry properties and unbalanced SVM; 2) after roof patch segmentation, a fast boundary extraction method is introduced to produce topology-correct water tight boundaries; 3) instead of making assumptions on the angles between roof boundary lines, we propose a data-driven algorithm which automatically learns the principal directions of roof boundaries and uses them in footprint production. Furthermore, we show the extendability of our algorithm by supporting non-flat object patterns with the help of only a few user interactions. We demonstrate the efficiency and accuracy of our algorithm by showing experiment results on urban area data of several different data sets.