Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ACM SIGGRAPH 2007 papers
Approximate image-based tree-modeling using particle flows
ACM SIGGRAPH 2007 papers
Knowledge and heuristic-based modeling of laser-scanned trees
ACM Transactions on Graphics (TOG)
Aerial Lidar Data Classification using AdaBoost
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
ACM SIGGRAPH Asia 2008 papers
Fusion of Feature- and Area-Based Information for Urban Buildings Modeling from Aerial Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
2D tree detection in large urban landscapes using aerial LiDAR data
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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
Texture-lobes for tree modelling
ACM SIGGRAPH 2011 papers
Adaptive partitioning of urban facades
Proceedings of the 2011 SIGGRAPH Asia Conference
2.5D building modeling by discovering global regularities
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Building large urban environments from unstructured point data
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Modeling residential urban areas from dense aerial LiDAR point clouds
CVM'12 Proceedings of the First international conference on Computational Visual Media
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We present an automatic system to reconstruct 3D urban models for residential areas from aerial LiDAR scans. The key difference between downtown area modeling and residential area modeling is that the latter usually contains rich vegetation. Thus, we propose a robust classification algorithm that effectively classifies LiDAR points into trees, buildings, and ground. The classification algorithm adopts an energy minimization scheme based on the 2.5D characteristic of building structures: buildings are composed of opaque skyward roof surfaces and vertical walls, making the interior of building structures invisible to laser scans; in contrast, trees do not possess such characteristic and thus point samples can exist underneath tree crowns. Once the point cloud is successfully classified, our system reconstructs buildings and trees respectively, resulting in a hybrid model representing the 3D urban reality of residential areas.