Creating Large-Scale City Models from 3D-Point Clouds: A Robust Approach with Hybrid Representation
International Journal of Computer Vision
Visually-complete aerial LiDAR point cloud rendering
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Modeling residential urban areas from dense aerial LiDAR point clouds
CVM'12 Proceedings of the First international conference on Computational Visual Media
Semantic decomposition and reconstruction of residential scenes from LiDAR data
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
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We present a robust method for modeling cities from unstructured point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. Buildings are modeled by an original approach which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. We experimentally validate the approach on complex urban structures and large urban scenes of millions of points.