Complete residential urban area reconstruction from dense aerial LiDAR point clouds

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.

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► We present an automatic system to reconstruct 3D urban models for residential areas.
► We differentiate buildings and trees in terms of the 2.5D characteristic.
► We develop a robust classification method to classify trees, buildings, and ground.
► Key urban elements such as buildings and trees are reconstructed respectively.
► For each residential area, a hybrid model is created from aerial LiDAR scans.