Object extraction in urban environments from large-scale dynamic point cloud datasets

In this paper, we introduce a system framework which can automatically interpret large point cloud datasets collected from dense urban areas by moving aerial or terrestrial Lidar platforms. We propose novel algorithms for region segmentation, motion analysis, object identification and population level scene analysis which steps can highly contribute to organize the data into a semantically indexed structure, enabling quick responses for content based user queries about the environment. The system is tested on real Lidar data, and for demonstration quantitative evaluation is given on vehicle detection.