3D City Modeling from Street-Level Data for Augmented Reality Applications

Abstract: We present a method for automatically creating compact and accurate 3D city models needed for enhanced Augmented Reality applications. The input data are panorama images and LIDAR scans collected at street level and positioned using IMU and a GPS. Our method corrects for the GPS error and the IMU drift to produce a globally consistent and well-registered data set for the whole city. We use structure from motion and skyline detection to complement the limited range of LIDAR data. Additionally, we propose a novel reconstruction technique that exploits architectural properties of urban environments to create an accurate 3D city model from incomplete data. Our method is able to process an entire city, or several terabytes of data, in a matter of days. We show that our reconstruction achieves higher accuracy than a commercial solution.