Geo-accurate model extraction from three-dimensional image-derived point clouds

Abstract

A methodology is proposed for automatically extracting primitive models of buildings in a scene from a three-dimensional point cloud derived from multi-view depth extraction techniques. By exploring the information provided by the two-dimensional images and the three-dimensional point cloud and the relationship between the two, automated methods for extraction are presented. Using the inertial measurement unit (IMU) and global positioning system (GPS) data that accompanies the aerial imagery, the geometry is derived in a world-coordinate system so the model can be used with GIS software. This work uses imagery collected by the Rochester Institute of Technology's Digital Imaging and Remote Sensing Laboratory's WASP sensor platform. The data used was collected over downtown Rochester, New York. Multiple target buildings have their primitive three-dimensional model geometry extracted using modern point-cloud processing techniques.