Large Scale Urban Reconstruction from Remote Sensing Imagery

Abstract

Automatic large-scale stereo reconstruction of urban areas is increasingly becoming a vital aspect for physical simulations as well as for rapid prototyping large scale 3D city models. In this paper we describe an easily reproducible workflow for obtaining an accurate and textured 3D model of the scene, with overlapping aerial images as input. Starting with the initial camera poses and their refinement via bundle adjustment, we create multiple heightmaps by dense stereo reconstruction and fuse them into one Digital Surface Model (DSM). This DSM is then triangulated, and to reduce the amount of data, mesh simplification methods are employed. The resulting 3D mesh is finally projected into each of the input images to obtain the best fitting texture for each triangle. As verification, we provide visual results as well as numerically evaluating the accuracy by comparing the resulting 3D model against ground truth generated by aerial laser scanning (LiDAR).