Challenges in Fusion of Heterogeneous Point Clouds

Abstract

Different platforms and sensors are used to derive 3d models of urban scenes. 3d reconstruction from satellite and aerial images are used to derive sparse models mainly showing ground and roof surfaces of entire cities. In contrast to such sparse models, 3d reconstructions from UAV or ground images are much denser and show building facades and street furniture as traffic signs and garbage bins. Furthermore, point clouds may also get acquired with LiDAR by mobile mapping systems. Point clouds do not only differ in the viewpoints, but also in their scales and point densities. Consequently, the fusion of such heterogeneous point clouds is highly challenging. Regarding urban scenes, another challenge is the occurence of only a few parallel planes where it is difficult to find the correct rotation parameters. We discuss the advantages and limitations of available methods showing results on building scenes from Germany. One of the most challenging scenes in our experiments arises from the fusion of a point cloud reconstructed from satellite images and the LiDAR point cloud from a mobile mapping system showing only a single building and its environment.