Probabilistic Range Image Integration For Dsm And True-Orthophoto Generation

Typical photogrammetric processing pipelines for digital surface model (DSM) generation perform aerial triangulation, dense image matching and a fusion step to integrate multiple depth estimates into a consistent 2.5D surface model. The integration is strongly influenced by the quality of the individual depth estimates, which need to be handled robustly. We propose a probabilistically motivated 3D filtering scheme for range image integration. Our approach avoids a discrete voxel sampling, is memory efficient and can easily be parallelized. Neighborhood information given by a Delaunay triangulation can be exploited for photometric refinement of the fused DSMs before rendering true-orthophotos from the obtained models. We compare our range image fusion approach quantitatively on ground truth data by a comparison with standard median fusion. We show that our approach can handle a large amount of outliers very robustly and is able to produce improved DSMs and true-orthophotos in a qualitative comparison with current state-of-the-art commercial aerial image processing software.