Improving the Pair Selection and the Model Fusion Steps of Satellite Multi-View Stereo Pipelines

Abstract

Multi-view stereo reconstruction of scenes from satellite images is traditionally performed with a pair-wise stereo-vision approach: (1) multiple views are grouped into pairs, (2) each pair is processed by two-view stereo methods producing an elevation model or point cloud, lastly (3) the pair-wise reconstructions are integrated and filtered to obtain a final result. These steps are organized in a pipeline and the end-to-end performance of reconstructions depends on the behavior of these steps. This work introduces two changes that increase the performance of the reconstructions: a new pair selection approach and a new integration method are presented. The new pair selection replaces commonly used heuristics with a principled criterion that predicts the completeness of a pair based on offline simulations. The presented integration method is based on an iterated bilateral filter. Experiments show that these changes yield a systematic improvement on the performance of the pipeline.