LAUSR

Recently, global structure from motion has successfully gained many followers, mainly because of its computational speed. Most of these global methods take the parameters of relative orientation (ROs ) as input and then perform averaging operations. Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global structure from motion. In this article, we propose a method to eliminate wrong ROs which have resulted from repetitive structure and very short baselines. We present two corresponding criteria that indicate the quality of ROs. Repetitive structure is detected based on counts of conjugate points of the various image pairs, while very short baselines are found by inspecting the intersection angles of corresponding image rays. By analyzing these two criteria, we detect and eliminate incorrect ROs. As correct ROs of image pairs with a longer baseline nearly parallel to both viewing directions can be valuable, a method to identify and keep these ROs is also part of our approach. We demonstrate the new method on various data sets, including public benchmarks as well as close-range images and images from unmanned aerial vehicles, by inserting our refined ROs into a global structure-from-motion pipeline. The experiments show that compared to other methods, we can generate the best results.