Outliers in 3D point clouds applied to efficient image-guided localization

Abstract

In this work, the tasks of improving positioning efficiency and minimization of space requirements in image-based navigation are explored. We proved the assumption that it is possible to reduce imagematching time and to increase storage capacities by removing outliers from 3D models used for localization, by applying three outlier removal methods to our datasets and observing the localization associated with the resulting models.