@article{PB-VRVis-2023-012,
author = {Reyes Aviles, Fernando and Fleck, Philipp and Schmalstieg, Dieter and Arth, Clemens},
title = {Bag of World Anchors for Instant Large-Scale Localization},
year = {2023},
journaltitle = {IEEE TVCG},
doi = {10.1109/TVCG.2023.3320264},
url = {https://www.vrvis.at/publications/PB-VRVis-2023-012},
abstract = {In this work, we present a novel scene description to perform large-scale localization using only geometric constraints.
Our work extends compact world anchors with a search data structure to efficiently p erform l ocalization a nd p ose e stimation of
mobile augmented reality devices across multiple platforms (e.g., HoloLens 2, iPad). The algorithm uses a bag-of-words approach
to characterize distinct scenes (e.g., rooms). Since the individual scene representations rely on compact geometric (rather than
appearance-based) features, the resulting search structure is very lightweight and fast, lending itself to deployment on mobile devices.
We present a set of experiments demonstrating the accuracy, performance and scalability of our novel localization method. In
addition, we describe several use cases demonstrating how efficient cross-platform localization facilitates sharing of augmented reality
experiences.},
keywords = {Camera localization, Correspondence problem, 3D registration, Augmented Reality, Computer vision, Cross-platform, Collaborative, Structural modeling},
}