Viktor Larsson
Profile area member
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
Author
Summary, in English
Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant progress has been made, researchers are still mostly driven by unrealistic benchmarks not representative of real-world AR scenarios. In particular, benchmarks are often based on small-scale datasets with low scene diversity, captured from stationary cameras, and lacking other sensor inputs like inertial, radio, or depth data. Furthermore, ground-truth (GT) accuracy is mostly insufficient to satisfy AR requirements. To close this gap, we introduce a new benchmark with a comprehensive capture and GT pipeline, which allow us to co-register realistic AR trajectories in diverse scenes and from heterogeneous devices at scale. To establish accurate GT, our pipeline robustly aligns the captured trajectories against laser scans in a fully automatic manner. Based on this pipeline, we publish a benchmark dataset of diverse and large-scale scenes recorded with head-mounted and hand-held AR devices. We extend several state-of-the-art methods to take advantage of the AR specific setup and evaluate them on our benchmark. Based on the results, we present novel insights on current research gaps to provide avenues for future work in the community.
Publishing year
2022
Language
English
Pages
686-704
Publication/Series
Lecture Notes in Computer Science
Volume
13667
Document type
Conference paper
Publisher
Springer
Topic
- Computer graphics and computer vision
Conference name
17th European Conference on Computer Vision, ECCV 2022
Conference date
2022-10-23 - 2022-10-27
Conference place
Tel Aviv, Israel
Status
Published
ISBN/ISSN/Other
- ISSN: 0302-9743
- ISSN: 1611-3349
- ISBN: 978-3-031-20070-0
- ISBN: 978-3-031-20071-7