Paul-Edouard Sarlin*1 ,
Mihai Dusmanu*1
Johannes L. Schönberger2 ,
Pablo Speciale2 ,
Lukas Gruber2 ,
Viktor Larsson2 ,
Ondrej Miksik2 ,
Marc Pollefeys1,2
ETH Zurich1, Microsoft2
European Conference on Computer Vision 2022
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Abstract
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. These benchmarks are often based on small-scale datasets with low scene diversity, captured from stationary cameras, and lack other sensor inputs like inertial, radio, or depth data. Furthermore, their ground-truth (GT) accuracy is mostly insufficient to satisfy AR requirements. To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes. To establish an accurate GT, our pipeline robustly aligns the trajectories against laser scans in a fully automated manner. As a result, 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. The results offer new insights on current research and reveal promising avenues for future work in the field of localization and mapping for AR.
BibTeX Citation
@InProceedings{sarlin2022lamar,
author = {Paul-Edouard Sarlin and
Mihai Dusmanu and
Johannes L. Sch\"onberger and
Pablo Speciale and
Lukas Gruber and
Viktor Larsson and
Ondrej Miksik and
Marc Pollefeys},
title = "{LaMAR}: {B}enchmarking {L}ocalization and {M}apping for {A}ugmented {R}eality",
booktitle = "ECCV",
year = "2022",
}