LaMAR: Benchmarking Localization and Mapping for AR

Paul-Edouard Sarlin^*1, Mihai Dusmanu^*1
Johannes L. Schönberger², Pablo Speciale², Lukas Gruber², Viktor Larsson², Ondrej Miksik², Marc Pollefeys^1,2

ETH Zurich¹, Microsoft²

European Conference on Computer Vision 2022

Paper

Dataset

Code

Leaderboard

Poster

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",
}