LaMAR
Benchmarking Localization and Mapping
for Augmented Reality
Paul-Edouard Sarlin*
·
Mihai Dusmanu*
Johannes L. Schönberger
·
Pablo Speciale
·
Lukas Gruber
·
Viktor Larsson
·
Ondrej Miksik
·
Marc Pollefeys
ECCV 2022
Project Page | Video
LaMar includes multi-sensor streams recorded by AR devices along hundreds of unconstrained trajectories
captured over 2 years in 3 large indoor+outdoor locations.
This repository hosts the source code for LaMAR, a new benchmark for localization and mapping with AR devices in realistic conditions. We are still in the process of fully releasing the benchmark. Here is the release plan:
- Evaluation data: apply here
- Evaluation, baselines, data format: see below
- Additional documentation
- Ground truthing pipeline: to be released soon
- Full raw data: to be released soon
- Leaderboard and evaluation server
Running the evaluation
Requirements:
- Python >= 3.8
- pycolmap installed from source (recommended) or via
pip install pycolmap
- hloc and its dependencies
- raybender
- pyceres
- everything listed in
requirements.txt
, viapython -m pip install -r requirements.txt
Running the single-frame evaluation:
python -m lamar_benchmark.run \
--scene SCENE --ref_id map --query_id query_phone \
--retrieval netvlad --feature sift --matcher mnn
By default, the script assumes that the data was placed in ./data/
and will write the intermediate dumps and final outputs to ./outputs/
.
BibTex citation
Please consider citing our work if you use any code from this repo or ideas presented in the paper:
@inproceedings{sarlin2022lamar,
author = {Paul-Edouard Sarlin and
Mihai Dusmanu and
Johannes L. Schönberger and
Pablo Speciale and
Lukas Gruber and
Viktor Larsson and
Ondrej Miksik and
Marc Pollefeys},
title = {{LaMAR: Benchmarking Localization and Mapping for Augmented Reality}},
booktitle = {ECCV},
year = {2022},
}
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