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  4. CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
 
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conference paper

CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data

Yan, Qi
•
Zheng, Jianhao
•
Reding, Simon
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January 1, 2022
2022 Ieee/Cvf Conference On Computer Vision And Pattern Recognition (Cvpr 2022)
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data. Despite significant progress in recent years, most learning-based approaches to visual localization target at a single domain and require a dense database of geo-tagged images to function well. To mitigate the data scarcity issue and improve the scalability of the neural localization models, we introduce TOPO-DataGen, a versatile synthetic data generation tool that traverses smoothly between the real and virtual world, hinged on the geographic camera viewpoint. New large-scale sim-to-real benchmark datasets are proposed to showcase and evaluate the utility of the said synthetic data. Our experiments reveal that synthetic data generically enhances the neural network performance on real data. Furthermore, we introduce CrossLoc, a cross-modal visual representation learn- ing approach to pose estimation that makes full use of the scene coordinate ground truth via self-supervision. Without any extra data, CrossLoc significantly outperforms the state-of-the-art methods and achieves substantially higher real-data sample efficiency. Our code and datasets are all available at crossloc.githup.io.

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Type
conference paper
DOI
10.1109/CVPR52688.2022.01684
Web of Science ID

WOS:000870783003016

Author(s)
Yan, Qi
•
Zheng, Jianhao
•
Reding, Simon
•
Li, Shanci
•
Doytchinov, Iordan  
Date Issued

2022-01-01

Publisher

IEEE COMPUTER SOC

Publisher place

Los Alamitos

Published in
2022 Ieee/Cvf Conference On Computer Vision And Pattern Recognition (Cvpr 2022)
ISBN of the book

978-1-6654-6946-3

Series title/Series vol.

IEEE Conference on Computer Vision and Pattern Recognition

Start page

17337

End page

17347

Subjects

Computer Science, Artificial Intelligence

•

Imaging Science & Photographic Technology

•

Computer Science

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
TOPO  
Event nameEvent placeEvent date
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

New Orleans, LA

Jun 18-24, 2022

Available on Infoscience
January 16, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/193695
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