Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
| Autor: | Hu, Wenmiao, Zhang, Yichen, Liang, Yuxuan, Yin, Yifang, Georgescu, Andrei, Tran, An, Kruppa, Hannes, Ng, See-Kiong, Zimmermann, Roger |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Proceedings of the 30th ACM International Conference on Multimedia (2022) 6155-6164 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1145/3503161.3548102 |
| Popis: | Street-view imagery provides us with novel experiences to explore different places remotely. Carefully calibrated street-view images (e.g. Google Street View) can be used for different downstream tasks, e.g. navigation, map features extraction. As personal high-quality cameras have become much more affordable and portable, an enormous amount of crowdsourced street-view images are uploaded to the internet, but commonly with missing or noisy sensor information. To prepare this hidden treasure for "ready-to-use" status, determining missing location information and camera orientation angles are two equally important tasks. Recent methods have achieved high performance on geo-localization of street-view images by cross-view matching with a pool of geo-referenced satellite imagery. However, most of the existing works focus more on geo-localization than estimating the image orientation. In this work, we re-state the importance of finding fine-grained orientation for street-view images, formally define the problem and provide a set of evaluation metrics to assess the quality of the orientation estimation. We propose two methods to improve the granularity of the orientation estimation, achieving 82.4% and 72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement compared to previous works. Integrating fine-grained orientation estimation in training also improves the performance on geo-localization, giving top 1 recall 95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two datasets. Comment: This paper has been accepted by ACM Multimedia 2022. This version contains additional supplementary materials |
| Databáze: | arXiv |
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