Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials

• URL: http://arxiv.org/abs/2307.03398v2
• Date: Thu, 13 Jul 2023 06:47:45 GMT
• Title: Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
• Authors: Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Andrei Georgescu, An Tran, Hannes Kruppa, See-Kiong Ng, Roger Zimmermann
• Abstract summary: An enormous amount of crowdsourced street-view images are uploaded to the internet. 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. 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.
• Score: 29.015865103837413
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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