  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.
