Abstract
We seek to localize a query panorama with a wide field of view given a large database of street-level geotagged imagery. This is a challenging task because of significant changes in appearance due to viewpoint, season, occluding people or newly constructed buildings. An additional key challenge is the computational and memory efficiency due to the planet-scale size of the available geotagged image databases. The contributions of this paper are two-fold. First, we develop a compact image representation for scalable retrieval of panoramic images that represents each panorama as an ordered set of vertical image tiles. Two panoramas are matched by efficiently searching for their optimal horizontal alignment, while respecting the tile ordering constraint. Second, we collect a new challenging query test dataset from Shibuya, Tokyo containing more than thousand panoramic and perspective query images with manually verified ground truth geolocation. We demonstrate significant improvements of the proposed method compared to the standard bag-of-visual-words and VLAD baselines.
