Open AccessAUTOMATIC ADJUSTMENT OF WIDE-BASE GOOGLE STREET VIEW PANORAMAS
Author(s) -
E. Boussias-Alexakis,
V. Tsironisa,
E. Petsa,
G. Karras
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b1-639-2016
Subject(s) - panorama , computer vision , computer science , artificial intelligence , vanishing point , point (geometry) , projection (relational algebra) , base (topology) , image (mathematics) , bundle adjustment , matching (statistics) , computer graphics (images) , planar , algorithm , mathematics , geometry , mathematical analysis , statistics
This paper focuses on the issue of sparse matching in cases of extremely wide-base panoramic images such as those acquired by Google Street View in narrow urban streets. In order to effectively use affine point operators for bundle adjustment, panoramas must be suitably rectified to simulate affinity. To this end, a custom piecewise planar projection (triangular prism projection) is applied. On the assumption that the image baselines run parallel to the street façades, the estimated locations of the vanishing lines of the façade plane allow effectively removing projectivity and applying the ASIFT point operator on panorama pairs. Results from comparisons with multi-panorama adjustment, based on manually measured image points, and ground truth indicate that such an approach, if further elaborated, may well provide a realistic answer to the matching problem in the case of demanding panorama configurations.