Regularity-driven building facade matching between aerial and street views

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

We present an approach for detecting and matching building facades between aerial view and street-view images. We exploit the regularity of urban scene facades as captured by their lattice structures and deduced from median-tiles' shape context, color, texture and spatial similarities. Our experimental results demonstrate effective matching of oblique and partially-occluded facades between aerial and ground views. Quantitative comparisons for automated urban scene facade matching from three cities show superior performance of our method over baseline SIFT, Root-SIFT and the more sophisticated Scale-Selective Self-Similarity and Binary Coherent Edge descriptors. We also illustrate regularity-based applications of occlusion removal from street views and higher-resolution texture-replacement in aerial views.

Original languageEnglish (US)
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Pages1591-1600
Number of pages10
ISBN (Electronic)9781467388504
DOIs
StatePublished - Dec 9 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: Jun 26 2016Jul 1 2016

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
CountryUnited States
CityLas Vegas
Period6/26/167/1/16

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Wolff, M., Collins, R. T., & Liu, Y. (2016). Regularity-driven building facade matching between aerial and street views. In Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 (pp. 1591-1600). [7780545] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December). IEEE Computer Society. https://doi.org/10.1109/CVPR.2016.176