Ultra-wide Baseline Facade Matching for Geo-localization

By Mayank Bansal, Kostas Daniilidis, Harpreet S. Sawhney. 2012

63 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Revisiting IM2GPS in the Deep Learning Era (2017, background): “Many approaches make used of aerial imagery for localization [3, 27, 17].”
Localizing and Orienting Street Views Using Overhead Imagery (2016, background): “Bansal et al. [3] match street-level images to aerial images by proposing a feature which encodes facade structure self-similarity.”
CVM-Net: Cross-View Matching Network for Image-Based Ground-to-Aerial Geo-Localization (2018, background): “As a result, matching ground view photos to aerial imagery gradually becomes an increasingly popular geo-localization approach [5, 22, 37, 23, 49, 50, 46, 41, 53, 43].”
Learning deep representations for ground-to-aerial geolocalization (2015, background): “[4] investigate ultra wide baseline matching between street-view images and 45° aerial view images.”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “In this context, solutions are presented that match an aerial query image against a reference dataset containing satellite images in a wide baseline approach [2,6,43].”
Cross-View Image Matching for Geo-Localization in Urban Environments (2017, methodology): “On the other hand, a more complete coverage for overhead reference data such as satellite/areial imagery and digital elevation model (DEM) has spurred a growing interest in cross-view geo-localization [1, 10, 2, 25, 11, 13, 22].”
Accurate Geo-Registration by Ground-to-Aerial Image Matching (2014, background): “Beyond the invariance to scale changes, there exists a rich body of work on affine or perspective invariance for improved robustness to large viewpoint changes [25][19][29][30][4].”
Learning to Match Aerial Images with Deep Attentive Architectures (2016, background): “For urban scenery, Bansal et al. [4] presented the Scale-Selective Self-Similarity (S4) descriptor which they used to identify and match building facades for image geolocalization purposes.”