Accurate Image Localization Based on Google Maps Street View

By Amir Zamir, Mubarak Shah. 2010

405 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

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, background): “While global features have the beneﬁt to retrieve diverse natural scene images based on ambient information, local image features yield higher precision in retrieving structured objects such as buildings and are thus more frequently used [8,9,10,11,12,13,14,15,16].”
Revisiting IM2GPS in the Deep Learning Era (2017, background): “Many previous works on image localization are at limited spatial scale (urban areas) or on special class of images (landmarks, streetview) [16, 39, 18, 34, 1, 17, 38].”
PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “Most local feature based approaches therefore focus on localization within cities, either based on photos from photo sharing websites [5, 36] or street view [2, 7, 26, 27, 43, 51, 52].”
Visual Place Recognition with Repetitive Structures (2015, background): “Examples include: (i) learning better visual vocabularies [21, 28]; (ii) developing quantization methods less prone to quantization errors [14, 27, 44]; (iii) combining returns from multiple query images depicting the same scene [4, 6]; (iv) exploiting the 3D or graph structure of the database [11, ”
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis (2019, methodology): “localization evaluation, a set of perspective images is generated by following the best practices from outdoor visual localization [22], [81], [97].”
Learned Contextual Feature Reweighting for Image Geo-Localization (2017, methodology): “Building image representations from local invariant features have been shown to be effective as they provide robustness to photometric and geometric changes [58, 4, 67, 31, 66].”
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision (2022, background): “Unlike previous works [108, 126, 127, 138], we neither make prior assumptions on the task nor simplify the problem by restricting the task to images from famous landmarks and cities, natural areas like deserts or mountains.”
Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs (2014, methodology): “First, we extract local features (e.g., SIFT) from the query image and retrieve a number of nearest neighbors for each query feature from the reference data set.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “In [195], SIFT descriptors are extracted from Street View images and indexed in tree structures which are then browsed with a nearest-neighbor algorithm from the descriptors of the current image.”
Localizing and Orienting Street Views Using Overhead Imagery (2016, methodology): “In [37], image localization is done eﬃciently by building a dataset of Google street-view images from which SIFT features are extracted, indexed and used for localization of a query image by voting.”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “For this reason, some approaches additionally make use of satellite aerial imagery to enhance the geolocalization in sparsely covered regions [35,40,44,45].”
Cross-View Image Matching for Geo-Localization in Urban Environments (2017, background): “In particular, this problem has attracted considerable attention due to the availability of ground-level geo-tagged imagery [8, 27, 21, 28, 19, 18].”
Wide-Area Image Geolocalization with Aerial Reference Imagery (2015, background): “The second category of methods match using global image features [11, 15, 45].”