City-Scale Location Recognition

By Grant Schindler, Matthew A. Brown, Richard Szeliski. 2007

730 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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In Computer Vision and Pattern Recognition

In Media Technology

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].”
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].”
NetVLAD: CNN Architecture for Weakly Supervised Place Recognition (2015, methodology): “shelf CNN descriptors on two challenging place recognition benchmarks. 1. Introduction Visual place recognition has received a signiﬁcant amount of attention in the past years both in computer vision [4,9,10,23,33,58,59,60,61,75,76] and robotics communities [15,16,40,42,70] motivated by, e.g., appli”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “all these approaches, the difficult task is to find sufficiently discriminating features to increase the matching ratio as explained in [198].”
Wide-Area Image Geolocalization with Aerial Reference Imagery (2015, background): “The first category of methods infer location by matching using local image features [1, 4, 6, 33, 35, 38, 43].”
Worldwide Pose Estimation Using 3D Point Clouds (2012, result): “Our focus on precise camera geometry is in contrast to most prior work on image localization that has taken an image retrieval approach [6, 7], where an image is localized by finding images that match it closely without recovering explicit camera pose.”
Improving Image-Based Localization by Active Correspondence Search (2012, methodology): “They estimate the position of the query image using the GPS tags of retrieved images [12, 13].”
24/7 Place Recognition by View Synthesis (2018, positive): “RECENT years have seen a tremendous progress [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16] in the large-scale visual place recognition problem [14], [17].”
Visual Place Recognition: A Survey (2016, methodology): “Pure image retrieval assumes that matching is based solely on appearance similarity and applies image retrieval techniques from computer vision that are not specific to place-based information [3].”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “the camera location and orientation for a given query image. There are several approaches for solving this task by matching the query against street level imagery such as Google Street View panoramas [22,31,32,33,34,35] using local feature based image retrieval [14,8,15]. Other approaches are based ”
Retrieving landmark and non-landmark images from community photo collections (2010, background): “[28] are among the first to use inverted file indexing by means of a vocabulary tree [24] for city-scale location recognition, scaling up to 30, 000 images covering 20km of streetside views.”
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.”
Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization (2017, methodology): “In contrast to our method, these approaches only approximate the pose of the query camera [14], [17], [18], [20].”
I know what you did last summer: object-level auto-annotation of holiday snaps (2009, methodology): “In this paper, we combine a data collection process similar to [9] [18] [20] [22] with object retrieval methods [7] [6] [10] [15] [16] [17] [21] [8] to obtain a large-scale, retrieval-based auto annotation system.”
Image Retrieval for Image-Based Localization Revisited (2012, background): “select only informative features [29] to construct a vocabulary tree [22] while Knopp et al.”
Visual Place Recognition with Repetitive Structures (2015, background): “Scalable place recognition methods [3, 7, 18, 31, 37] often build on the efficient bag-of-visual-words representation developed for object and image retrieval [6, 13, 15, 24, 26, 40].”
Learning Less is More - 6D Camera Localization via 3D Surface Regression (2017, neutral): “0% 20% 40% 60% 80% ORB+PNP [22] DSAC (RGB Training) Our (w/o 3D Model) DSAC (RGB-D Training) Our (w/ 3D Model)”
Appearance-only SLAM at large scale with FAB-MAP 2.0 (2011, neutral): “For example, Schindler et al. (2007) describe a city-scale location recognition system based on the vocabulary tree approach of Nistér and Stewenius (2006).”