Improving Image-Based Localization by Active Correspondence Search

By Torsten Sattler, Bastian Leibe, Leif Kobbelt. 2012

304 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

PlaNet - Photo Geolocation with Convolutional Neural Networks (2016, background): “…subsets of the problem, like landmark build-1 ings [1, 37, 54], cities where street view imagery is available [7, 26, 52], or places where coverage of internet photos is dense enough to allow building a structure-from-motion reconstruction that a query photo can be matched against [31, 41].”
24/7 Place Recognition by View Synthesis (2018, unknown): “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].”
Geometric Loss Functions for Camera Pose Regression with Deep Learning (2017, methodology): “Traditionally, this has been approached by computing the pose from 2D-3D correspondences between 2D features in the query image and 3D points in the model, which are determined through descriptor matching [7, 28, 27, 42, 49].”
Image Matching Across Wide Baselines: From Paper to Practice (2020, background): “Matching two or more views of a given scene is at the core of fundamental computer vision problems, including image retrieval [48, 7, 69, 91, 63], 3D reconstruction [3, 43, 79, 106], re-localization [74, 75, 51], and SLAM [61, 30, 31].”
Learning and Calibrating Per-Location Classifiers for Visual Place Recognition (2013, methodology): “2013), which enables accurate prediction of query image camera position (Li et al. 2012; Sattler et al. 2012).”
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization (2015, methodology): “Yet, most large-scale localization approaches rely on directly matching the feature descriptors against the landmark descriptors without any intermediate retrieval step [25, 26, 41, 42, 49].”
Graph-Based Discriminative Learning for Location Recognition (2013, background): “However, there is no single definition for what it means to be a location, and, accordingly, a wide variety of representations for places have been used in research: Are places, for instance, a set of distinct landmarks, each represented by a set of images? [33, 16] Are places latitude and longitude”
Visual landmark recognition from Internet photo collections: A large-scale evaluation (2014, methodology): “ using local feature based image retrieval [14,8,15]. Other approaches are based on 3D point clouds created by applying structure-frommotion on Internet photo collections or manually collected photos [36,37,21,23]. Since image retrieval methods cannot be applied here, these approaches directly match”
Image Geo-Localization Based on MultipleNearest Neighbor Feature Matching UsingGeneralized Graphs (2014, background): “Index Terms—Geo-location, image localization, Generalized Minimum Clique Problem (GMCP), generalized minimum spanning tree (GMST), feature matching, multiple nearest neighbor feature matching, feature correspondence, generalized graphs Ç”
From Coarse to Fine: Robust Hierarchical Localization at Large Scale (2018, methodology): “For Active Search [33], City Scale Localization [40], DenseVLAD [45], and NetVLAD [2], we use the evaluation reported in the paper introducing the benchmark.”
Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map (2017, methodology): “We evaluate its performance against four standard publicly available benchmark datasets for city-scale localization ( [12,43]): (1) Dubrovnik, (2) Rome, (3) Vienna and (4) San Francisco (SF-0), where the first 3 have about millions of 3D map points, but the last one is much bigger in size (e.”
Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification (2013, methodology): “Recently proposed alternatives include direct image matching to sparse SfM point clouds [27, 17, 18].”
Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization (2017, methodology): “This paper is based on our previous publications [7], [8].”