Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map

By Liu Liu, Hongdong Li, Yuchao Dai. 2017

176 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps (2018, methodology): “Pose estimation approaches [9,18,19,20,21,22,23] match query images against 3D models of an area, and employ 2D-3D feature correspondences to identify 6-DOF query poses.”
Back to the Feature: Learning Robust Camera Localization from Pixels to Pose (2021, background): “Such approaches detect, describe [7, 49], and match [32, 46, 48, 73, 81, 96] local features, maintain an explicit sparse 3D representation of the environment, and sometimes leverage image retrieval [33, 86] to scale to large scenes [32, 57, 70, 75, 82, 87].”
Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification (2018, background): “Other proposals match query images against 3D models of cities [10,19,24,27,30].”
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression (2019, methodology): “Descriptor-based methods handle city-scale scenes [37, 39, 68, 81] and run in real-time on mobile devices [6, 38, 41, 48].”
A review of multimodal image matching: Methods and applications (2021, methodology): “[512] proposed a method that can quickly and precisely pinpoint the location and perspective of image shots according to a prestored large-scale 3D point-cloud map.”
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions (2018, methodology): “3D structure-based localization methods [34, 35, 39, 54, 56, 67, 77] establish correspondences between 2D features in a query image and 3D points in a SfM point cloud via descriptor matching.”
Lending Orientation to Neural Networks for Cross-View Geo-Localization (2019, unknown): “, [5, 24, 19, 18, 20, 27]), using ground-to-aerial cross-view matching (i.”
From Coarse to Fine: Robust Hierarchical Localization at Large Scale (2018, methodology): “The former perform direct matching of local descriptors between 2D keypoints of a query image and 3D points in a 3D SfM model [48, 51, 41, 25, 49].”