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In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
Pyramid methods in image processing
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Scale-space theory: A basic tool for analyzing structures at different scales
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Nathan Jacobs, Nathaniel Roman, and Robert Pless · 2007
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James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, and Deva Ramanan · 2010
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ORB: An efficient alternative to SIFT or SURF
Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski · 2011
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ASIFT: An algorithm for fully affine invariant comparison
Guoshen Yu and Jean-Michel Morel · 2011
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Relja Arandjelovic and Andrew Zisserman · 2012
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Scalable Nearest Neighbor Algorithms for High Dimensional Data
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Generic 3D Representation via Pose Estimation and Matching
Amir R. Zamir, Tilman Wekel, Pulkit Agrawal, Colin Wei, Jitendra Malik, and Silvio Savarese · 2016
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Evaluating Local Features for Day-Night Matching
Hao Zhou, Torsten Sattler, and David W. Jacobs · 2016
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HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
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End-to-End Learning of Deep Visual Representations for Image Retrieval
Albert Gordo, Jon Almazán, Jerome Revaud, and Diane Larlus · 2017
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Working hard to know your neighbor’s margins: Local descriptor learning loss
Anastasiya Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas · 2017
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Largescale image retrieval with attentive deep local features
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Domain-size pooling in local descriptors: DSP-SIFT
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Reconstructing the World* in Six Days *(As Captured by the Yahoo 100 Million Image Dataset)
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Discriminative Learning of Deep Convolutional Feature Point Descriptors
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Very deep convolutional networks for large-scale image recognition
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Ignacio Rocco, Relja Arandjelovic, and Josef Sivic · 2017
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Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?
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Matching neural paths: transfer from recognition to correspondence search
Nikolay Savinov, Lubor Ladicky, and Marc Pollefeys · 2017
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Quad-networks: unsupervised learning to rank for interest point detection
Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys · 2017
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Comparative evaluation of hand-crafted and learned local features
Johannes L. Schönberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys · 2017
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City-Scale Localization for Cameras with Known Vertical Direction
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SuperPoint: Self-Supervised Interest Point Detection and Description
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Hierarchical Metric Learning and Matching for 2D and 3D Geometric Correspondences
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Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability
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Fine-tuning CNN Image Retrieval with No Human Annotation
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Benchmarking 6DoF outdoor visual localization in changing conditions
Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, Fredrik Kahl, and Tomas Pajdla · 2018
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Semantic Visual Localization
Johannes L. Schönberger, Marc Pollefeys, Andreas Geiger, and Torsten Sattler · 2018
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InLoc: Indoor visual localization with dense matching and view synthesis
Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, and Akihiko Torii · 2018
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Learning to Detect Features in Texture Images
Linguang Zhang and Szymon Rusinkiewicz · 2018
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LF-Net: Learning local features from images
Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi · 2019
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