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The horizon line is an important contextual attribute for a wide variety of image understanding tasks.
Robust estimation of a location parameter
Peter J Huber · 1964
Earlier work this paper cites.
Manhattan world: Compass direction from a single image by bayesian inference
James M Coughlan and Alan L Yuille · 1999
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Shape from texture: homogeneity revisited
Antonio Criminisi and Andrew Zisserman · 2000
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Single view metrology
Antonio Criminisi, Ian Reid, and Andrew Zisserman · 2000
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Camera calibration with two arbitrary coplanar circles
Qian Chen, Haiyuan Wu, and Toshikazu Wada · 2004
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Horizon detection using machine learning techniques
Sergiy Fefilatyev, Volha Smarodzinava, Lawrence O Hall, and Dmitry B Goldgof · 2006
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Efficient edge-based methods for estimating manhattan frames in urban imagery
Patrick Denis, James Elder, and Francisco Estrada · 2008
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Putting objects in perspective
Derek Hoiem, Alexei A Efros, and Martial Hebert · 2008
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Non-iterative approach for fast and accurate vanishing point detection
J-P Tardif · 2009
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Geometric image parsing in man-made environments
Olga Barinova, Victor Lempitsky, Elena Tretiak, and Pushmeet Kohli · 2010
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Automatic upright adjustment of photographs
Hyunjoon Lee, Eli Shechtman, Jue Wang, and Seungyong Lee · 2012
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Worldwide pose estimation using 3d point clouds
Yunpeng Li, Noah Snavely, Dan Huttenlocher, and Pascal Fua · 2012
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A machine learning approach to horizon line detection using local features
Touqeer Ahmad, George Bebis, Emma E Regentova, and Ara Nefian · 2013
Cited alongside, same era.
A minimum error vanishing point detection approach for uncalibrated monocular images of man-made environments
Yiliang Xu, Sangmin Oh, and Anthony Hoogs · 2013
Cited alongside, same era.
Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
Cited alongside, same era.
Caffe: Convolutional architecture for fast feature embedding
Robust optimization for deep regression
Vasileios Belagiannis, Christian Rupprecht, Gustavo Carneiro, and Nassir Navab · 2015
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Fast r-cnn
Ross Girshick · 2015
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Reconstructing the world* in six days *(as captured by the yahoo 100 million image dataset)
Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm · 2015
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Convolutional networks for real-time 6-dof camera relocalization
Alex Kendall, Matthew Grimes, and Roberto Cipolla · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
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Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Finding vanishing points via point alignments in image primal and dual domains
José Lezama, Rafael Grompone von Gioi, Gregory Randall, and Jean-Michel Morel · 2014
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Robust global translations with 1dsfm
Kyle Wilson and Noah Snavely · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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http://hlw.csr.uky.edu/
Horizon Lines in The Wild
Cited in the paper.
Later among the works it cites.
DeepFocal: A method for direct focal length estimation
Scott Workman, Connor Greenwell, Menghua Zhai, Ryan Baltenberger, and Nathan Jacobs · 2015
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Detecting vanishing points using global image context in a non-manhattan world
Menghua Zhai, Scott Workman, and Nathan Jacobs · 2016
Closest in time.
Unconstrained salient object detection via proposal subset optimization
Jianming Zhang, Stan Sclaroff, Zhe Lin, Xiaohui Shen, Brian Price, and Radomır Mech · 2016
Closest in time.