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We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video.
A computer algorithm for reconstructing a scene from two projections
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Distinctive image features from scale-invariant keypoints
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Epnp: An accurate o (n) solution to the pnp problem
Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua · 2009
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Scene reconstruction and visualization from internet photo collections
Keith N Snavely · 2009
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Accurate, dense, and robust multiview stereopsis
Yasutaka Furukawa and Jean Ponce · 2010
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Improving the efficiency of hierarchical structure-and-motion
Riccardo Gherardi, Michela Farenzena, and Andrea Fusiello · 2010
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Dtam: Dense tracking and mapping in real-time
Richard A Newcombe, Steven J Lovegrove, and Andrew J Davison · 2011
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Scene reconstruction and visualization from internet photo collections: A survey
Noah Snavely · 2011
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Visualsfm: A visual structure from motion system
Changchang Wu et al · 2011
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Ceres solver
Sameer Agarwal, Keir Mierle, et al · 2012
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A robust o (n) solution to the perspective-n-point problem
Shiqi Li, Chi Xu, and Ming Xie · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Dense visual slam for rgb-d cameras
Christian Kerl, Jurgen Sturm, and Daniel Cremers · 2013
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Sun3d: A database of big spaces reconstructed using sfm and object labels
Jianxiong Xiao, Andrew Owens, and Antonio Torralba · 2013
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Lsd-slam: Large-scale direct monocular slam
Jakob Engel, Thomas Schöps, and Daniel Cremers · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Multi-view stereo: A tutorial
Yasutaka Furukawa, Carlos Hernández, et al · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
Xufeng Han, Thomas Leung, Yangqing Jia, Rahul Sukthankar, and Alexander C Berg · 2015
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al · 2015
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Posenet: A convolutional network for real-time 6-dof camera relocalization
Alex Kendall, Matthew Grimes, and Roberto Cipolla · 2015
Demon: Depth and motion network for learning monocular stereo
Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy, and Thomas Brox · 2017
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Sfm-net: Learning of structure and motion from video
Sudheendra Vijayanarasimhan, Susanna Ricco, Cordelia Schmid, Rahul Sukthankar, and Katerina Fragkiadaki · 2017
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Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks
Sen Wang, Ronald Clark, Hongkai Wen, and Niki Trigoni · 2017
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
Later among the works it cites.
High quality monocular depth estimation via transfer learning
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Cited alongside, same era.
Orb-slam: a versatile and accurate monocular slam system
Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
Cited alongside, same era.
High-quality depth from uncalibrated small motion clip
Hyowon Ha, Sunghoon Im, Jaesik Park, Hae-Gon Jeon, and In So Kweon · 2016
Cited alongside, same era.
Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
Cited alongside, same era.
Ibraheem Alhashim and Peter Wonka · 2018
Closest in time.
Codeslam—learning a compact, optimisable representation for dense visual slam
Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J Davison · 2018
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Pyramid stereo matching network
Jia-Ren Chang and Yong-Sheng Chen · 2018
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Learning to solve nonlinear least squares for monocular stereo
Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, and Andrew J Davison · 2018
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Direct sparse odometry
Jakob Engel, Vladlen Koltun, and Daniel Cremers · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
Reza Mahjourian, Martin Wicke, and Anelia Angelova · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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Ba-net: Dense bundle adjustment network
Chengzhou Tang and Ping Tan · 2018
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Learning depth from monocular videos using direct methods
Chaoyang Wang, José Miguel Buenaposada, Rui Zhu, and Simon Lucey · 2018
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Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry
Nan Yang, Rui Wang, Jorg Stuckler, and Daniel Cremers · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan · 2018
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Zhichao Yin and Jianping Shi · 2018
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Deeptam: Deep tracking and mapping
Huizhong Zhou, Benjamin Ummenhofer, and Thomas Brox · 2018
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