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In this paper we formulate structure from motion as a learning problem.
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Bundle Adjustment — A Modern Synthesis Vision Algorithms: Theory and Practice
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Multiple View Geometry in Computer Vision
R. I. Hartley and A. Zisserman · 2004
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Distinctive Image Features from Scale-Invariant Keypoints
D. G. Lowe · 2004
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An efficient solution to the five-point relative pose problem
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Accurate and efficient stereo processing by semi-global matching and mutual information
H. Hirschmüller · 2005
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A. Saxena, S. H. Chung, and A. Y. Ng · 2005
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Make3d: Learning 3d scene structure from a single still image
D. A. Forsyth · 2009
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Building Rome on a Cloudless Day
J.-M. Frahm, P. Fite-Georgel, D. Gallup, T. Johnson, R. Raguram, C. Wu, Y.-H. Jen, E. Dunn, B. Clipp, S. Lazebnik, and M. Pollefeys · 2010
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DTAM: Dense tracking and mapping in real-time
R. A. Newcombe, S. Lovegrove, and A. Davison · 2011
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Indoor Segmentation and Support Inference from RGBD Images
P. K. Nathan Silberman, Derek Hoiem and R. Fergus · 2012
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A benchmark for the evaluation of rgb-d slam systems
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers · 2012
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Dense versus Sparse Approaches for Estimating the Fundamental Matrix
L. Valgaerts, A. Bruhn, M. Mainberger, and J. Weickert · 2012
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Towards Linear-Time Incremental Structure from Motion
C. Wu · 2013
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Discriminative unsupervised feature learning with exemplar convolutional neural networks
A. Dosovitskiy, P. Fischer, J. T. Springenberg, M. Riedmiller, and T. Brox · 2015
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Predicting Depth, Surface Normals and Semantic Labels With a Common Multi-Scale Convolutional Architecture
D. Eigen and R. Fergus · 2015
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Learning image representations tied to egomotion
D. Jayaraman and K. Grauman · 2015
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Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
F. Liu, C. Shen, G. Lin, and I. Reid · 2015
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Rethinking the Inception Architecture for Computer Vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
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SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels
J. Xiao, A. Owens, and A. Torralba · 2013
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Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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LSD-SLAM: Large-scale direct monocular SLAM
J. Engel, T. Schöps, and D. Cremers · 2014
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Mve-a multiview reconstruction environment
S. Fuhrmann, F. Langguth, and M. Goesele · 2014
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Caffe: Convolutional Architecture for Fast Feature Embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Adam: A Method for Stochastic Optimization
D. Kingma and J. Ba · 2014
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Global, dense multiscale reconstruction for a billion points
B. Ummenhofer and T. Brox · 2015
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Learning to compare image patches via convolutional neural networks
S. Zagoruyko and N. Komodakis · 2015
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Computing the Stereo Matching Cost With a Convolutional Neural Network
J. Žbontar and Y. LeCun · 2015
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Deepstereo: Learning to predict new views from the world’s imagery
J. Flynn, I. Neulander, J. Philbin, and N. Snavely · 2016
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Modelling Uncertainty in Deep Learning for Camera Relocalization
A. Kendall and R. Cipolla · 2016
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Iterative Instance Segmentation
K. Li, B. Hariharan, and J. Malik · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Structure-from-motion revisited
J. L. Schönberger and J.-M. Frahm · 2016
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Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm · 2016
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